Global Supply Chain Management Simulation Debrief Slides ©© Enspire Enspire Learning Learning and and Harvard Harvard Business Business School School (revised Dec 2010) 1 Board Members’ Objectives Member Objective Betty Forecasting: choice of options (consensus vs. mean) Doug Forecasting: choice of options (role of risk) Yvonne Stocking Levels: Weighing the costs of over/understocking Meryl Production flexibility: accurate response/ sourcing strategy (focus on flexibility) Paul Production
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A PROJECT REPORT ON DEMAND FORECASTING OF RETAIL SUPPLY CHAIN MANAGEMENT USING STATISTICAL ANALYSIS By AVINASH KUMAR SONEE 2005B3A8582G KRISHNA MOHAN YEGAREDDY 2006B3PS704P AT HETERO MED SOLUTIONS LIMITED Madhuranagar‚ Hyderabad A Practice School–II station of [pic] BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE‚ PILANI DECEMBER‚ 2009 A PROJECT REPORT On DEMAND FORECASTING OF RETAIL SUPPLY CHAIN MANAGEMENT USING STATISTICAL ANALYSIS by AVINASH KUMAR SONEE - (M
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1. Introduction In order to reduce electric energy consumption‚ the use of CFLs‚ also known as energy saving lamps‚ is being strongly recommended. Utilities have also expressed concern since they have been actively recommending their use in their demand-side management (DSM) programs through giveaways and rebates. CFLs are non-linear consumers. Non-linear consumers produce harmonic currents which flow through the electric power network. They cancel out in generators and other consumers. The results
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Chapter 7 FORECASTING QUESTIONS & ANSWERS Q7.1 Accurate company sales and profit forecasting requires careful consideration of firm-specific and broader influences. Discuss some of the microeconomic and macroeconomic factors a firm must consider in its own sales and profit forecasting. Q7.1 ANSWER The better a company can assess future demand‚ the better it can plan its resources. Every corporation is exposed to three types of factors influencing demand: company‚ competitive and
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Predictive-Corrective Incompressible SPH B. Solenthaler ∗ University of Zurich R. Pajarola † University of Zurich Figure 1: Three examples produced with our incompressible simulation: (Left) 2M particles splashing against the simulation boundaries. (Center) Close-up view of a wave tank. (Right) A fluid represented by 700k particles colliding with cylinder obstacles. Abstract We present a novel‚ incompressible fluid simulation method based on the Lagrangian Smoothed Particle Hydrodynamics (SPH)
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DEMAND FORECASTING The Context of Demand Forecasting The Importance of Demand Forecasting Forecasting product demand is crucial to any supplier‚ manufacturer‚ or retailer. Forecasts of future demand will determine the quantities that should be purchased‚ produced‚ and shipped. Demand forecasts are necessary since the basic operations process‚ moving from the suppliers’ raw materials to finished goods in the customers’ hands‚ takes time. Most firms cannot simply wait for demand to emerge and then
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QUALITATIVE FORECASTING METHODS Qualitative forecasting methods are based on educated opinions of appropriate persons 1. Delphi method: forecast is developed by a panel of experts who anonymously answer a series of questions; responses are fed back to panel members who then may change their original responses a- very time consuming and expensive b- new groupware makes this process much more feasible 2. Market research: panels‚ questionnaires‚ test markets‚ surveys‚ etc. 3. Product life-cycle
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Simulation software Simulation software is based on the process of modeling a real phenomenon with a set of mathematical formulas. It is‚ essentially‚ a program that allows the user to observe an operation through simulation without actually performing that operation. Simulation software is used widely to design equipment so that the final product will be as close to design specs as possible without expensive in process modification. Simulation software with real-time response is often used in gaming
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or organization. It entails the design‚ operation‚ control‚ and updating of systems responsible for the productive use of human resources‚ equipment‚ and facilities in the development of a product or a service (Chase‚ Aquilano‚ Jacobs‚ 2001). Philosophically‚ therefore‚ OM is managerially and activity oriented while OR is mainly technique and mathematically oriented involving modeling a situation or a problem and finding an optimal solution for it (Anderson‚ Sweeney‚ Williams‚ 2002). Decision support
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Traffic Modeling Traffic modeling in a sense is an overview of general traffic flow calculations. It provides a blueprint and a layout of incoming and outgoing traffic with a formula to calculate the timing of overall cars involved within the traffic flow. With the vast roads and streets managing traffic can be difficult without the proper calculations. Mathematical functions can be ways to express simplicity with the eliminations of difficult equations through the use of practical formulas.
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