Planning Solution Enables PepsiCo to Optimize Manufacturing and Distribution against Seasonal Sales Demand PEPSICO BUSINESS NEEDS AND CHALLENGES PepsiCo approached PCA‚ seeking improvements to how they managed their supply-chain planning and forecasting operations — their ability to optimize manufacturing‚ distribution and warehousing of hundreds of different beverage products and snack foods against seasonal sales projections across European‚ Middle East and Asian continents. Under-production meant lost
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POS systems with the latest forecasting trends. Some tactics they have implemented include an extensive Point-of-sale system (POS)‚ which captures transaction data on nearly every person who walks through a cafe’s door. The sale of each entrée represents one customer. They forecast monthly guest counts‚ retail sales‚ banquet sales‚ and concert sales (if applicable) at each café. In order to evaluate management‚ a 3-year weighted moving average is applied to cafe sales. As the text described‚ the
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Quantitative Methods ADMS 3330 3 0 3330.3.0 Forecasting QMB Chapter 6 © M.Rochon 2013 Quantitative Approaches to Forecasting Are based on analysis of historical data concerning one or more time series. Time series - a set of observations measured at successive points in time‚ or over successive periods of time. If the historical data: • are restricted to past values of the series we are trying to forecast‚ it is a time series method. 1 Components of a Time Series 1)
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Demand forecasting is one of the most important tools of production and operation management of a company. 1. The objective of demand forecasting is to forecast the sales of the company in future and it helps the company in budgeting it’s sales and to determine the resources which the company will require to fulfill that demand. 2. Forecasting demand method can also help the companies to avoid oversupply and undersupply of the products 3. This also helps the company in inventory management and lowers
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1. Inventory decisions at L. L Bean use statistical processes on the frozen forecasts provided by the product managers. L. L Bean uses past forecast errors as a basis of measurement for future forecast errors. The decision for stock involves two processes. Firstly‚ the historical forecast errors are computed. This involves taking the ratio of actual demand to forecast demand. The frequency distribution of historical errors is then compiled across items‚ for new and never out items separately‚ to
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DEVELOPMENT OF A WEB-BASED SALES AND INVENTORY SYSTEM (SALES MODULE) OF H & N FUELS‚ ISABEL‚ LEYTE ROSALYN LONDRES BACALE ------------------------------------------------- ------------------------------------------------- 1/ A Software project manuscript presented as a partial fulfillment of the requirements for graduation with the degree of Bachelor of Science in Information Technology from the Visayas State University - Isabel‚ Leyte. It is prepared at the Department of Engineering
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Although demand forecasting is usually the responsibility of the sales and/or marketing functions‚ it is a very important input into the capacity planning and control decision‚ and so is of interest to operations managers. After all‚ without an estimate of future demand it is not possible to plan effectively for future events‚ only to react to them. It is therefore important to understand the basis and rationale for these demand forecasts. As far as capacity planning and control is concerned‚ there
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Chapter FORECASTING Discussion Questions 1. Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When quantitative data are difficult to obtain‚ qualitative models may be appropriate. 2. Approaches are qualitative and quantitative. Qualitative is relatively subjective; quantitative uses numeric models. 3. Short-range (under 3 months)‚ medium-range (3 months to 3 years)‚ and long-range (over
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The Policy Process Part II Lenue Richardson HCS/455 March 14‚ 2013 University of Phoenix The Policy Process Part II Introduction The development of policy is not something that can be done in an efficient manner. However; there are times when policies are very burdensome and can be a very big challenge‚ one that is loaded with all sorts of committees and everything else‚ it is truly an experience. Although the creating of a policy is a very different experience it is necessary
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1) Raw data‚ not seasonalized 2) Seasonal Adjustment used: Census II X-12 multiplicative (MASA): Used because of the presence of seasonal variations that are increasing with the level of my series. Increasing degree of variability overtime… TX non seasonalized and seasonalized 3) Combined seasonally adjusted with non-seasonally adjusted De-seasonalizing the data helped with the removal of seasonal component that creates higher volatility in model. Now‚ variations
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