Persusive research paper on stem cell research and why it needs to

Persusive on stem cell and why it needs to continue and be funded by congress - Research Paper Example The present enthusiasm over prospective stem cell-produced remedies radiates from the new innovations of genetic biology. Though one cannot forecast the results from basic research, there is enough information available to suggest that a good deal of this enthusiasm is justified. This enthusiasm is not shared by those of the religious right. This faction is opposed to embryonic stem cell research which they claim as immoral and characterize as devaluing human life, much the same as does abortion, drawing a link between the two. This discussion will provide a brief overview of stem cell research and its benefits to society, the debate surrounding the issue and the arguments for continued research. Embryonic stem cells possess the ability to restore defective or damaged tissues which would heal or regenerate organs which have been adversely affected by a degenerative disease. Cell therapy has the very real potential to provide new cures for diabetes, cancer, kidney disease, macular deg eneration, multiple sclerosis and many other kinds of diseases. Cell therapy has also demonstrated a great potential to help repair and regenerate spinal cord injuries which would help paralyzed patients recapture lost body functions. The possibilities are limitless including greatly advancing the human lifespan because aging organs could be replenished. â€Å"We may even have the ability one day to grow our own organs for transplantation from our own stem cells, eliminating the danger of organ rejection† (â€Å"Future of Cell Therapy†, 2006). The three main objectives given for pursuing stem cell research are obtaining vital scientific information about embryonic development; curing incapacitating ailments and for testing new drugs instead of having to use animals. The scientific techniques for obtaining stem cells could lead to unparalleled advances and even cures for these and other ailments. It has been substantiated from animal research that stem cells can be diff erentiated into cells that will behave appropriately in their transplanted location. For example, the transplantation of stem cells following treatments for cancer has found much success for many years. There are numerous potential sources. The first is bone marrow stem cells. This type of stem cell is probably the most recognized of the stem cells. It has been used routinely to treat a variety of blood and bone marrow diseases, blood cancers and immune disorders. Leukemia is the most recognized disease that has been treated with a bone marrow transplant. New evidence suggests that bone marrow stem cells may be able to differentiate (the process by which an unspecialized cell acquires the features of a specialized cell) into cells that make up tissues outside of the blood such as liver and muscle (â€Å"Stem Cells In Use.† Learn.Genetics). The second type of stem cell is the adult stem cell. An adult stem cell is thought to be an undifferentiated cell, found among differentia ted cells in tissues or organs. These cells can renew themselves and can differentiate to become some or all of the major specialized cells types in the tissue and muscle it resides in. The primary function of this type of stem cell is to maintain and repair the tissue in which they reside. Because there are a very limited number of adult stem cells in each tissue coupled

United States History Essay Example for Free

United States History Essay The political, economic and social background of English colonialism during the period of 1603-1763 in North America envisions the great thought of European period of exploration because of its ever-forgotten influence in the New World. In early sixteenth century, many colonies were established in North America and among them the Southern and Central areas of English settlement were discovered to benefit more profit from their landlords of English kingdom. As the colonies maintained the international plan of trade extraction, they have close allegiance with indigenous population. The importance of changing economic and political relationships between the Indians and Englishmen seemed to be an essential issue in the history of North America. It created a sensation to develop the growth of awareness in both Whites and Indians because of their business contacts. To protect themselves and to maintain the business of commercial extractions and to maintain the freedom of religious beliefs, the colonies were established a democratic government during their ruling time period in England. Because of close contact with indigenous population of North America, colonists were faced with varied set of societies who were fundamentally different from the societies in Europe. Most of the colonists treated the native people as ferocious and envisioned them as an icon to structure the society. In a work, The Rediscovery of North America (1990), Lopez says, †¦ the physical destruction of a local landscape to increase the wealth of people who dont live there, or to supply materials to buyers in distant places who will never know the destruction that process leaves behind . The main feature that resulted by English colonization was massive immigration, which brought out the concept of multiculturalism. Broadly speaking, colonialism forms the economic and political strategies of domination with the principles self-government over the population. The other essential feature of English colonization in North America in the period of 1607-1763 was the European global expansionism, which was treated in late fifteenth century with an emphasis on English expansionism in North America. Basically, the European immigration to the America had been studied in histories, diaries and classics. The main purpose of European immigration to America may be to get freedom from religious discrimination and to develop economic strategy. The negative aspect, by the European settlers when entered the America during fifteenth century was lose of population by dreadful diseases like small pox, measles. Because of this reason, European settlement drastically reduced the North America population. As the colonists brought a wide range of deadly diseases from European cities and spread in North America, most of the people of North America were suffered, as they had no immunity to protect from dreadful diseases. Because of the European settlement, the North America faced many critical situations by colonization. Thus the struggle between European imperial powers and the social, economic, and political issues of late fifteenth and sixteenth centuries in North America were remained as the memorable milestone in American history. On the other side, the invasion of European global expansionism brought out the Western civilization in the New World, by the introduction of four major common languages. 1) English 2) Spanish 3) Portuguese 4) French. The colonies introduced many European concepts to the Americas such as European written form of communication, their form of government, and European technological knowledge of science, medicine and art to develop the world to a great extent. Hence the English colonization in North America was placed a dynamic position into the global political economy in the period 1603-1763 and became as a source of narrative to many authors to portray the ever last moment of American history. References: Lopez, Barry. The Rediscovery of North America. Lexington: University Press of Kentucky, 1990. Marx, Leo. The Machine in the Garden: Technology and the Pastoral ideal in America . New York: Oxford University Press, 1964. McCall, Barbara. The European Invasion. (Native American Culture. Jordan E. Kerber, series editor. ) Rourke Publications, Inc. , 1994. Roger L. Nichols. The American Indian: Past and Present, 4th Edition. McGraw-Hill, 1992. Wood, Marion. DOttavi, Francesca, illus. Myths and Civilization of the Native Americans. Peter Bedrick Books, 1998.

Development of Intelligent Sensor System

Development of Intelligent Sensor System Chapter 1 1.1 Introduction What is Automation? Automation in general, can be explained as the use of computers or microcontrollers to control industrial machinery and processes thereby fully replacing human operators. Automation is a kind of transition from mechanization. In mechanization, human operators are provided with machinery to assist their operations, where as automation fully replaces the human operators with computers. The advantages of automation are: Increased productivity and higher production rates. Better product quality and efficient use of resources. Greater control and consistency of products. Improved safety and reduced factory lead times. Home Automation Home automation is the field specializing in the general and specific automation requirements of homes and apartments for their better safety, security and comfort of its residents. It is also called Domotics. Home automation can be as simple as controlling a few lights in the house or as complicated as to monitor and to record the activities of each resident. Automation requirements depend on person to person. Some may be interested in the home security while others will be more into comfort requirements. Basically, home automation is anything that gives automatic control of things in your house. Some of the commonly used features in home automation are: Control of lighting. Climate control of rooms. Security and surveillance systems. Control of home entertainment systems. House plant watering system. Overhead tank water level controllers. Intelligent Sensors Complex large-scale systems consist of a large number of interconnected components. Mastering the dynamic behavior of such systems, calls for distributed control architectures. This can be achieved by implementing control and estimation algorithms in several controllers. Some algorithms manipulate only local variables (which are available in the local interface) but in most cases, algorithms implemented in some given computing device will use variables which are available in this devices local interface, and also variables which are input to the control system via remote interfaces, thus rising the need for communication networks, whose architecture and complexity depend on the amount of data to be exchanged, and on the associated time constraints. Associating computing (and communication) devices with sensing or actuating functions, has given rise to intelligent sensors. These sensors have gained a huge success in the past ten years, especially with the development of neural network s, fuzzy logic, and soft computing algorithms. The modern definition of smart or intelligent sensors can be formulated now as: ‘Smart sensor is an electronic device, including sensing element, interfacing, signal processing and having several intelligence functions as self-testing, self-identification, self-validation or self-adaptation. The keyword in this definition is ‘intelligence. The self-adaptation is a relatively new function of smart sensors and sensor systems. Self-adaptation smart sensors and systems are based on so-called adaptive algorithms and directly connected with precision measurements of frequency-time parameters of electrical signals. The later chapters will give an elaborate view on why we should use intelligent sensors, intelligent sensor structure, characteristics and network standards. Chapter 2 2.1 Conventional Sensors Before talking more on intelligent sensors, first we need to examine regular sensors in order to obtain a solid foundation on which we can develop our understanding on intelligent sensors. Most of the conventional sensors have shortcomings, both technically and economically. For a sensor to work effectively, it must be calibrated. That is, its output must be made to match some predetermined standard so that its reported values correctly reflect the parameter being measured. In the case of a bulb thermometer, the graduations next to the mercury column must be positioned so that they accurately correspond to the level of mercury for a given temperature. If the sensor is not calibrated, the information that it reports wont be accurate, which can be a big problem for the systems that use the reported information. The second concern one has when dealing with sensors is that their properties usually change over time, a phenomenon knows as drift. For instance, suppose we are measuring a DC current in a particular part of a circuit by monitoring the voltage across a resistor in that circuit. In this case, the sensor is the resistor and the physical property that we are measuring the voltage across it. As the resistor ages, its chemical properties will change, thus altering its resistance. As with the issue of calibration, some situations require much stricter drift tolerances than others; the point is that sensor properties will change with time unless we compensate for the drift in some fashion, and these changes are usually undesirable. The third problem is that not only do sensors themselves change with time, but so, too, does the environment in which they operate. An excellent example of that would be the electronic ignition for an internal combustion engine. Immediately after a tune-up, all the belts are tight, the spark plugs are new, the fuel injectors are clean, and the air filter is pristine. From that moment on, things go downhill; the belts loosen, deposits build up on the spark plugs and fuel injectors, and the air filter becomes clogged with ever-increasing amounts of dirt and dust. Unless the electronic ignition can measure how things are changing and make adjustments, the settings and timing sequence that it uses to fire the spark plugs will become progressively mismatched for the engine conditions, resulting in poorer performance and reduced fuel efficiency. The ability to compensate for often extreme changes in the operating environment makes a huge difference in a sensors value to a particular applic ation. Yet a fourth problem is that most sensors require some sort of specialized hardware called signal-conditioning circuitry in order to be of use in monitoring or control applications. The signal-conditioning circuitry is what transforms the physical sensor property that were monitoring (often an analog electrical voltage that varies in some systematic way with the parameter being measured) into a measurement that can be used by the rest of the system. Depending upon the application, the signal conditioning may be as simple as a basic amplifier that boosts the sensor signal to a usable level or it may entail complex circuitry that cleans up the sensor signal and compensates for environmental conditions, too. Frequently, the conditioning circuitry itself has to be tuned for the specific sensor being used, and for analog signals that often means physically adjusting a potentiometer or other such trimming device. In addition, the configuration of the signal-conditioning circuitry tends to be unique to both the specific type of sensor and to the application itself, which means that different types of sensors or different applications frequently need customized circuitry. Finally, standard sensors usually need to be physically close to the control and monitoring systems that receive their measurements. In general, the farther a sensor is from the system using its measurements, the less useful the measurements are. This is due primarily to the fact that sensor signals that are run long distances are susceptible to electronic noise, thus degrading the quality of the readings at the receiving end. In many cases, sensors are connected to the monitoring and control systems using specialized (and expensive) cabling; the longer this cabling is, the more costly the installation, which is never popular with end users. A related problem is that sharing sensor outputs among multiple systems becomes very difficult, particularly if those systems are physically separated. This inability to share outputs may not seem important, but it severely limits the ability to scale systems to large installations, resulting in much higher costs to install and support multiple r edundant sensors. What we really need to do is to develop some technique by which we can solve or at least greatly alleviate these problems of calibration, drift, and signal conditioning. 2.2 Making Sensors Intelligent Control systems are becoming increasingly complicated and generate increasingly complex control information. Control must nevertheless be exercised, even under such circumstances. Even considering just the detection of abnormal conditions or the problems of giving a suitable warning, devices are required that can substitute for or assist human sensation, by detecting and recognizing multi-dimensional information, and conversion of non visual information into visual form. In systems possessing a high degree of functionality, efficiency must be maximized by division of the information processing function into central processing and processing dispersed to local sites. With increased progress in automation, it has become widely recognized that the bottleneck in such systems lies with the sensors. Such demands are difficult to deal with by simply improvising the sensor devices themselves. Structural reinforcement, such as using array of sensors, or combinations of different types of sensors, and reinforcement from the data processing aspect by a signal processing unit such as a computer, are indispensible. In particular, the data processing and sensing aspects of the various stages involved in multi-dimensional measurement, image construction, characteristic extraction and pattern recognition, which were conventionally performed exclusively by human beings, have been tremendously enhanced by advances in micro-electronics. As a result, in many cases sensor systems have been implemented that substitute for some or all of the intellectual actions of human beings, i.e. intelligent sensor systems. Sensors which are made intelligent in this way are called ‘intelligent sensors or ‘smart sensors. According to Breckenridge and Husson, the smart sensor itself has a data processing function and automatic calibration/automatic compensation function, in which the sensor itself detects and eliminates abnormal values or exceptional values. It incorporates an algorithm, which is capable of being altered, and has a certain degree of memory function. Further desirable characteristics are that the sensor is coupled to other sensors, adapts to changes in environmental conditions, and has a discriminant function. Scientific measuring instruments that are employed for observation and measurement of physical world are indispensible extensions of our senses and perceptions in the scientific examination of nature. In recognizing nature, we mobilize all the resources of information obtained from the five senses of sight, hearing, touch, taste and smell etc. and combine these sensory data in such a way as to avoid contradiction. Thus more reliable, higher order data is obtained by combining data of different types. That is, there is a data processing mechanism that combines and processes a number of sensory data. The concept of combining sensors to implement such a data processing mechanism is called ‘sensor fusion 2.2.1 Digitizing the Sensor Signal The discipline of digital signal processing or DSP, in which signals are manipulated mathematically rather than with electronic circuitry, is well established and widely practiced. Standard transformations, such as filtering to remove unwanted noise or frequency mappings to identify particular signal components, are easily handled using DSP. Furthermore, using DSP principles we can perform operations that would be impossible using even the most advanced electronic circuitry. For that very reason, todays designers also include a stage in the signal-conditioning circuitry in which the analog electrical signal is converted into a digitized numeric value. This step, called analog-to-digital conversion, A/D conversion, or ADC, is vitally important, because as soon as we can transform the sensor signal into a numeric value, we can manipulate it using software running on a microprocessor. Analog-to-digital converters, or ADCs as theyre referred to, are usually single-chip semiconductor devices that can be made to be highly accurate and highly stable under varying environmental conditions. The required signal-conditioning circuitry can often be significantly reduced, since much of the environmental compensation circuitry can be made a part of the ADC and filtering can be performed in software. 2.2.2 Adding Intelligence Once the sensor signal has been digitized, there are two primary options in how we handle those numeric values and the algorithms that manipulate them. We can either choose to implement custom digital hardware that essentially â€Å"hard-wires† our processing algorithm, or we can use a microprocessor to provide the necessary computational power. In general, custom hardware can run faster than microprocessor-driven systems, but usually at the price of increased production costs and limited flexibility. Microprocessors, while not necessarily as fast as a custom hardware solution, offer the great advantage of design flexibility and tend to be lower-priced since they can be applied to a variety of situations rather than a single application. Once we have on-board intelligence, were able to solve several of the problems that we noted earlier. Calibration can be automated, component drift can be virtually eliminated through the use of purely mathematical processing algorithms, and we can compensate for environmental changes by monitoring conditions on a periodic basis and making the appropriate adjustments automatically. Adding a brain makes the designers life much easier. 2.2.3 Communication Interface The sharing of measurements with other components within the system or with other systems adds to the value of these measurements. To do this, we need to equip our intelligent sensor with a standardized means to communicate its information to other elements. By using standardized methods of communication, we ensure that the sensors information can be shared as broadly, as easily, and as reliably as possible, thus maximizing the usefulness of the sensor and the information it produces. Thus these three factors consider being mandatory for an intelligent sensor: A sensing element that measures one or more physical parameters (essentially the traditional sensor weve been discussing), A computational element that analyzes the measurements made by the sensing element, and A communication interface to the outside world that allows the device to exchange information with other components in a larger system. Its the last two elements that really distinguish intelligent sensors from their more common standard sensor relatives because they provide the abilities to turn data directly into information, to use that information locally, and to communicate it to other elements in the system. 2.3 Types of Intelligent Sensors Intelligent sensors are chosen depending on the object, application, precision system, environment of use and cost etc. In such cases consideration must be given as to what is an appropriate evaluation standard. This question involves a multi-dimensional criterion and is usually very difficult. The evaluation standard directly reflects the sense of value itself applied in the design and manufacture of the target system. This must therefore be firmly settled at the system design stage. In sensor selection, the first matter to be considered is determination of the subject of measurement. The second matter to be decided on is the required precision and dynamic range. The third is ease of use, cost, delivery time etc., and ease of maintenance in actual use and compatibility with other sensors in the system. The type of sensor should be matched to such requirements at the design stage. Sensors are usually classified by the subject of measurement and the principle of sensing action. 2.3.1 Classification Based on Type of Input In this, the sensor is classified in accordance with the physical phenomenon that is needed to be detected and the subject of measurement. Some of the examples include voltage, current, displacement and pressure. A list of sensors and their categories are mentioned in the following table. Category Type Dynamic Quantity Flow rate, Pressure, force, tension Speed, acceleration Sound, vibration Distortion, direction proximity Optical Quantities Light (infra red, visible light or radiation) Electromagnetic Quantities Current, voltage, frequency, phase, vibration, magnetism Quantity of Energy or Heat Temperature, humidity, dew point Chemical Quantities Analytic sensors, gas, odour, concentration, pH, ions Sensory Quantities or Biological Quantities Touch, vision, smell Table 2.3.1: Sensed items Classified in accordance with subject of measurement. 2.3.2 Classification Based on Type of Output In an intelligent sensor, it is often necessary to process in an integrated manner the information from several sensors or from a single sensor over a given time range. A computer of appropriate level is employed for such purposes in practically y all cases. For coupling to the computer when constructing an intelligent sensor system, a method with a large degree of freedom is therefore appropriate. It is also necessary to pay careful attention to the type of physical quantity carrying the output information to the sensor, and to the information description format of this physical quantity or dynamic quantity, and for the description format an analog, digital or encoded method etc., might be used. Although any physical quantities could be used as output signal, electrical quantities such as voltage are more convenient for data input to a computer. The format of the output signal can be analog or digital. For convenience in data input to the computer, it is preferable if the output signal of the sensor itself is in the form of a digital electrical signal. In such cases, a suitable means of signal conversion must be provided to input the data from the sensor to the computer 2.3.3 Classification Based on Accuracy When a sensor system is constructed, the accuracy of the sensors employed is a critical factor. Usually sensor accuracy is expressed as the minimum detectable quantity. This is determined by the sensitivity of the sensor and the internally generated noise of the sensor itself. Higher sensitivity and lower internal noise level imply greater accuracy. Generally for commercially available sensors the cost of the sensor is determined by the accuracy which it is required to have. If no commercial sensor can be found with the necessary accuracy, a custom product must be used, which will increase the costs. For ordinary applications an accuracy of about 0.1% is sufficient. Such sensors can easily be selected from commercially available models. Dynamic range (full scale deflection/minimum detectable quantity) has practically the same meaning as accuracy, and is expressed in decibel units. For example a dynamic range of 60dB indicates that the full scale deflection is 103 times the minimum detectable quantity. That is, a dynamic range of 60dB is equivalent to 0.1% accuracy. In conventional sensors, linearity of output was regarded as quite important. However, in intelligent sensor technology the final stage is normally data processing by computer, so output linearity is not a particular problem. Any sensor providing a reproducible relationship of input and output signal can be used in an intelligent sensor system. Chapter 3 3.1 Sensor selection The function of a sensor is to receive some action from a single phenomenon of the subject of measurement and to convert this to another physical phenomenon that can be more easily handled. The phenomenon constituting the subject of measurement is called the input signal, and the phenomenon after conversion is called the output signal. The ratio of the output signal to the input signal is called the transmittance or gain. Since the first function of a sensor is to convert changes in the subject of measurement to a physical phenomenon that can be more easily handled, i.e. its function consists in primary conversion, its conversion efficiency, or the degree of difficulty in delivering the output signal to the transducer constituting the next stage is of secondary importance The first point to which attention must be paid in sensor selection is to preserve as far as possible the information of the input signal. This is equivalent to preventing lowering of the signal-to-noise ratio (SNR). For example, if the SNR of the input signal is 60 dB, a sensor of dynamic range less than 60 dB should not be used. In order to detect changes in the quantity being measured as faithfully as possible, a sensor is required to have the following properties. Non-interference. This means that its output should not be changed by factors other than changes in the subject of measurement. Conversion satisfying this condition is called direct measurement. Conversion wherein the measurement quantity is found by calculation from output signals determined under the influence of several input signals is called indirect measurement. High sensitivity. The amount of change of the output signal that is produced by a change of unit amount of the input quantity being measured, i.e. the gain, should be as large as possible. Small measurement pressure. This means that the sensor should not disturb the physical conditions of the subject of measurement. From this point of view, modulation conversion offers more freedom than direct-acting conversion. High speed. The sensor should have sufficiently high speed of reaction to track the maximum anticipated rate of variation of the measured quantity. Low noise. The noise generated by the sensor itself should be as little as possible. Robustness. The output signal must be at least more robust than the quantity being measured, and be easier to handle. Robustness means resistance to environmental changes and/or noise. In general, phenomena of large energy are more resistant to external disturbance such as noise than are phenomena of smaller energy, they are easier to handle, and so have better robustness. If a sensor can be obtained that satisfies all these conditions, there is no problem. However, in practice, one can scarcely expect to obtain a sensor satisfying all these conditions. In such cases, it is necessary to combine the sensor with a suitable compensation mechanism, or to compensate the transducer of the secondary converter. Progress in IC manufacturing technology has made it possible to integrate various sensor functions. With the progressive shift from mainframes to minicomputers and hence to microcomputers, control systems have changed from centralized processing systems to distributed processing systems. Sensor technology has also benefited from such progress in IC manufacturing technology, with the result that systems whereby information from several sensors is combined and processed have changed from centralized systems to dispersed systems. Specifically, attempts are being made to use silicon-integrated sensors in a role combining primary data processing and input in systems that measure and process two-dimensional information such as picture information. This is a natural application of silicon precision working technology and digital circuit technology, which have been greatly advanced by introduction of VLSI manufacturing technology. Three-dimensional integrated circuits for recognizing letter patterns and odour sensors, etc., are examples of this. Such sensor systems can be called perfectly intelligent sensors in that they themselves have a certain data processing capability. It is characteristic of such sensors to combine several sensor inputs and to include a microprocessor that performs data processing. Their output signal is not a simple conversion of the input signal, but rather an abstract quantity obtained by some reorganization and combination of input signals from several sensors. This type of signal conversion is now often performed by a distributed processing mechanism, in which microprocessors are used to carry out the data processing that was previously performed by a centralized computer system having a large number of interfaces to individual sensors. However, the miniaturization obtained by application of integrated circuit techniques brings about an increase in the flexibility of coupling between elements. This has a substantial effect. Sensors of this type constitute a new technology that is at present being researched and developed. Although further progress can be expected, the overall picture cannot be predicted at the present time. Technically, practically free combinations of sensors can be implemented with the object of so-called indirect measurement, in which the signals from several individual sensors that were conventionally present are collected and used as the basis for a new output signal. In many aspects, new ideas are required concerning determination of the object of measurement, i.e. which measured quantities are to be selected, determination of the individual functions to achieve this, and the construction of the framework to organize these as a system. 3.2 Structure of an Intelligent Sensor The rapidity of development in microelectronics has had a profound effect on the whole of instrumentation science, and it has blurred some of the conceptual boundaries which once seemed so firm. In the present context the boundary between sensors and instruments is particularly uncertain. Processes which were once confined to a large electronic instrument are now available within the housing of a compact sensor, and it is some of these processes which we discuss later in this chapter. An instrument in our context is a system which is designed primarily to act as a free standing device for performing a particular set of measurements; the provision of communications facilities is of secondary importance. A sensor is a system which is designed primarily to serve a host system and without its communication channel it cannot serve its purpose. Nevertheless, the structures and processes used within either device, be they hardware or software, are similar. The range of disciplines which arc brought together in intelligent sensor system design is considerable, and the designer of such systems has to become something of a polymath. This was one of the problems in the early days of computer-aided measurement and there was some resistance from the backwoodsmen who practiced the art of measurement. 3.2.1 Elements of Intelligent Sensors The intelligent sensor is an example of a system, and in it we can identify a number of sub-systems whose functions are clearly distinguished from each other. The principal sub-systems within an intelligent sensor are: A primary sensing element Excitation Control Amplification (Possibly variable gain) Analogue filtering Data conversion Compensation Digital Information Processing Digital Communication Processing The figure illustrates the way in which these sub-systems relate to each other. Some of the realizations of intelligent sensors, particularly the earlier ones, may incorporate only some of these elements. The primary sensing element has an obvious fundamental importance. It is more than simply the familiar traditional sensor incorporated into a more up-to-date system. Not only are new materials and mechanisms becoming available for exploitation, but some of those that have been long known yet discarded because of various difficulties of behaviour may now be reconsidered in the light of the presence of intelligence to cope with these difficul ­ties. Excitation control can take a variety of forms depending on the circumstances. Some sensors, such as the thermocouple, convert energy directly from one form to another without the need for additional excitation. Others may require fairly elaborate forms of supply. It may be alternating or pulsed for subsequent coherent or phase-sensitive detection. In some circumstances it may be necessary to provide extremely stable supplies to the sensing element, while in others it may be necessary for those supplies to form part of a control loop to maintain the operating condition of the clement at some desired optimum. While this aspect may not be thought fundamental to intelligent sensors there is a largely unexplored range of possibilities for combining it with digital processing to produce novel instrumentation techniques. Amplification of the electrical output of the primary sensing element is almost invariably a requirement. This can pose design problems where high gain is needed. Noise is a particular hazard, and a circumstance unique to the intelligent form of sensor is the presence of digital buses carrying signals with sharp transitions. For this reason circuit layout is a particularly important part of the design process. Analogue filtering is required at minimum to obviate aliasing effects in the conversion stage, but it is also attractive where digital filtering would lake up too much of the real-time processing power available. Data conversion is the stage of transition between the continuous real world and the discrete internal world of the digital processor. It is important to bear in mind that the process of analogue to digital conversion is a non-linear one and represents a potentially gross distortion of the incoming information. It is important, however, for the intelligent sensor designer always to remember that this corruption is present, and in certain circumstances it can assume dominating importance. Such circumstances would include the case where the conversion process is part of a control loop or where some sort of auto-ranging, overt or covert, is built in to the operational program. Compensation is an inevitable part of the intelligent sensor. The operating point of the sensors may change due to various reasons. One of them is temperature. So an intelligent sensor must have an inbuilt compensation setup to bring the operating point back to its standard set stage. Information processing is, of course, unique to the intelligent form of sensor. There is some overlap between compensation and information processing, but there are also significant areas on independence. An important aspect is the condensation of information, which is necessary to preserve the two most precious resources of the industrial measurement system, the information bus and the central processor. A prime example of data condensa ­tion occurs in the Doppler velocimctcr in which a substantial quantity of informa ­tion is reduced to a single number representing the velocity. Sensor compensation will in general require the processi Development of Intelligent Sensor System Development of Intelligent Sensor System Chapter 1 1.1 Introduction What is Automation? Automation in general, can be explained as the use of computers or microcontrollers to control industrial machinery and processes thereby fully replacing human operators. Automation is a kind of transition from mechanization. In mechanization, human operators are provided with machinery to assist their operations, where as automation fully replaces the human operators with computers. The advantages of automation are: Increased productivity and higher production rates. Better product quality and efficient use of resources. Greater control and consistency of products. Improved safety and reduced factory lead times. Home Automation Home automation is the field specializing in the general and specific automation requirements of homes and apartments for their better safety, security and comfort of its residents. It is also called Domotics. Home automation can be as simple as controlling a few lights in the house or as complicated as to monitor and to record the activities of each resident. Automation requirements depend on person to person. Some may be interested in the home security while others will be more into comfort requirements. Basically, home automation is anything that gives automatic control of things in your house. Some of the commonly used features in home automation are: Control of lighting. Climate control of rooms. Security and surveillance systems. Control of home entertainment systems. House plant watering system. Overhead tank water level controllers. Intelligent Sensors Complex large-scale systems consist of a large number of interconnected components. Mastering the dynamic behavior of such systems, calls for distributed control architectures. This can be achieved by implementing control and estimation algorithms in several controllers. Some algorithms manipulate only local variables (which are available in the local interface) but in most cases, algorithms implemented in some given computing device will use variables which are available in this devices local interface, and also variables which are input to the control system via remote interfaces, thus rising the need for communication networks, whose architecture and complexity depend on the amount of data to be exchanged, and on the associated time constraints. Associating computing (and communication) devices with sensing or actuating functions, has given rise to intelligent sensors. These sensors have gained a huge success in the past ten years, especially with the development of neural network s, fuzzy logic, and soft computing algorithms. The modern definition of smart or intelligent sensors can be formulated now as: ‘Smart sensor is an electronic device, including sensing element, interfacing, signal processing and having several intelligence functions as self-testing, self-identification, self-validation or self-adaptation. The keyword in this definition is ‘intelligence. The self-adaptation is a relatively new function of smart sensors and sensor systems. Self-adaptation smart sensors and systems are based on so-called adaptive algorithms and directly connected with precision measurements of frequency-time parameters of electrical signals. The later chapters will give an elaborate view on why we should use intelligent sensors, intelligent sensor structure, characteristics and network standards. Chapter 2 2.1 Conventional Sensors Before talking more on intelligent sensors, first we need to examine regular sensors in order to obtain a solid foundation on which we can develop our understanding on intelligent sensors. Most of the conventional sensors have shortcomings, both technically and economically. For a sensor to work effectively, it must be calibrated. That is, its output must be made to match some predetermined standard so that its reported values correctly reflect the parameter being measured. In the case of a bulb thermometer, the graduations next to the mercury column must be positioned so that they accurately correspond to the level of mercury for a given temperature. If the sensor is not calibrated, the information that it reports wont be accurate, which can be a big problem for the systems that use the reported information. The second concern one has when dealing with sensors is that their properties usually change over time, a phenomenon knows as drift. For instance, suppose we are measuring a DC current in a particular part of a circuit by monitoring the voltage across a resistor in that circuit. In this case, the sensor is the resistor and the physical property that we are measuring the voltage across it. As the resistor ages, its chemical properties will change, thus altering its resistance. As with the issue of calibration, some situations require much stricter drift tolerances than others; the point is that sensor properties will change with time unless we compensate for the drift in some fashion, and these changes are usually undesirable. The third problem is that not only do sensors themselves change with time, but so, too, does the environment in which they operate. An excellent example of that would be the electronic ignition for an internal combustion engine. Immediately after a tune-up, all the belts are tight, the spark plugs are new, the fuel injectors are clean, and the air filter is pristine. From that moment on, things go downhill; the belts loosen, deposits build up on the spark plugs and fuel injectors, and the air filter becomes clogged with ever-increasing amounts of dirt and dust. Unless the electronic ignition can measure how things are changing and make adjustments, the settings and timing sequence that it uses to fire the spark plugs will become progressively mismatched for the engine conditions, resulting in poorer performance and reduced fuel efficiency. The ability to compensate for often extreme changes in the operating environment makes a huge difference in a sensors value to a particular applic ation. Yet a fourth problem is that most sensors require some sort of specialized hardware called signal-conditioning circuitry in order to be of use in monitoring or control applications. The signal-conditioning circuitry is what transforms the physical sensor property that were monitoring (often an analog electrical voltage that varies in some systematic way with the parameter being measured) into a measurement that can be used by the rest of the system. Depending upon the application, the signal conditioning may be as simple as a basic amplifier that boosts the sensor signal to a usable level or it may entail complex circuitry that cleans up the sensor signal and compensates for environmental conditions, too. Frequently, the conditioning circuitry itself has to be tuned for the specific sensor being used, and for analog signals that often means physically adjusting a potentiometer or other such trimming device. In addition, the configuration of the signal-conditioning circuitry tends to be unique to both the specific type of sensor and to the application itself, which means that different types of sensors or different applications frequently need customized circuitry. Finally, standard sensors usually need to be physically close to the control and monitoring systems that receive their measurements. In general, the farther a sensor is from the system using its measurements, the less useful the measurements are. This is due primarily to the fact that sensor signals that are run long distances are susceptible to electronic noise, thus degrading the quality of the readings at the receiving end. In many cases, sensors are connected to the monitoring and control systems using specialized (and expensive) cabling; the longer this cabling is, the more costly the installation, which is never popular with end users. A related problem is that sharing sensor outputs among multiple systems becomes very difficult, particularly if those systems are physically separated. This inability to share outputs may not seem important, but it severely limits the ability to scale systems to large installations, resulting in much higher costs to install and support multiple r edundant sensors. What we really need to do is to develop some technique by which we can solve or at least greatly alleviate these problems of calibration, drift, and signal conditioning. 2.2 Making Sensors Intelligent Control systems are becoming increasingly complicated and generate increasingly complex control information. Control must nevertheless be exercised, even under such circumstances. Even considering just the detection of abnormal conditions or the problems of giving a suitable warning, devices are required that can substitute for or assist human sensation, by detecting and recognizing multi-dimensional information, and conversion of non visual information into visual form. In systems possessing a high degree of functionality, efficiency must be maximized by division of the information processing function into central processing and processing dispersed to local sites. With increased progress in automation, it has become widely recognized that the bottleneck in such systems lies with the sensors. Such demands are difficult to deal with by simply improvising the sensor devices themselves. Structural reinforcement, such as using array of sensors, or combinations of different types of sensors, and reinforcement from the data processing aspect by a signal processing unit such as a computer, are indispensible. In particular, the data processing and sensing aspects of the various stages involved in multi-dimensional measurement, image construction, characteristic extraction and pattern recognition, which were conventionally performed exclusively by human beings, have been tremendously enhanced by advances in micro-electronics. As a result, in many cases sensor systems have been implemented that substitute for some or all of the intellectual actions of human beings, i.e. intelligent sensor systems. Sensors which are made intelligent in this way are called ‘intelligent sensors or ‘smart sensors. According to Breckenridge and Husson, the smart sensor itself has a data processing function and automatic calibration/automatic compensation function, in which the sensor itself detects and eliminates abnormal values or exceptional values. It incorporates an algorithm, which is capable of being altered, and has a certain degree of memory function. Further desirable characteristics are that the sensor is coupled to other sensors, adapts to changes in environmental conditions, and has a discriminant function. Scientific measuring instruments that are employed for observation and measurement of physical world are indispensible extensions of our senses and perceptions in the scientific examination of nature. In recognizing nature, we mobilize all the resources of information obtained from the five senses of sight, hearing, touch, taste and smell etc. and combine these sensory data in such a way as to avoid contradiction. Thus more reliable, higher order data is obtained by combining data of different types. That is, there is a data processing mechanism that combines and processes a number of sensory data. The concept of combining sensors to implement such a data processing mechanism is called ‘sensor fusion 2.2.1 Digitizing the Sensor Signal The discipline of digital signal processing or DSP, in which signals are manipulated mathematically rather than with electronic circuitry, is well established and widely practiced. Standard transformations, such as filtering to remove unwanted noise or frequency mappings to identify particular signal components, are easily handled using DSP. Furthermore, using DSP principles we can perform operations that would be impossible using even the most advanced electronic circuitry. For that very reason, todays designers also include a stage in the signal-conditioning circuitry in which the analog electrical signal is converted into a digitized numeric value. This step, called analog-to-digital conversion, A/D conversion, or ADC, is vitally important, because as soon as we can transform the sensor signal into a numeric value, we can manipulate it using software running on a microprocessor. Analog-to-digital converters, or ADCs as theyre referred to, are usually single-chip semiconductor devices that can be made to be highly accurate and highly stable under varying environmental conditions. The required signal-conditioning circuitry can often be significantly reduced, since much of the environmental compensation circuitry can be made a part of the ADC and filtering can be performed in software. 2.2.2 Adding Intelligence Once the sensor signal has been digitized, there are two primary options in how we handle those numeric values and the algorithms that manipulate them. We can either choose to implement custom digital hardware that essentially â€Å"hard-wires† our processing algorithm, or we can use a microprocessor to provide the necessary computational power. In general, custom hardware can run faster than microprocessor-driven systems, but usually at the price of increased production costs and limited flexibility. Microprocessors, while not necessarily as fast as a custom hardware solution, offer the great advantage of design flexibility and tend to be lower-priced since they can be applied to a variety of situations rather than a single application. Once we have on-board intelligence, were able to solve several of the problems that we noted earlier. Calibration can be automated, component drift can be virtually eliminated through the use of purely mathematical processing algorithms, and we can compensate for environmental changes by monitoring conditions on a periodic basis and making the appropriate adjustments automatically. Adding a brain makes the designers life much easier. 2.2.3 Communication Interface The sharing of measurements with other components within the system or with other systems adds to the value of these measurements. To do this, we need to equip our intelligent sensor with a standardized means to communicate its information to other elements. By using standardized methods of communication, we ensure that the sensors information can be shared as broadly, as easily, and as reliably as possible, thus maximizing the usefulness of the sensor and the information it produces. Thus these three factors consider being mandatory for an intelligent sensor: A sensing element that measures one or more physical parameters (essentially the traditional sensor weve been discussing), A computational element that analyzes the measurements made by the sensing element, and A communication interface to the outside world that allows the device to exchange information with other components in a larger system. Its the last two elements that really distinguish intelligent sensors from their more common standard sensor relatives because they provide the abilities to turn data directly into information, to use that information locally, and to communicate it to other elements in the system. 2.3 Types of Intelligent Sensors Intelligent sensors are chosen depending on the object, application, precision system, environment of use and cost etc. In such cases consideration must be given as to what is an appropriate evaluation standard. This question involves a multi-dimensional criterion and is usually very difficult. The evaluation standard directly reflects the sense of value itself applied in the design and manufacture of the target system. This must therefore be firmly settled at the system design stage. In sensor selection, the first matter to be considered is determination of the subject of measurement. The second matter to be decided on is the required precision and dynamic range. The third is ease of use, cost, delivery time etc., and ease of maintenance in actual use and compatibility with other sensors in the system. The type of sensor should be matched to such requirements at the design stage. Sensors are usually classified by the subject of measurement and the principle of sensing action. 2.3.1 Classification Based on Type of Input In this, the sensor is classified in accordance with the physical phenomenon that is needed to be detected and the subject of measurement. Some of the examples include voltage, current, displacement and pressure. A list of sensors and their categories are mentioned in the following table. Category Type Dynamic Quantity Flow rate, Pressure, force, tension Speed, acceleration Sound, vibration Distortion, direction proximity Optical Quantities Light (infra red, visible light or radiation) Electromagnetic Quantities Current, voltage, frequency, phase, vibration, magnetism Quantity of Energy or Heat Temperature, humidity, dew point Chemical Quantities Analytic sensors, gas, odour, concentration, pH, ions Sensory Quantities or Biological Quantities Touch, vision, smell Table 2.3.1: Sensed items Classified in accordance with subject of measurement. 2.3.2 Classification Based on Type of Output In an intelligent sensor, it is often necessary to process in an integrated manner the information from several sensors or from a single sensor over a given time range. A computer of appropriate level is employed for such purposes in practically y all cases. For coupling to the computer when constructing an intelligent sensor system, a method with a large degree of freedom is therefore appropriate. It is also necessary to pay careful attention to the type of physical quantity carrying the output information to the sensor, and to the information description format of this physical quantity or dynamic quantity, and for the description format an analog, digital or encoded method etc., might be used. Although any physical quantities could be used as output signal, electrical quantities such as voltage are more convenient for data input to a computer. The format of the output signal can be analog or digital. For convenience in data input to the computer, it is preferable if the output signal of the sensor itself is in the form of a digital electrical signal. In such cases, a suitable means of signal conversion must be provided to input the data from the sensor to the computer 2.3.3 Classification Based on Accuracy When a sensor system is constructed, the accuracy of the sensors employed is a critical factor. Usually sensor accuracy is expressed as the minimum detectable quantity. This is determined by the sensitivity of the sensor and the internally generated noise of the sensor itself. Higher sensitivity and lower internal noise level imply greater accuracy. Generally for commercially available sensors the cost of the sensor is determined by the accuracy which it is required to have. If no commercial sensor can be found with the necessary accuracy, a custom product must be used, which will increase the costs. For ordinary applications an accuracy of about 0.1% is sufficient. Such sensors can easily be selected from commercially available models. Dynamic range (full scale deflection/minimum detectable quantity) has practically the same meaning as accuracy, and is expressed in decibel units. For example a dynamic range of 60dB indicates that the full scale deflection is 103 times the minimum detectable quantity. That is, a dynamic range of 60dB is equivalent to 0.1% accuracy. In conventional sensors, linearity of output was regarded as quite important. However, in intelligent sensor technology the final stage is normally data processing by computer, so output linearity is not a particular problem. Any sensor providing a reproducible relationship of input and output signal can be used in an intelligent sensor system. Chapter 3 3.1 Sensor selection The function of a sensor is to receive some action from a single phenomenon of the subject of measurement and to convert this to another physical phenomenon that can be more easily handled. The phenomenon constituting the subject of measurement is called the input signal, and the phenomenon after conversion is called the output signal. The ratio of the output signal to the input signal is called the transmittance or gain. Since the first function of a sensor is to convert changes in the subject of measurement to a physical phenomenon that can be more easily handled, i.e. its function consists in primary conversion, its conversion efficiency, or the degree of difficulty in delivering the output signal to the transducer constituting the next stage is of secondary importance The first point to which attention must be paid in sensor selection is to preserve as far as possible the information of the input signal. This is equivalent to preventing lowering of the signal-to-noise ratio (SNR). For example, if the SNR of the input signal is 60 dB, a sensor of dynamic range less than 60 dB should not be used. In order to detect changes in the quantity being measured as faithfully as possible, a sensor is required to have the following properties. Non-interference. This means that its output should not be changed by factors other than changes in the subject of measurement. Conversion satisfying this condition is called direct measurement. Conversion wherein the measurement quantity is found by calculation from output signals determined under the influence of several input signals is called indirect measurement. High sensitivity. The amount of change of the output signal that is produced by a change of unit amount of the input quantity being measured, i.e. the gain, should be as large as possible. Small measurement pressure. This means that the sensor should not disturb the physical conditions of the subject of measurement. From this point of view, modulation conversion offers more freedom than direct-acting conversion. High speed. The sensor should have sufficiently high speed of reaction to track the maximum anticipated rate of variation of the measured quantity. Low noise. The noise generated by the sensor itself should be as little as possible. Robustness. The output signal must be at least more robust than the quantity being measured, and be easier to handle. Robustness means resistance to environmental changes and/or noise. In general, phenomena of large energy are more resistant to external disturbance such as noise than are phenomena of smaller energy, they are easier to handle, and so have better robustness. If a sensor can be obtained that satisfies all these conditions, there is no problem. However, in practice, one can scarcely expect to obtain a sensor satisfying all these conditions. In such cases, it is necessary to combine the sensor with a suitable compensation mechanism, or to compensate the transducer of the secondary converter. Progress in IC manufacturing technology has made it possible to integrate various sensor functions. With the progressive shift from mainframes to minicomputers and hence to microcomputers, control systems have changed from centralized processing systems to distributed processing systems. Sensor technology has also benefited from such progress in IC manufacturing technology, with the result that systems whereby information from several sensors is combined and processed have changed from centralized systems to dispersed systems. Specifically, attempts are being made to use silicon-integrated sensors in a role combining primary data processing and input in systems that measure and process two-dimensional information such as picture information. This is a natural application of silicon precision working technology and digital circuit technology, which have been greatly advanced by introduction of VLSI manufacturing technology. Three-dimensional integrated circuits for recognizing letter patterns and odour sensors, etc., are examples of this. Such sensor systems can be called perfectly intelligent sensors in that they themselves have a certain data processing capability. It is characteristic of such sensors to combine several sensor inputs and to include a microprocessor that performs data processing. Their output signal is not a simple conversion of the input signal, but rather an abstract quantity obtained by some reorganization and combination of input signals from several sensors. This type of signal conversion is now often performed by a distributed processing mechanism, in which microprocessors are used to carry out the data processing that was previously performed by a centralized computer system having a large number of interfaces to individual sensors. However, the miniaturization obtained by application of integrated circuit techniques brings about an increase in the flexibility of coupling between elements. This has a substantial effect. Sensors of this type constitute a new technology that is at present being researched and developed. Although further progress can be expected, the overall picture cannot be predicted at the present time. Technically, practically free combinations of sensors can be implemented with the object of so-called indirect measurement, in which the signals from several individual sensors that were conventionally present are collected and used as the basis for a new output signal. In many aspects, new ideas are required concerning determination of the object of measurement, i.e. which measured quantities are to be selected, determination of the individual functions to achieve this, and the construction of the framework to organize these as a system. 3.2 Structure of an Intelligent Sensor The rapidity of development in microelectronics has had a profound effect on the whole of instrumentation science, and it has blurred some of the conceptual boundaries which once seemed so firm. In the present context the boundary between sensors and instruments is particularly uncertain. Processes which were once confined to a large electronic instrument are now available within the housing of a compact sensor, and it is some of these processes which we discuss later in this chapter. An instrument in our context is a system which is designed primarily to act as a free standing device for performing a particular set of measurements; the provision of communications facilities is of secondary importance. A sensor is a system which is designed primarily to serve a host system and without its communication channel it cannot serve its purpose. Nevertheless, the structures and processes used within either device, be they hardware or software, are similar. The range of disciplines which arc brought together in intelligent sensor system design is considerable, and the designer of such systems has to become something of a polymath. This was one of the problems in the early days of computer-aided measurement and there was some resistance from the backwoodsmen who practiced the art of measurement. 3.2.1 Elements of Intelligent Sensors The intelligent sensor is an example of a system, and in it we can identify a number of sub-systems whose functions are clearly distinguished from each other. The principal sub-systems within an intelligent sensor are: A primary sensing element Excitation Control Amplification (Possibly variable gain) Analogue filtering Data conversion Compensation Digital Information Processing Digital Communication Processing The figure illustrates the way in which these sub-systems relate to each other. Some of the realizations of intelligent sensors, particularly the earlier ones, may incorporate only some of these elements. The primary sensing element has an obvious fundamental importance. It is more than simply the familiar traditional sensor incorporated into a more up-to-date system. Not only are new materials and mechanisms becoming available for exploitation, but some of those that have been long known yet discarded because of various difficulties of behaviour may now be reconsidered in the light of the presence of intelligence to cope with these difficul ­ties. Excitation control can take a variety of forms depending on the circumstances. Some sensors, such as the thermocouple, convert energy directly from one form to another without the need for additional excitation. Others may require fairly elaborate forms of supply. It may be alternating or pulsed for subsequent coherent or phase-sensitive detection. In some circumstances it may be necessary to provide extremely stable supplies to the sensing element, while in others it may be necessary for those supplies to form part of a control loop to maintain the operating condition of the clement at some desired optimum. While this aspect may not be thought fundamental to intelligent sensors there is a largely unexplored range of possibilities for combining it with digital processing to produce novel instrumentation techniques. Amplification of the electrical output of the primary sensing element is almost invariably a requirement. This can pose design problems where high gain is needed. Noise is a particular hazard, and a circumstance unique to the intelligent form of sensor is the presence of digital buses carrying signals with sharp transitions. For this reason circuit layout is a particularly important part of the design process. Analogue filtering is required at minimum to obviate aliasing effects in the conversion stage, but it is also attractive where digital filtering would lake up too much of the real-time processing power available. Data conversion is the stage of transition between the continuous real world and the discrete internal world of the digital processor. It is important to bear in mind that the process of analogue to digital conversion is a non-linear one and represents a potentially gross distortion of the incoming information. It is important, however, for the intelligent sensor designer always to remember that this corruption is present, and in certain circumstances it can assume dominating importance. Such circumstances would include the case where the conversion process is part of a control loop or where some sort of auto-ranging, overt or covert, is built in to the operational program. Compensation is an inevitable part of the intelligent sensor. The operating point of the sensors may change due to various reasons. One of them is temperature. So an intelligent sensor must have an inbuilt compensation setup to bring the operating point back to its standard set stage. Information processing is, of course, unique to the intelligent form of sensor. There is some overlap between compensation and information processing, but there are also significant areas on independence. An important aspect is the condensation of information, which is necessary to preserve the two most precious resources of the industrial measurement system, the information bus and the central processor. A prime example of data condensa ­tion occurs in the Doppler velocimctcr in which a substantial quantity of informa ­tion is reduced to a single number representing the velocity. Sensor compensation will in general require the processi

Ambition :: essays research papers

That formidable force that makes one believe that one needs what one usually only desires; that mind-set that is really more of an entity that sits on ones shoulder, and relentlessly screams â€Å"further, further†; that asset that makes those who are not zealous, jealous... That is ambition.Ambition has been the backbone of every army! Through those great ancient Egyptian wars, through Persia, through Hastings, through Waterloo, through the native American/greedy colonist battles, through the world-wars, through the Balkans, and through every other great conflict that has ever existed but that I am unable to cite, each party was blessed by pure and passionate ambition...ambition to win at whatever cost necessary. Surely only the collective force of ambition found in a battle is liable to cause as much suffering and damage as has been caused by all battles that have ever been lost or won? Even the weakest, most injured warrior who persevered has been touched not by insanity, but by raw ambition, and even the most perturbed and exasperated warlord is supported not by his schemes, but by the ambition to realise them.Ambition is the ultimate wonder of the world! Ambition made all seven of them and more...shouldn’t it be regarded as their veritable (no matter how intangible) superior? From the conception to the design to the construction to the completion, ambition was the proverbial foreman, as once again we see that collective zeal create a phenomenon...thus...Ambition is the source of all that is good and all that is evil! It makes the wonders and it makes the wars.Ambition is the winner and loser of every game! Every footballer, every chess player, every marathon runner, every duck-legged Olympic walker smells of fervour which seeps from the pores of their ambition.When years of dedication pass the baton to ambition, there is a sage to be reckoned with. One only wonders why in spite of the eternal paradoxical query â€Å"what would happen if an irresistible force met with an irresistible force?†, someone always has to lose?Ambition makes you healthy, wealthy, and wise! Doesn’t it? Observe this immortal quotation of Henry Ford:"Whether you think you can or you think you cannot, you are right"The truth he successfully conveys is that we are the scribes of our own destiny. It is ambition that makes us think we can be successful, and lack of ambition that makes us think we cannot.

Riordan Manufacturing Executive Report

Riordan Manufacturing Executive Report Warren Buffet once said, â€Å"Price is what you pay. Value is what you get. † With a company that has over five hundred employees, four locations worldwide, and $50 million in annual sales, placing the value on the organization is simple; look at the bottom line and see the profit. This is the situation at Riordan Manufacturing where the price it paid to do business was less than what it made, defining a clear value in what Riordan provides.Riordan’s Sales and Marketing department has a clear value; $50 million is sales to show of it. But how do we show the value for other departments within the organization such as Information Systems (IS) and Information Technology (IT) solutions? While the IS and IT costs and what Riordan pays for it are clear from looking at the financials, the value is not. This brings us to the question of what value does Riordan Manufacturing get out of its IS services and IT department. This question is th e problem that Riordan Manufacturing has had for many years.Recently, Riordan executive management hired a new Chief Information Officer (CIO) to improve its infrastructure and to answer this question. While management wants to use more IS and IT solutions throughout the organization, it has had trouble finding the perfect fit in all aspects of its operations. Even though IS and IT costs have risen, the puzzle pieces still have not come together; the value and benefit has not been clear to the company. As we will see throughout this review, it is time to focus and find the value that technology and information brings to the organization.It is time to find the efficiencies and savings that the company needs to clearly see the value. With Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or Supply Chain Management (SCM) systems, there has got to be value in the way they are used. There has also got to be value in using outside services such as Application Ser vice Providers (ASPs) than can help realize and utilize the value of these systems. Finding the best way to utilize technology will allow Riordan to find the best value in the IS and IT departments.Business systems analysis and recommendations Riordan Manufacturing prides itself on its public reputation as the leading edge manufacturer of plastic injection moldings that is backed by a powerful and innovative research and development team. However, internally, the organization is suffering greatly from severely outdated, labor-intensive, pencil and paper processes in the sales and marketing departments. Additionally, the business processes that are automated are departmentalized suited for the use of a single job function or department.Existing automated processes belong to the finance and accounting department as well as the inventory office of the manufacturing department. There is no connectivity between systems, and worse, attempts to establish communications between similar syst ems across the organization’s national and multinational locations have been fruitless. It is impossible for organization decision-makers to have a clear idea of the interworking of the organization and its financial health.Chief Operating Officer (COO), Hugh McCauley, has developed a comprehensive strategic direction for the organization decomposed into individual strategic initiatives and further broken down into various programs that are the responsibility of relevant departmental leaders to accomplish. Riordan’s COO has done an excellent job of initiating a strategic direction and smaller goals to ensure the organization maintains industry leader status, but the missing link that will truly drive each program’s success, is an information technology linkage.To remain ahead of competition and to transform into a more agile organization, it is recommended that Riordan’s management evaluate the benefits of enterprise-wide information systems. Enterprise-w ide information systems Organizations commonly implement enterprise-wide systems to improve access to information and as a result experience growth, reduce costs, and improve efficiencies. The benefits of enterprise-wide systems such as enterprise resource management (ERP), customer relationship management (CRM), and supply chain management (SCM) directly align with Riordan’s strategic direction.Proper implementation of these systems will add value to Riordan’s business model. However, enterprise-wide implementations are characterized by large investments and large time commitments. Therefore, the organization must evaluate which systems will help it achieve the majority of its goals, in a reasonable time frame. In doing so, decision-makers can determine if each system’s value is worth its costs and the order in which to invest in and implement each system. Enterprise Resource Management (ERP) Pros.An ERP system is a necessary investment for Riordan because it i ntegrates all departments and their respective functions across the organization into a single IT system (UMaine, 2009). There are three main benefits of ERP systems that directly address problems with Riordan’s operations. One benefit is a logical solution to a mess of incompatible applications currently in use by the organization. ERP also allows global access and sharing of organizational data as well. Additionally, implementing an ERP system will help the organization bypass the difficulties and expenses of replacing legacy systems (UMaine, 2009).An analysis of Riordan’s current issues with its Finance and Accounting department reveal an immediate need for an ERP solution. Riordan’s current process to complete the general ledger, income statement, and balance sheet is so labor intensive that accountants cannot complete the task until two or three weeks after month’s end. Additionally, external audits are required monthly. Riordan’s process docu mentation is difficult to analyze, making this important task costly and labor intensive as well. Compliance with new, stricter government regulations is also extremely difficult for this department.Riordan management finds these issues unacceptable and expects them to be addressed first (University of Phoenix, 2012). An ERP solution will correct these error-prone, labor-intensive processes through automation (Business-Software. com, 2010). This will help employees of the Finance and Accounting department complete Riordan’s financial statements in a timelier manner. Further, ERP will ensure that all workflows and procedures are formally documented, allowing external auditors to complete their job more effectively and produce timely and accurate feedback for Riordan management.ERP systems also ensure there is only â€Å"one version of truth† by feeding data to one centralized, integrated database (Business-Software, 2012). Not only does ERP help the organization deliver timely, accurate information to its customers and suppliers but is also necessary for regulatory compliance. Cons. Although an ERP system will lay the foundation for modern, efficient enterprise-wide business systems, Riordan must weigh the risks of implementing the system. The first risk is cost. For a multinational company like Riordan, the cost of installation can range from $30,000 to $500 million (Demand Media Inc. , 2012).Riordan will need to do an extensive financial analysis to determine if the organization has enough capital to expend on an ERP implementation as well as enough contingency funding to bail the organization out should the implementation fail. Another risk of ERP implementation is the level of complexity that and ERP system will add to Riordan’s processes. Riordan’s current staff may find the system to be too difficult to use and rebel against the system as a result. Riordan may also find that a portion of their existing staff may turnover as a r esult of the implementation and will need to hire a more specialized user base as a result.These specialized employees may require higher salaries than those they replace. To avoid this, Riordan will want to provide comprehensive training to affected users, but it will be a time-consuming processefficiency benefits of the system will not be measurable until the organization adjusts to the changes that the ERP system will bring (Demand Media Inc. , 2012). An important risk of ERP implementation to consider is data integrity. Because the ERP system’s database will be the single source of Riordan’s data, it must be accurate and secure.Integrating the ERP system with existing systems may require some software modifications. It will be important to ensure that the integration of system results in data that is single version of the truth as well as securing any new transactions between systems. It is also important to note that some ERP systems will be too inflexible to work with Riordan’s current business process and strategy (Demand Media Inc. , 2012). The organization must evaluate vendor and implementation options to ensure the ERP system help drive Riordan’s objectives, not hinder them.Customer Relationship Management (CRM) Pros. Riordan’s strategic direction and initiatives are highly customer-centric. Riordan’s strategy is to compete in its market by serving the top 15 customer clusters, and driving this strategy by providing the highest level of customer satisfaction to its 20 most important account holders. Additionally, the COO wants the organization to transform the Riordan brand into a significant competitive advantage (University of Phoenix, 2012). A CRM system is an excellent way to drive the success of this customer-centric strategy.CRM manages all aspects of an organization’s relationship with its customers to help increase customer loyalty, retention, and the organization’s profitability (UMain e, 2009). Additionally, CRM systems are synonymous with building brand awareness and loyalty. A modern definition of a brand is the summation of hundreds of small interactions between an organization and its customers (Yarmoff, 2001). A CRM system can capture the data that results from these actions for analysis by the marketing department. Marketing analysts can determine what is unique about the organization that draws customers to it, helping Riordan build its brand.In the upcoming fiscal year, Riordan is launching an aggressive sales and marketing program to grow its revenue, expand its customer base, and retain its best customers. The sales and marketing department will find difficulty in successfully completing this program considering their outdated, pencil and paper processes within the department. It will be difficult for Riordan’s marketers to target customers effectively by sifting through hundreds of paper files of historical sales records to conduct market analys is.It will be difficult for the sales department to document special customers’ needs, and ensure they are served throughout the organization’s order fulfillment processes by using the disparate sales systems currently in use. For back office analytical purposes, CRM can assist the marketing department in drawing upon data from a single data source to reveal trends, explain outcomes, predict results of campaigns, and identify the organization’s most profitable customers (UMaine, 2009).Analytical CRM helps the marketing department understand what Riordan’s customers like, dislike, and what appeals to them and indicate if Riordan is meeting or is capable of meeting customer’s needs. Analytical CRM provides this deep understanding of an organization’s customer base through data analysis and business intelligence tools (UMaine, 2009). It sends pertinent information to the marketing department for campaigns and to the front-end part of the system to provide the sales force with the information it requires. On the front-end, operational CRM can assist the sales representatives.Technology will include a contact management system, and opportunity management system. In this use-case, the CRM system will alert the sales representative regarding what the customer likes/dislikes to enhance cross-selling and up-selling opportunities. Front-end CRM will also help the sales representatives in resolving customer issues by providing web-based customer self-service tips and call scripting to better equip the representatives with handling the most common issues (UMaine, 2009). The CRM system can log recurring customer issues.This will assist Riordan’s sales representatives in solving difficult problems that have been previously addresses as well as assist management in targeting recurring scenarios that require improvement, resulting in increased customer satisfaction and retention rates. Cons. Like other enterprise-wide systems, CRM systems are characterized by expensive and difficult implementations. Riordan management has to ensure that the CRM initiative is well planned, historical data is input accurately, and ensure workflow is properly defined to reduce the risk of project failure (Gartner Research, 2000).Success will also be highly dependent on cooperation with the ERP and SCM initiatives, as they eventually will become integrated systems dependent on one another’s data inputs and outputs. Solutions must be chosen with compatibility and interconnectivity in mind. In CRM, over-automation is always a risk (Gartner Research, 2000). CRM is about bringing a company and its customers closer together, and some human-to-human interaction is necessary for customers to feel as if they are being heard. Finally, CRM systems are difficult systems to measure numeric outcomes and value (Gartner Research, 2000).Riordan management must define what unique, possibly intangible outcomes they want to measure from the CRM system to ensure it is providing its intended value. Supply Chain Management (SCM) Pros. As a manufacturer, Riordan would experience many benefits from the implementation of a SCM system. As part of its business strategy, Riordan is currently striving for supply chain excellence. The organization will drive this strategy by streamlining time-to-market processes, achieving 90% of customer requested ship dates, and reducing inefficiencies associated with its current shipping methods (University of Phoenix, 2012).A SCM system can help the organization achieve these goals as it manages the information flows between and among stages in a supply chain to maximize total supply chain effectiveness and profitability (UMaine, 2009). A SCM system captures data from the five phases of the supply chain management process; planning, sourcing, making, delivering, and returning. This data translates into complete visibility and awareness of one’s supply chain, and in turn, competitiv e advantages. A SCM will be most effective in the planning phase if Riordan leverages information from an ERP and CRM system.With that data, the SCM system uses metrics to forecast and accurately meet customer demand. Riordan will eliminate waste from its inventory by having the right amount of materials on hand to fulfill customers’ orders as they are placed, reducing costs stemming from holding onto excessive inventory. Developing insight across all inventory locations will also permit better sharing of resources on-hand to meet emergency customer needs. In the sourcing phase, the SCM system will provide vendor management capabilities. Riordan can input data about eliable suppliers it has partnered with in the past as well as suppliers that have provided inadequate services to document differences in quality among vendors. Riordan can also capture pricing data to determine which vendors provide the best value for their products. This will make sourcing easier and more effec tive. The deals Riordan obtains from strong vendor partnerships could translate in more discounts for customers farther down the line in the supply chain. In the â€Å"make† phase of SCM, a SCM system can ensure Riordan is manufacturing its products in accordance with the organization’s quality standards.Managers can determine the desired quality levels, translate the quality levels into metrics, and have these performance metrics monitored with the SCM system. The system will indicate when manufacturing is in line with quality metrics, surpassing metrics, or below metrics. A good deal of Rirodan’s reputation is dependent on manufacturing high quality products, so performance metrics in this area are important. The deliver phase is important to monitor with the SCM system as Riordan is experiencing inefficiencies in its logistic processes.The SCM system will help Riordan deal with processes and controls of logistic process to create efficient and effective trans port and storage of its products as they are delivered to the customer (UMaine, 2009). A SCM system can analyze delivery times and help management determine where inefficiencies are occurring and why. The results may prompt management to create more conveniently located inventory facility locations to create more reliable transit times. The system will also help Riordan coordinate more effectively with its outsourced trucking company.The ability to share information between both parties will allow Riordan to load its trucks to 100% capacity, resulting in cost savings for itself as well as less trucks and drivers for its partner. Finally, the return phase of SCM will be most effective when combined with data from the CRM system. The return phase of SCM pertains to the process allowing customers to return defective and excess products (UMaine, 2009). It is important to capture why customers are returning products to serve them better in the future and learn from errors. It is also imp ortant forRiordan to be instantly aware that a return process has been initiated, so it can send out a replacement to a customer immediately. The customer may be dissatisfied as it is, so handling returns effectively can encourage the customer to continue using the company for future orders. This phase will help Riordan meet its goals of serving customers better as well as retaining them. Cons. Riordan must consider the many risks of supply chain management because this system is dependent on the cooperation of external business partners. The first roadblock is gaining trust from business partners.Riordan and its suppliers must be willing to exchange some confidential information in exchange for increase supply chain efficiency. Next a supply chain is only as strong as its weakest link, so if Riordan’s suppliers cannot provide quality goods in proper time frames, Riordan cannot get the most out of its SCM system (Wailgum, 2008). Internally, there will be resistance to change as well. Employees will need to adjust to stricter data entry requirements as well as higher scrutiny of their performance. Training will be crucial, as mistakes with the system will be made initially.A SCM system cannot absorb a company’s history and processes in the first few months (Wailgum, 2008) Management will have to be patient and continually feed the system clean data to reap visibility benefits. Recommendations The success of implementing enterprise-wide business tools is dependent on the creation of a single data source, populated by accurate and relevant information. To lay the foundation that will capture and integrate all information from Riordan’s unique workflows and processes, it is recommended that Riordan first invest in and implement an ERP system.Once this implementation is complete, and the organization is accustomed to the changes this system will bring, a CRM implementation can be considered. The ERP system’s centralized database will ser ve as the data source for the CRM system, making this implementation an easier transition for the organization. Once the CRM system is successfully implemented, a SCM implementation can be considered as Riordan’s financial health and strategic initiatives permit. A SCM system is recommended as the final enterprise-wide system to implement as it is reliant on information captured by both ERP systems and CRM systems (Wailgum, 2008).The success of these implementations will be dependent on a number of factors such as implementing the solutions in-house versus outsourcing the efforts, and implementing a performance metric system to ensure that the systems are continually providing the value intended. Outsourcing Models Riordan needs to consider outsourcing some of its business functions to reduce upfront cost and integration challenges faced while implementing enterprise applications ERP and CRM. There are many outsourcing models to consider and many benefits and advantages.There fore, a thorough analysis and understanding of outsourcing models is necessary for Riordan’s long-term strategic alliance with vendors. This section outlines outsourcing models and services provided by the application service provider or ASP and other outsourcing models. ASP Model Application service provider is one who has expertise in implementing and managing IT operations of the business applications over a secure Internet on behalf of its customer or client.ASP also known as Managed Application provider (MAP), or managed services â€Å"combine hosted software, hardware and networking technologies to offer a service-based application, as opposed to a company-owned and operated application† (Sans Institute, 2006). ASP services include end-to-end solutions necessary for executing and operation of ERP, CRM, accounting, payroll, cloud computing, and customized applications. In the ASP model, the provider typically identifies the applications common to many organization s (for example, ERP) and hosts them in their data centers.The access to applications provided via a browser-based or thin client software. According to Pearlson and Saunders (2010), â€Å"ASP not only provides access to software, but infrastructure, people, and maintenance to run it in a customized fashion for a client. † Hence, the objective of ASP is to provide a secure, error-free environment of application systems and infrastructure round the clock. The ideal candidates for taking the advantage of ASP are Riordan’s non-core applications, which relaxes IT resources and make them available for core applications.Another instance of ASP is Software as a Service (SaaS), which host multiple companies (multi-tenant) to use the same set of software and hardware, but still provide a user experience of single application. The application accessed via Internet, and provides rich web interface using technologies like AJAX and XML. The web applications delivered via SaaS is cus tomizable and integrates into in-house application using web services and ETL tools. For example, Sales Force applications from SalesForce. com are multi-tenant web applications used by many organizations as their primary CRM application.Engagement with ASP involves service level agreements (SLAs), which contains many clause and vendor expectations. SLAs consists of sections on â€Å"availability, accessibility, performance, maintenance, backup/recovery, upgrades, equipment ownership, software ownership, security, and confidentiality† (Pearlson & Saunders, 2010). ASP may provide cost-effective solution in their area of their expertise. However, for security professionals, the move to use the ASP model comes at an often-high cost. The ASP may be an expert in its domain, but its security function may be immature (Schoenfield, n. d. ).Hence, one should consider risk assessment and analyze the end-to-end solution of ASPs and their security models. Many ASPs available in the marke t, Riordan should evaluate them once the outsourcing requirement finalized. The table below provides list of ASPs and their domain expertise. Table 1 List of ASPs Application Service Provider Domain expertise Appshop www. appshop. com Oracle 11i ebusiness suite Applications BlueStar Solutions www. bluestarsolutions. com Managing ERP solutions with a focus on SAP Corio www. corio. com Specializes in Oracle Applications Outtask www. outtask. com Integration of budgeting, customer service, sales anagement, and human resources applications Surebridge www. surebridge. com High-tech manufacturing, distribution, health care applications USi www. usinternetworking. com Ariba, Siebel, Microsoft, and Oracle customer base Note. Adapted from â€Å"Information Systems Sourcing,† by K. E. Pearlson and C. E. Saunders, 2010, Managing and Using Information Systems. A Strategic Approach. Copyright 2010 by John Wiley & Sons Inc. According to Subramanian and Williams (2007), a complex scenario o f services provided by single or multiple vendors offer multiple benefits. The long term agreements are necessary to reap higher benefits.This model provides competence needed in the initial stage and provides better quality, increases productivity, and reduces cost as time progress. Service provider takes full end-to-end responsibility by investing on new technologies, mitigating risks to maintain business continuity and building high-level of trust. Figure 1. Services offered by managed Services. Note. Reprinted from Infosys White Paper (p. 5), by Subramanian and Williams, 2007, Copyright 2011 Infosys Limited Crowdsourcing Crowdsourcing is a new outsourcing model introduced in 2006 by Jeff Howe in an article titled â€Å"The rise of crowdsourcing† in WIRED online magazine.In the traditional outsourcing model, the work of an employee outsourced to external service provider. In this model, the available skilled resources and ideas limited to service provider and its industry and domain experience. Crowdsourcing reach out to a larger community over the Internet to complete a job or task. Thus, organizations gain access to a wide range of skills and resources available online. According to Jeff Howe the definition of Crowdsourcing is the act of taking a task traditionally performed by an employee or contractor and outsourcing it to an undefined, generally large group of people, in the form of an open call.Companies and individual make an open call to perform a job for a small amount. Open call to an Internet community of collective intelligence can increase productivity. With the advancement of Internet and Web 2. 0, many websites like elance. com, odesk. com and guru. com provide abundant resources of freelancers available globally. Riordan can make use of this model where the projects need specialized skills and risk is very low. Resources picked on reviews and rewards obtained from past assignments.For example, Riordan’s websites along with dyna mic B2B and B2C pages using PHP and open source technologies is a good candidate of Crowdsourcing. Disadvantages of Crowdsourcing are low quality, communication issues, and researching for reliable resources with required skill set. Full Outsourcing Full outsourcing refers to outsourcing overall IT functions in an organization to an external service provider. This is similar to ASP model but the hardware and software remain on-site and vendor resources may collaborate with employee on-site or remotely, depending on the IT functions.For example, an enterprise may outsource helpdesk and desktop support. In this case vendor’s resources remain on-site to provide support on hardware and software issues. Some of software issues rectified remotely. Software development and maintenance happen in the vendor’s location. Companies typically choose this model if their perspective of IT does not support organizations strategic initiative. Doing so managers, and employees can concen trate on other value-adding assignment. Companies outsource completely to accommodate growth and respond to their business environment (Pearlson & Saunders, 2010, p. 09) with SLA’s and multiple vendors. Riordan should not opt for full outsourcing because of risk in exposing copyrighted material, formulas, trade secrets, and unique manufacturing methods to competitors. Selective outsourcing fit well for Riordan. Selective Outsourcing Selective outsourcing allows IT executives with options of retaining few IT functions in-house for strategic reasons. Selective outsourcing gives greater flexibility and often better service because of competitive market (Pearlson & Saunders, 2010, p. 210). According to Subramanian and Williams (2007), another name for selective outsourcing is â€Å"Strategic out-tasking. IT executive will have total control, manage projects, and review deliverables in-house. Only few IT functions like new application development, enhancing application due busine ss changes, fix non-critical issues outsourced and vendor take responsibility. Selective outsourcing is best suited for companies new to outsourcing. Riordan must outsource short-term assignments and small projects before venturing outsourcing in a bigger scale and when they cannot find resources with specific skills or to gain strategic advantage.Outsourcing versus in-house implementations Rose India Technologies PVT. Ltd (2011) defines outsourcing as â€Å"the process by which a company contracts another company to provide particular services†. These services and functions would be otherwise carried out in-house by the company’s own employees. The main reason companies outsource supply chain management are to reduce cost, free up internal resources, save time, to gain better control of managing functions, not enough internal resources to handle the job and share the risk with a partner.Some of disadvantages of outsourcing SCM include the underestimation of cost due t o communication, inadequate governance meaning that an in-house overseeing committee needs to be set up, reduction of technical, key information and crucial knowledge control, dimensioning leadership with the business relations managed by supplier, increase in business continuity, increased cost due to salaries raises in other companies, and unethical suppliers. Outsourcing SCM will save the organization money in IT expenditures.The system becomes is streamlined and use by all location, more energy and money is left for core business strategies. The vendor will handle the development and implementation of custom finance software along with an accounting package along with the establishment of a joint venture offshore back office operation of the company’s invoicing, revenue processing, and auditing services. Some IT functions should not be outsourced such as core business competencies, functions that are knowledge based, and are company proprietary information.Multidisciplina ry, Interdepartmental, factuality, and critical business function that may involve political risk should not be outsourced as well. Riordan should not outsource its core business function this part of the project should be done in-house so that Riordan maintain and controls these function to keep down all risk whether they are security risk or political risk. The reason most companies outsource ERP system is because they do not have the experience and the expertise to implement an ERP package.When it comes to implementation the supplier has a perfected system for installation and implementation, and most organizations do not want to assign full time staff to implementation thereby taking away from the day-to-day work as well as ERP package can be confusing and frustrating to employees due to false starts and downtime. Outsourcing ERP tends to be a good decision when it comes to medium to lager companies because more than likely outside help will be need for consultation, references, credentials, implementation, and monitoring and check consultants.Stress within the company is one disadvantage because employees must learn a new system, and process that may affect productivity and efficiency. The effect can be short and long term. Other cons such cost overruns during and implementation, converting, training and customized modules. The ERP system should be outsourced to a vendor can handle the testing and coding of the new system to insure it integrates with the existing or new MRP systems well verification and documentation leaving the IT Department free to oversee vendor and other IT functions ( Janstal,1999).Many ERP vendors and consulting firms, who have professional implementation and customization skills for manufacturing ERP software less adjustments, are necessary because they will design software specifically for Riordan Manufacturing. The cost for the production of ERP software purchase depends on the size and functions of the software and the extent of the adjustment (Baihaki,2009).The business of Customer Relationship Management (CRM) is evolving and changing in the market on a constant and constant base to meet the growing and demanding need for new strategies that increase business profit margin by having an application that create interactive analysis of the customers’ requirements and leading to the customers satisfaction. C. R. M s’ are all over the market place the need is to choose one, implement, install and train. Simple jobs no need to outsource. Value of Implementations and Outsourcing RecommendationsSupply Chain Management Implementation Studies show that the effective supply chain management (SCM) enables organizations to perform better and maximize profitability by aligning their supply chains with the market demand (Baltzan & Phillips, 2010). SCM implementation helps the organization to reduce inventory levels, minimize order-processing costs, improve responsiveness to customer needs, and compress or der cycle time by streamlining and automating information flow among the different components of the supply chain process (Sumner, 2005).SCM implementation enables an organization to gain competitive advantage by reducing operating costs and increasing process efficiencies to meet market demands and to ensure timely delivery of products or services. Customer Relationship Management Implementation Intense market competition is forcing organizations to change their business models from sales-focused to customer-focused making customer satisfaction a paramount for the organization’s success (Baltzan & Phillips, 2010).Customer relationship management (CRM) implementation will enable organizations to gain insight into customer buying behavior and purchase patterns and develop business strategies to improve customer satisfaction and service quality (Baltzan & Phillips, 2010). Riordan can improve customer satisfaction, service quality, customer loyalty, profitability, and sales volu me by implementing CRM systems to manage its interactions with the customers efficiently. Enterprise Resource Planning Implementation Business leaders require access to real-time business information to make business-related decisions in an efficient manner to improve performance.Enterprise resource planning (ERP) implementation integrates organization’s business processes into an information technology (IT) system facilitating an integrated view of enterprise-wide business information (Baltzan & Phillips, 2010). ERP enables Riordan to streamline, automate, and integrate business processes to improve efficiency of business functions to reduce operating costs, improve customer service, increase revenues, eliminate redundancies, and improve decision-making (Baltzan & Phillips, 2010). Outsourcing RecommendationsOrganizations have to find ways to improve performance of business operations to sustain in the global competition. Outsourcing enables an organization to increase produc tivity, reduce operating costs, and improve flexibility by taking advantage of low labor cost regions and difference in time zones (Morello, 2003). Riordan should consider full outsourcing model for the implementation of SCM, CRM, and ERP systems to another organization while ensuring appropriate knowledge transfer to its employees during the implementation process.Service providers expertise in the implementation of SCM, CRM, and ERP systems will help the organization to streamline, automate, standardize, and fine tune business processes to increase efficiency of business operations. Knowledge transfer to Riordan’s employees during and after implementation process will enable the organization to gain and retain adequate knowledge to support business systems efficiently. Riordan can use the selective outsourcing model for ongoing maintenance of the SCM, CRM, and ERP systems.Outsourcing only the IT support services to another organization will enable Riordan to keep critical b usiness process knowledge in-house while ensuring round-the-clock IT support to its global business operations. Outsourcing redundant IT support services to another organization will help Riordan to concentrate more on business critical functions to increase productivity and improve operations performance. Providing Continued Value Once IT implementations are completed assessing the value of new systems is the next step.When companies invest in IT initiatives they want to see the added value to the organizations IT and IS departments. Setting up a process to measure continued value is essential in achieving this. For Riordan is recommend using IT metrics and Key Performance Indicators (KPI) to measure value. KPIs also known as Key Success Indicators will help the organization define and also measure progress toward organizational IT goals. KPIs are quantifiable measurements that are agreed upon, reflecting the critical success factors of the organization.If Key Performance Indicator s are going to be of any value there must be a way for it to be accurately defined and measured. Equally important, KPIs once defined should be consistent year to year. The organization should also look to set targets for each Key Performance Indicator. Once KPIs are defined a way to measure it needs to be set up to collect information, a target, has to be established. Below are some KPIs for Riordan in assessing success of IT initiatives: IS and IT implementation adds tangible value to organization Implementation reduce IS and IT costMeasuring Effectiveness Measuring the effectiveness of a system can show its value to the organization but also set a benchmark to continually assess the system year after year. Using Effectiveness IT metrics measures its effectiveness on IT systems from the standpoint of a business tool. The metric can measure how IT affects specific aspects of a business and the business process such as conversion rates, customer satisfaction, and sell-through increa ses. Specifically, the metrics goal is to show how well a company is doing in reaching its objectives.In constantly questioning, it determines if the right decisions are being made to reach these objectives. One way we can measure effectiveness is through surveys to IT, finance and HR departments. The survey giving to employees can give insight as to if the system is helping make work more productive to Riordan employees or caused increased difficultly. The surveys should look to answer these questions: Is the system increasing daily productivity? Does system work seamlessly with critical IT systems? Are there any disadvantages to the new system?Is the system easy to support? Easy to use? Is more staff training needed? Establishing benchmarks is a typical way of measuring performance of IT Effectiveness. Benchmarking puts IT in greater demand to align with the business and demonstrate the company economic value. IT benchmarks can show how competitive IS and IT services are, and if t here are ways to improve the process or increase efficiency of delivery of services. There are different models that can be used for Riordan to asses IS values. These are recommended:Peer/ industry comparisons Customer satisfaction IT effectiveness/value IT efficiency/cost Business IT process Cost benchmarking will address the problem of cost and quality of services. It can show what can be done to reduce cost and improve performance by showing best practices in the industry. Figure 2. Cost Benchmarking. Reprinted from IT Benchmarking: A Baseline for improving performance, by Ambuhl and Bitterman, 2004. A successful benchmark is valued by the actionable recommendation that yield immediate and long-term results.Be sure to outlining specifics in improving efficiency and effectiveness or it will have no significant value. Measuring Efficiency Measuring IT effectiveness is only one aspect to determining IT value. Efficiency IT metrics while similar has differences. This metric will meas ure the performance of an IT system. It measures performance such as throughput, speed, and availability of a system. This will also determine how well the newly implemented system works with established systems. To measure performance organized documenting and reporting process must be in place.Efficiency IT metrics can be used to measure the throughput, how fast information is travelling throughout Riordan’s intranet or the speed of transactions with its suppliers and customers. Additionally, it can measure traffic to a website. Traffic measures how many people come to a website in a given period of time. Additional benefits to the Efficiency IT metric, it not only measures the efficiency of a IT system for evaluating and improving its performance, but it makes sure the system is being used the right way, ensuring the effectiveness of the processes, and that they are in step with business objectives.Utilizing methods such as KPIs, Effectiveness IT metrics, Efficiency IT met rics, and benchmarking will help Riordan establish a clear dashboard, adding value to its IS and IT departments with every successful IT implementation. It will also show continual value of its IT systems by performance, which systems continue to add value and which do not. Conclusion As we have seen, Riordan Manufacturing’s utilization of information systems and information technology leaves a lot to be desired. With so much potential, they continue to have manual processes for tasks and information that can and should be automated and interconnected.Because of the lack of technology, executive management is not capable of seeing the big picture they need across the entire organization in order to make the best decisions. Because of this, it is hard for the company to see the value of the continued IS and IT investments. However, with the implementation of such systems as ERP, CRM, or SCM, value can be added, value can be see in the benefits they provide. While different sys tems may have a better fit within the organization, Riordan must determine what is best for them; they must determine how they will be used in the most beneficial way.With any major infrastructure change, there are new risks and costs to the organization. The correct hardware must be purchased; the right software must be installed; the business rules and requirements must be met; the systems must be maintained over time. Putting these puzzle pieces together might not always go as well as planned. This is why Riordan must consider some of the outsourcing solutions discussed. Deciding on using an ASP, running in a SaaS model, or installing and maintaining the systems themselves, each require extremely detailed analysis.While it is easy to see the cost of each of these systems and deployment methods, Riordan must continue to go back to determining the value of each system at the same time. If the value is a reduced IT staff; if the value is a more integrated system; if the value is a m ore productive manufacturing process, the systems will show their value themselves. Riordan Manufacturing wants to receive the value out of what they pay for. Yet we see that value is not a pay for what you get model, but value is in how you use what you paid for. References Ambuhl, C. , & Bitterman, M. (2004). IT Benchmarking: A Baseline for mproving performance. 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