Dss Analysis And Decision Support System Information Technology Essay

Dss Analysis And Decision Support System Information Technology Essay Abstract During our study and research on DSS we came to mutual agreement that DSS is an ever evolving domain. Lot of research has been carried out on the usage of DSS in many different domains especially in Clinic. But we found that research on the DSS System as a whole (regardless of which domain) has not been conducted many times in the past. Based on the initial study we have identified the following problems 1. There is no universally accepted definition for DSS, 2. There have been a many reports of failure of DSS systems. In the research paper below we have tried to define DSS system based on the Characteristics and the Targeted users. Paper also covers the decision making process, the decision analysis cycle, Framework of DSS which form the base of the DSS. We have also made an attempt to formulate the Critical success factors of the DSS and Reasons for the failure of DSS. We have tried to collect most of our data through secondary research which involves collating of data from existing research documents and books.   In 1960 J. C. R. Licklider wrote a paper on his observation of how the interaction between man and computer can improve the quality and competency in recognizing and problem solving. His paper proved to be like a guide to many future researches on DSS. In 1962 with use of hypertext online system helped in storage and retrieval of documents and creation of digital libraries. SAGE (Semi Automatic Ground Environment) built by Forrester is probably the first data driven computerized DSS. In 1964 Scott Morton built up an interactive model driven management decision system which could help managers make important management decisions. In 1970 John D.C. Little noted that the requirement for designing models and system to make a management decision was completeness to data, simplicity, ease of control and robustness, which till date are relevant in improving and evaluating modern DSS. By 1975, he built up a DSS called Brandaid which could support promotion, advertising, pricing and product r elated decisions. In 1974 the focus was on giving managers with information which was from accounting and transaction processing system with use if MIS(Management Information Systems) but MIS was found to not helping out managers with making key decisions. Hence in 1979 Scott Morton and Gorry argued that MIS just primarily focused on structured decisions and hence the system which also supports unstructured and semi-structured decision should be termed as Decision support systems. Gorry and Scott Morton coined the phrase DSS in 1971, about ten years after MIS became popular. (David Arnott, An Analysis of Decision Support Systems Research, p.1) Decision support system now-a-days are critical for the daily operation and success of many organizations. Due to which there is a huge investment being made on development, customization, implementation and upgradation of these systems. Despite the rapid growth of information technology over the past decade, the success of Decision Support System remains questionable due to the lack of insufficient studies on the outcomes. As David Arnott and Gemma Dodson stated in Decision Support System Failure (David Arnott, Gemma Dodson, p.1) The development of a decision support system is a risky affair. The Volatile task environment and dynamic nature of managerial work means that DSS Projects are prone to Failure. As per David Arnott and Gemma Dodson definition above its very important to understand why organization take such a big risk and invest in a Decision support system. (Efraim Turban, Ramesh Sharda, Decision Support and Business Intelligence Systems, 8th Edition, p.12) Some of the factors why company use DSS Systems suggested by Efraim and Ramesh are: Speedy Computation Improved Communication and Collaboration Increase Productivity of group members Improved data management Managing Giant Data warehouses Quality Support Agility Support Overcoming cognitive limits in processing and storing information The paper here deals with the study of how decision analysis happens in DSS, Problems and there types, Why DSS are required or implemented by organization, Decision making process, Types of DSS, Reason for the failure of DSS, Critical success factor of DSS. Activities that require decision making form a set or a group of problems, varying from structured problem to unstructured problem. As Simon States The boundary between well structured and ill structured problems is vague, fluid and not susceptible to formalization.  (The structure of ill structured problems, 1973, Herbert A. Simon) the Decision making process, decision made and the style of making decision can be influenced by the personality of the individual and their cognitive style, and which is one of the major reasons for different decision aids being sought. (Management Information System 8/E Raymond McLeod, Jr. and George Schell) Decision types in terms of problem structure: Structured problems can be solved with algorithms and decision rules. A structured decision can be defined as one in which three components of a decision-the data, process, and evaluation. Structured decisions are made on a regular basis in business environments. If a rigid framework is placed for the decision making process it helps to solve the problem. Unstructured problems have no structure in Simons phases. These decisions have the same components as structured ones-data, process, and evaluation- but there nature is different. For example, decision maker use different set of data and process to reach a decision or goal. In addition, as the nature of the decision is different a few numbers of people within the organization are even qualified to evaluate the decision and to confirm one. Semi structured problems have structured and unstructured phases. Most of the DSS System is focused on Semi Structured decision. Characteristics of this type of decisions of this type are Having some agreement on the data, process, and/or evaluation to be used, Efforts to maintain a level of human-judgement in the decision making process. To determine which Support system is required it is necessary to analyze thoroughly and understand the limitations and ill effects, which the decision maker are manifested with. Apart from which it is also important to understand the objectives of the system. (Management Information System 8/E Raymond McLeod, Jr. and George Schell) Decision Support System Objectives: Efficiency of the system. Making decisions. To support managers, not to replace people. Used when the decision is semi structured or unstructured. Incorporate a database. Incorporate models. It is also important that like any other computer based system the DSS should be: Simple Robust Easy to Use Adaptive Easy to communicate with. Now that we have a brief idea about the type of problems that are faced by the managers and the qualities that the DSS system should pertain understanding the decision making process would give an insight to the how a decision is made. Decision Making: (Administrative Behavior, Herbert Simon, 1947) Herbert Simon in 1947 defines decision as the behavioral and cognitive processes of making rational human choices, that is, decisions. It states that any decision making is a behavioral and cognitive process of making choices from a set of options available. So, it is important for the DSS, to be accurate enough for making a choice from many different options available. To make accurate choices from the options available DSS takes help from constrains defined and the goals that it has to achieve. (Administrative Behavior, Herbert Simon, 1947) Simon states in his journal The human being striving for rationality and restricted within the limits of his knowledge has developed some working procedures that partially overcome these difficulties. These procedures consist in assuming that he can isolate from the rest of the world a closed system containing a limited number of variables and a limited range of consequences. By this Simon mean that people with limited knowledge about a particular task or domain will develop some technique that will help the person to overcome these difficulties. This in a sense defines the basic purpose of DSS system to make help managers with making decision. It is also important to understand the term isolated from the rest of the world, by this Simon meant that the decision should be purely be based on the goals to be obtained and based on the criteria defined it should not come under any other influence. He also formulated a model of decision making. (David Arnott, An Analysis of Decision Support Systems Research, p.1) Simons model of decision-making has been used in DSS research since the fields inception and was an integral component of Gorry and Scott Mortons seminal MIS/DSS framework. (Image Taken from Wikipedia, Figure 1) In Simon model of decision making (Figure 1) there are several phases through which an individual goes through to reach his objectives or goal. Phases of Decision Making as per Simon Model are as follows: Intelligence: Identify reality. Get problem/opportunity understanding. Obtain required information. Design: Make decision criteria. Make decision alternatives. Look for related unmanageable events. Identify the links between criteria, alternatives, and events. Choice: Logically assess the decision alternatives. Make recommended actions that best meet the decision criteria. Implementation: Consider the decision  analysis  and assessment. Evaluate the cost of the recommendations. Have confidence in the decision. Make an implementation plan. Secure required supplies. Set implementation plan into act. Based on the Decision making process by Simon and the problem structure described above we can define the accuracy of decisions can be measured by the following criteria: The methods or technique with which it achieves the desired results or goals; and The efficiency with which the goals and sub goals are obtained. By this we mean members of the organization may focus on the method and technique used to reach to the result or goal, but the administrative management must pay attention to the efficiency with which the desired result was obtained. To understand the efficiency of the decision made it is necessary to analysis the decision made. Decision Analysis in itself is a vast field and deals with many methodologies to measure the efficiency of the decision. Decision Analysis: (Ronald Howard, 1965, Decision Analysis: Applied Decision Theory)Decision Analysis is a discipline which was developed to deal with the challenges of making important decisions which involved handling major uncertainty, long-term targets and complex value issues. Decision Analysis comprises the philosophical, theoritical, methodological, and professional practice necessary to formalize the analysis of important decisions. (Ronald Howard, 1965, Decision Analysis: Applied Decision Theory) Decision analysis is a logical procedure for the balancing of the factors that influence a decision. The procedure incorporates uncertainties, values, and preferences in a basic structure that models the decision. Typically, it includes technical, marketing, competitive, and environmental factors. The essence of the procedure is the construction of a structural model of the decision in a form suitable for computation and manipulation; the realization of this model is often a set of computer programs. Decision-making consists of assigning values on the outcomes of interest to the decision-maker. Thus, decision analysis evaluates the decision-makers trade-offs between monetary and non-monetary outcomes and also establishes in quantitative terms his preferences for outcomes that are risky or distributed over time. Ronald A. Howard in his paper Advances: Foundations of DA Revisited goes on to discuss the Pillars of Decision Analysis The First Pillar: Systems Analysis Systems analysis grew out of World War II and was concerned with understanding dynamic systems. Key notions were those of state variables, feedback, stability, and sensitivity analysis. The field of systems engineering is currently in a state of resurgence. Decision analysis and systems engineering have many complementary features (Howard, 1965, 1973). The Second Pillar: Decision Theory Decision theory is concerned primarily with making decisions in the face of uncertainty. Its roots go back to Daniel Bernoulli (Bernoulli, 1738) and Laplace. Bernoulli introduced the idea of logarithmic utility to explain the puzzle called the St. Petersburg paradox. In the most influential book on probability ever written (Laplace, 1812), Laplace discusses the Esperance mathematique and the Esperance morale. Today we would call these the mean and the certain equivalent. The Third Pillar: Epistemic Probability Jaynes taught that there is no such thing as an objective probability: a probability reflects a persons knowledge (or equivalently ignorance) about some uncertain distinction. People think that probabilities can be found in data, but they cannot. Only a person can assign a probability, taking into account any data or other knowledge available. Since there is no such thing as an objective probability, using a term like subjective probability only creates confusion. Probabilities describing uncertainties have no need of adjectives. This understanding goes back to Cox (2001), Jeffreys (1939), Laplace (1996) and maybe Bayes, yet somehow it was an idea that had been lost over time. A famous scientist put it best over 150 years ago: The actual science of logic is conversant at present only with things either certain, impossible, or entirely doubtful, none of which (fortunately) we have to reason on. Therefore the true logic for this world is the calculus of Probabilities, which takes account of the magnitude of the probability which is, or ought to be, in a reasonable mans mind. (Maxwell, 1850) The Fourth Pillar: Cognitive Psychology In the 1960s few appreciated the important role that cognitive psychology would play in understanding human behaviour. At the time of DAADT, we just did our best to help experts assign probabilities. In the 1970s the work of Tversky, Kahneman, and others provided two valuable contributions. First, it showed that people making decisions relying only on their intuition were subject to many errors that they would recognize upon reflecting on what they had done. This emphasized the need for a formal procedure like decision analysis to assist in making important decisions. The second contribution was to show the necessity for those who are assisting in the probability and preference assessments to be aware of the many pitfalls that are characteristic of human thought. Tversky and Kahneman called these heuristics methods of thought that could be useful in general but could trick us in particular settings. We can think of these as the optical illusions of the mind. An important distinction here is that between descriptive and normative decision-making. Descriptive decision-making, as the name implies, is concerned with how people actually make decisions. The test of descriptive decision-making models is whether they actually describe human behaviour. Normative decision-making is decision-making according to certain rules, or norms, that we want to follow in our decision-making processes. The underlying premise of decision analysis is to distinguish between a good decision and a good outcome. A good decision is termed as logical decision which is based on the information, values, and preferences of the decision-maker. A good outcome is one that benefits the end user. The aim is to arrive at good decisions in all situations which would go on to ensure as high a percentage of good outcomes. But at times it may be observed that even a good decision has achieved a good outcome. But for majority of the situations we may face making good decisions is the best way to ensure good outcomes. A decision can be defined as a choice among alternatives that will yield uncertain futures, for which we have preferences. To explain the formal aspects of decision analysis the image of the three-legged stool shown in Figure 3.1 (Howard, 2000). The legs of the stool are the three elements of any decision: what you can do, the alternatives; what you know, the information you have; and what you want, your preferences. Collectively, the three legs represent the decision basis, the specification of the decision. Note that if any leg is missing, there is no decision to be made. If you have only one alternative, then you have no choice in what you do. If you do not have any information linking what you do to what will happen in the future, then all alternatives serve equally well because you do not see how your actions will have any effect. If you have no preferences regarding what will happen as a result of choosing any alternative, then you will be equally happy choosing any one. The seat of the stool is the logic that operates on the decision basis to produce the best alternative. We shall soon be constructing the seat to make sure that it operates correctly. Decision Analysis provides a formal language for communication for the people involved in the decision-making process. During this, the basis for a decision becomes clear, not just the decision itself. The views may differ on whether to adopt an alternative because individuals possess different relevant information or because they may value the consequences differentlly. Decision analysis Cycle: The professional practice of decision analysis is decision engineering. Creating a focused analysis requires the continual elimination of every factor that will not contribute to making the decision. This winnowing has been a feature of decision analysis since the beginning (Howard, 1968, 1970). Since DAADT, the process has been described as a decision analysis cycle, depicted in Figure 3.4 (Howard, 1984a). The application of decision analysis can be modeled in form of an iterative procedure called the Decision Analysis Cycle. Decision Analysis Cycle: The procedure is divided into three phases: Deterministic phase: the variables affecting the decision are defined and relations between the variables established, the values are assigned, and the importance of the variables is measured upto a acceptable level of certainity. Probabilistic phase: the associated probability assignments on values are derived. We also take into account the assessment of risk preference, which identifies the best possible solution in the face of uncertainty. Informational phase: the results of the first two phases are reviewed to determine the economic value of eliminating uncertainty in each of the important variables in the problem.It is the most important phase among the three because it evaluates in monetary terms to have the perfect information. Decision Support System: There is no universally accepted definition for the DSS system as of now. It is the major reason we have to rely on the Characteristics and Objectives of the DSS to understand the system. Below are a few famous definition for the DSS we would refer to formulate a definition for the system. (Decision Support Systems: An Organizational Perspective, Keen Scott-Morton, 1978) Keen and Scott define DSS as Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semi structured problems. If we correlate the definition from Keen and Morton and Simons definition stating The human being striving for rationality and restricted within the limits of his knowledge has developed some working procedures that partially overcome these difficulties. These procedures consist in assuming that he can isolate from the rest of the world a closed system containing a limited number of variables and a limited range of consequences. We understand that the base of the DSS system is to support the manager. But one of the drawbacks of the definition from Keen and Morton is that they state that the system deals with only semi structured problems but the present DSS system also handles Unstructured and Structured issues. Peter Keen in 1980 defined DSS as Personal System to assist Manager must be built from the Managers perspective and must be based on a very detailed understanding of how the manager makes decision and how the manager organization functions. (Donald R. Moscato, 2004, p.1) In the above definition Peter Keen tries to define DSS in terms of the implementation and customization of DSS and states that it should be done based on Managers perspective, styles of decision making and the organizations function. Drawback with this definition is that it defines DSS as a personnel system and with the introduction of Group DSS and Communication DSS the definition becomes obsolete. Bonczek, Holsapple and Whinston (Foundations of Decision Support Systems, Bonczek, Holsapple and Whinston, 1981, p.19) argued the system must possess an interactive query facility, with a query language that is easy to learn and use. The above definition tries to explain that DSS systems should be interactive and should have a language of its own so that constrains of the decision and the goals can be addressed to the system and is easy to understand and use. (We have stated in the section objectives of DSS). (Daniel J Power, 2001, p.1)Sprague and Carlson (1982) define Decision Support Systems broadly as interactive computer based systems that help decision-makers use data and models to solve ill-structured, unstructured or semi-structured problems. Sparague and Carlson explained the DSS system as an interactive system and which can help managers solve ill-structured, unstructured and semi-structured problem. If you observe the definition is a co-relation of definition provided by Peter Keen, Keen Scott-Morton 1978 and Bonczek, Holsapple and Whinston-1981 by removing there drawbacks. A few more definition that we thought explains DSS are as follows: Marakas in 2002 (Marakas, 2002, p.4) stated the following is a formal definition of DSS: A decision support system is a system under the control of one or more decision makers that assists in the activity of decision making by providing an organized set of tools intended to impose structure on portions of the decision-making situation and to improve the ultimate effectiveness of the decision outcome. Importance of Marakas definition is that it takes into consideration the tools that a manager can use to work with DSS system (can term it as third party tools in some cases) other that the query language or the normal interactive screen of the DSS. From the above example it is pretty clear that to define a DSS not only we will have to study the characteristics and the tools, types of DSS but also the framework of the DSS to select a definition or to define one. (Ralph H. Sprague, Hugh J. Watson, Decision Support System Putting Theory into practice, 3rd edition, 1993, p.4) Characteristics of DSS: They tend to be aimed at the less well structured, underspecified problems that upper level managers typically face. They attempt to combine the use of models or analytic techniques with traditional data access and retrieval function They specifically focus on features which make them easy to use by non-computer people in an interactive mode They emphasize flexibility and adaptability to accommodate changes in the environment and the decision making approach of the user. Framework of DSS: From (Daniel J Powers, 2001, p.1) we come to know that the framework for the Decision support system should be based on the following factors: (by this Daniel J Power meant System should be discussed and explained in terms of four descriptors to maintain better communication:) Dominant Technological Component The Targeted Users Purpose Deployment Technology (Daniel J Powers, 2001, p.1) And the Five generic categories of DSS are: Communication Driven Data Driven Document Driven Knowledge Driven Model Driven decision support system. (Daniel J Powers, 2001, p.1) DSS Deployment technology can be: Mainframe Computers A client server LAN Web Based Architecture Marakas (2002) meant that it is important to understand the type of DSS to determine the best design and approach of a new DSS. In 1976 Steven Alter, a doctoral student created a taxonomy of seven DSS types on Gorry and Scott-Morton framework based on a study of 56 DSSs. In 1980, Steven Alter (Daniel J Power, 2001, p.2) proposed his taxonomy of Decision Support Systems. Alters seven category typology is still relevant for discussing some types of DSS, but not for all DSS. Alters idea was that a Decision Support System could be categorized in terms of the generic operations it performs, independent of type of problem, functional area or decision perspective. His seven types included: File Drawer Systems Data Analysis Systems Analysis Information Systems Accounting and Financial models Representational Models Optimization Models Suggestion Models. Alters first three types of DSS have been called data oriented or data driven; the second three types have been called model oriented or model driven; and Alters suggestion DSS type has been called intelligent or knowledge driven DSS. Importance of Alters Study was: Supports concept of Developing Systems that address particular decisions. Makes clear that DSS need not be restricted to a particular Application Type. Based on Alters study Daniel J Power formulated an expanded framework. The purpose of expanded DSS framework is to help people understand and apply the framework to integrate, evaluate, implement and select appropriate means for supporting and informing decision-makers. Expanded Framework suggested by Daniel J Power (Daniel J Power, Expanded DSS framework, June 2001, p.5) Dominant DSS Component Target Users: Internal / External Purpose: General /Specific Deployment Technology Communications Communications- Driven DSS Internal teams, now expanding to external partners Conduct a meeting or Help users collaborate Web or Client/ Server Database Data-Driven DSS Managers, staff, now Suppliers Query a Data Warehouse Main Frame, Client/ Server, Web Document base Document-Driven DSS Internal users, but the user group is expanding Search Web pages or Find documents Web or Client/ Server Knowledge base Knowledge- Driven DSS Internal users, now Customers Management Advice or Choose products Client/Server, Web, Stand-alone PC Models Model-Driven DSS Managers and staff, now customers Crew Scheduling or Decision Analysis Stand-alone PC or Client/Server or Web (Ralph H. Sprague, Hugh J. Watson, Decision Support System Putting Theory into practice, 3rd edition, 1993, p.4) Three Technology Levels: Specific DSS System which actually accomplishes the work might be called the specific DSS. DSS Generator This is a set of related hardware and software which provides a set of capabilities to quickly and easily build a specific DSS. DSS Tool These are hardware or software elements which facilitates the development of a specific DSS or DSS Generator. Based on the details above we would like to define DSS as DSS can be defined as use of computer application that can help managers, staff members, or people who interact within the organization to make decisions and identify problems by using available data and communication technology. It is also very important to understand the reason for the failure of DSS. And what are the factors that could cause the failure of system and which factors are to be termed as the success factors of DSS. Reason for Failure of DSS System: Despite the benefits that DSS offers the implementation of such system has been limited. Some of the reasons can be the following: Proper evaluation of the DSS preceding and during DSS development. DSS output does not fit the producers decision-making style. Complexity involved while operating the DSS. Post Implementation support. Benefits from these systems are not always realized Other than the above reason few disadvantages of the DSS system are: Over dependency for Decision making Assuming it to be correct. Unanticipated effects Deflect personal responsibilities Information overload. Considering the above reason, to increase the rate of success of DSS implementation and customization, the following factors should be considered and managed. Critical Success Factors of DSS: Hartono (Hartono et al, 2006, p.257) uses the following words to describe their interpretation of Critical Success Factors: Success antecedents are those key factors that organizations can manage so that the management information system is favorably received and the implementation is deemed as successful (Johannes Johansson; Bjorn Gustafson, Critical Success Factors affecting Decision Support System Success, from an end-user perspective,2009, p.1)Johannes Johansson and Bjorn Gustafson identified three factors that significantly affect end-users perceived net benefits, namely Data Quality, Problem Match and Support Quality. (S. Newman1, T. Lynch, and A. A. Plummer; Success and failure of decision support systems: Learning as we go, p.1)The case study HotCross, a DSS under development to evaluate crossbreeding systems in northern Australia, provided evidence of a shift in the development process because greater emphasis was put on the learning process of breeding program design by end-users rather than emphasis on learning how to use the DSS itself. Greater end user involvement through participatory learning approaches (action learning, action research, and soft systems methodologies), iterative prototyping (evolving development processes), as well as keeping DSS development manageable and small in scope, will provide avenues for impr

Cellular Manufacturing System

What is Lean? Running an operation lean means: Removal of waste of all kinds (e. g. time, motion, inventory, poor cost of quality, etc. ) An organization that stimulates productivity and quality An organization using value-added processes Low Quality = High Waste High Quality = Low Waste and Higher Value There are several ways to be Lean— TQM Six Sigma SMED (Single Minute Exchange of Die) PM/TPM (Preventive Maintenance/Total Preventive Maintenance) JIT (Just In Time) Continuous Improvement/Kaizen And Many More†¦CELLULAR MANUFACTURING: MANUFACTURING A Lean Manufacturing Concept Cellular Manufacturing One of these lean practices, cellular manufacturing, is based on a group of different processes located in close proximity to manufacture a group of similar products. The primary purpose of cellular manufacturing is to reduce cycle time and inventories to meet market response times. Some of the other benefits include: Space Reduction Quality Improvement Labor Cost Reduction Im proved Machine Utilization Where would you begin? First, you would define the cell you plan to create.Criteria required for defining the cell would be based on: Processes Required Part Numbers & Attributes (size, shape, raw materials required) Market segments/customers Degree of Automation By Jay P. Patel, Principle Consultant, Quality & Productivity Solutions, Inc. Copyright ©2000 by Jay Patel Cellular manufacturing is an application of the group technology concepts for factory reconfiguration and shop floor layout design. A part family can be parts similar in size or parts created using similar manufacturing steps.Typically, a cell is dedicated to a single part family. Cellular manufacturing does have some important human resource issues to consider: Operators must be trained properly to perform tasks including inspection and simple maintenance Cross functional training is critical since operators perform a variety of tasks and move between workstations and cells as the need ari ses Operators should be trained on Team Building Supervisors become Coaches. Cell teams require only guidance. Supervisors facilitate, assist nd guide the overall effort. Compensation issues: Cell employees usually receive the higher pay because they are better qualified to do multiple tasks. We recommend implementation of an incentive program that provides incentives on the basis of results and incremental improvements. Management may be concerned that there will be resistance from employees when the result is actually opposite. Typically, any initial resistance disappears once employees understand the win-win situation at hand.Cells need support from several functions including product engineering, material management, manufacturing engineering, QC/QA, maintenance and management. It is vital to implementation success that this support is committed, visible and consistent. Team Selection Most crucial to the implementation of cellular manufacturing is team selection. To help determi ne who is best to include in your team, identify the skills required for each step by creating a Process Map. Clarify the roles and responsibilities of the roles you need filled.Once the skills are identified, determine the potential team members. Explain to potential members what the nature of the task is and get a feel for their interest. What will you look for in your team members? Understanding the importance of selecting the right team cannot be overemphasized. Both objective and technical criteria should be established, including skills in: teamwork interpersonal skills leadership skills change adaptation positive thinking Develop a checklist for selecting these members based on the needs of the cell.This can also be used in setting goals for those who wish to grow in their own skill sets. 2 From here you will need to do some negotiating. You will need to meet with affected Supervisors to discuss: Overall priority Current structure and plans Current authorities and relative po wer of team leaders Potential team members and the support they will need Negotiate to acquire members best suited for the team. Renegotiate as necessary with the help of management and a detailed implementation plan. Skill Matrices Match skills to tasks.Create a matrix to define who is best qualified to perform specific tasks. This will be the document used for contracting your team to the project: aiding in obtaining commitment as well as communicating responsibilities and expectations. Determine what training is needed and implement a training plan. Make sure communication lines are established, open and well used. The success of the program is contingent on the team dynamic and the goals set. Communication is key—learn to listen and help others to do the same. What are the communication channels and links? Who provides and who expects what?Can the team members accomplish the tasks assigned? Team Management Develop trust and provide the team a reason to be motivated. Learn to manage team morale and create a system to encourage teamwork. Make your team an example of how successful teams cause great things to happen. Start with a pilot cell—be sure the product family chosen has the most potential for success. Stockpile if possible to provide an alternate source during implementation. When changes are necessary, communicate the reason for these changes in order to gain acceptance and understanding from your team.Be confident in implementation. Designing the Cell Cell design should be based on your project needs. Keep in mind the following when designing the cell: product life cycle continuous, flowing work through the cell proper allocation of resources for gauging and tooling aggressive update of equipment and processes cell development is evolutionary and always in a state of flux challenges in facility changes due to structure and age Involve your team and their knowledge in the construction of the cell.Understanding throughput is essential to cell development—do not underestimate the contribution and acceptance of the team once the concept is understood. Ultimately, the logistics and cell construction is only a small part of the battle. Creating a culture where a team can flourish is the hard part. This task requires a true leader. Can you meet the challenge? -JPP 3

Infiniti car

Luxury car is a vehicle that provides luxury where It means by a higher quality equipment with better performance, it's provide: a good quality, comfort, higher design, the technologically Innovative modern, and not only that but the luxury car have a features that convey an image, brand or status. Luxury car is already around the world and in the Australia as well.The sales of the Australia was rise like in one of article in Australia : Sydney Morning Herald that talk about the sales car in Australia was rise and increased by 7 per cent in the first four months in 2003 and he industry Is predicting that a record-beating tally of compared with the same period last year and the Industry Is predicting that a record-beating tally of 850,000 vehicles will be sold In 2003. Then there was four famous luxury car that sold In Australia's market: Holder, Ford, Toyota and Amateurish.Then more than 320 models are sold through 50 brands (Dowling, 2003). So it means that the sales of the luxury c ar in Australia was good, and to make the market more grow that help the Australia economy, that have a new brand car. Infinite is a new car company to the Australian luxury automobile segment. We want to focus the Infinite car to the specific demographic In Sydney. The method we use It by exploratory research. Because It focusing on new ideas and find potential market opportunities. The focus target market is business people that have income more than $ 100. ASK The method of exploratory research are as follows: The primary data: we can collect by questionnaire filled by getting some questionnaire filled by customers and sometimes the exploratory research are usually based on secondary data consists books and website. The marketing concept The concept of the marketing research is related with the whole of the organization would be driven by a constant concern for its customer to search the potential customer and related it with our organization, particular needs of the customer, th en coordinate all of activities that can affect to the customer. Wilson, 2010) For the marketing concept, we can use the concept method by research analysis: 1 . Formulation the problem understand our focus target where the Infinitely Is a new car company that soul not so popular. Not all people know about that yet so we must understand the full impact and implication of the luxury car industry. 2. Collection the data We can do by questionnaire that can be is reliance on verbal questions, written, or oral.For example we can do the questionnaire by taking the collect data with 40 respondents who can filled the questionnaires and give the required necessary Information. The respondents all business people that can be working In the office, own organization, company. 3. Making sampling plan We can describe the character of the group people that we focus on. Where the demographic information is age around more than 25 years old, and can be woman or man. The sample size we can make it 40 where we want to do how many that we deed It, than the area of our sample is Sydney, Australia.Then our sample technique Is a group of subjects (sample) chosen from a large group (a population). 4. Research The research is Sydney The respondent for our information Frequency of usage in the question we ask for highly consumed brand Management problem Infinite needs to discuss the internal problem with their staffs as this will leads to the development of a marketing research question. However, infinite needs to create customer profile, industry profile, understanding of buyer behavior, and to test advertising concept for its next advertising campaign.It's related it with the marketing research question. Once a management problem translated into a research problem, then you can find a solution. In this case, Infinite is a new car company, and not everyone knows about this company. They might experience the problems with their sales revenue, therefore Infinite needs to examine expecta tions and experiences of several groups such as potential customers, first time buyers and repeat purchases.Infinite need to determine lack of sales due to: Poor expectations that lead to lack of desire to buy because customers were not satisfied with the reduce, so they decided to not purchasing it anymore. Poor performance experience ( not meeting expectations and value of money that customers pay for Infinite car) The strategic marketing problem-based data and the testing of alternative solutions with : 1. Identify problem or opportunity The Infinite is a new car company.Not many people know about the car company yet so it will be many threat from other competitors for example that the luxury car from an old company that the brand already famous. We can identify by: Strength : it's a new company so it might be many people want to know about the new car so they ill be try to get the information and search it Weakness : because a new company so there still not many people know abou t the new car Opportunity : new company so it have many potential to reach the market because people like something new.Threats : other competitor or other brand that it's already famous. 2. Determine research design and collection forms Infinite can conduct surveys, by giving questionnaire or conducting focus group for measuring the performance standards and expectations seeks by customers, to increase their sales and customer loyalty. 3. Design sample Because this is new company, therefore Infinite should not only conducting research for the potential customers and target market, but also they need to research about the design that they are going to use or put into the car. . Collect data With secondary data where the information that has been gathered before for some purpose like internal data (available within organization) and external data (outside organization) Primary data frequently collected through questionnaire but also can be gathered through observation and qualitative research. 5. Analyses and interpret data The data of Infinity must be prepared for data analysis using several steps such as collecting, coding and tabulation. 6.Prepared and present research report Research report is actual document submitted to client or management summarizing results and conclusions Quantitative ad qualitative The qualitative data is data that based on quality or qualities data that have a quantitative data is do in small number of participants by focus group or interview. For example : 1. What is your reason to buy the luxury car? 2. How you find the luxury car information? 3. What are you looking when you decided to buy the luxury cat that you want?The quantitative data is prefer to data that concerned with quantifying where it means that measuring and counting. Which we can subscribes to a particular empirical approach to knowledge, believing that measuring accurately enough we can make claims to the object study (Alex, 2013). The quantitative data we can do by questionnaire. For example : 1 . What age that more like to buy the luxury car ? Age No of respondents 25 – 35 20 36 – 45 11 46 – 55 5 56 and more 4 Total We can see that the respondent is more in the age around 25 – 35 for buying luxury car. 2. The preference to buy a luxury car Answers PriceQuality 15 Availability 6 Involvement Recommendation 3 Most of the respondent is more prefer the quality to buy the luxury car 3. Why people want to buy a luxury car Value 8 Flaunt Value Interested in Art 7 Exclusivity 10 The respondents prefer to buy luxury car because they want to flaunt their superiority over others. 4. What is the type of Jobs that most people buy the luxury car ? Type of Jobs Businessman Office work Government employees 12 Most of the people buy the luxury car is people are working in type of Jobs is businessman. 5. What is the source of information to get to know the luxury cars?Sources No of responders Television Newspaper Website Product d emos We can see most of the respondent are like to use the source by television to get interest with the luxury car that make them want to get the information about the car. Marketing research with the management decisions The management decisions are related it with the marketing research that we do. So first we must know what the problem when we want to make one decisions on our ask how to advertised the product Should it advertised by website, magazine or other source for advertising How was the cost and the budget for our new product companyWhat price we should decided that with our product and in customer side So the research would feed the information back to management so the management can work it out if the potential take up and the price would ensure profitability. (Sara, 2011) Reference : Alex. (2013). Types of Research : Quantitative vs. Qualitative. Wordless . Dowling, J. (2003). Luxury car sales rocketing; there must be a war on. Sydney Morning Herald . Sara. (2011). D ifferences between management decisions problem and marketing research problem? . Business and finance . Wilson, A. M. (2010). Marketing research : an integrated approach. Australia: Pearson Australia.

Gender And Its Impact On Gender Essay - 2026 Words

The idea of gender is most commonly known as a very simple thing: either male or female. While today we know that is not strictly the case, and that gender is a more fluid thing, that it is more of a spectrum, that was not always a societal acceptance. Even today there is a hesitancy to accept that gender is not as black and white as people think, so the topic of gender in the time of Shakespeare well, one would expect it to be even stricter than the opposition faced today in terms of whether or not gender is more than just male or female. But when looking throughout his works, it can be found that that is not the case. Shakespeare’s characters and stories are of a varying nature, and he plays with the notion of gender binaries and the two-gender paradigm. He can be ambiguous, he can flip traditionally expected roles, he can challenge the notion of gender altogether, it just depends on which of his works is being read. Shakespeare’s approach to gender and its significan ce, is that of a challenging one. He challenges not only the physical presentation of gender, but also the traditional roles associated with it. He also calls attention to the issue with simply taking gender at face value, at viewing it as a simplistic, shallow concept. Shakespeare showcases the fluidity, and in a way the insignificance, of gender within many of his plays, but this paper will be specifically looking at the two-gender paradigm (not the complete gender spectrum) within Macbeth and As You Like ItShow MoreRelatedGender And Gender Impact On Gender1836 Words   |  8 PagesGender is important to Western society, and fittingly, it is deeply woven into the infrastructure of the United States. Whether it comes from the media, school, work, or church, people are fed innumerable amounts of messages everyday instructing them how he or she should look and behave based on their sex, and there is no escaping it. 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