Demand mgt in scm


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Demand mgt in scm

  1. 1. Demand Management in SCM<br />Supply Chain Management<br />Presented by <br /> Radio Heads<br />Presented to: Mr. Abdullah Al-Amin<br />
  2. 2. Introduction<br />focused efforts to estimate and manage customers’ demand<br /> with the intention of using this information to shape operating decisions<br />
  3. 3. Objectives<br />Gathering and analyzing information about consumers, their problems, and their unmet needs<br />Identifying partners to perform the functions needed in the demand chain<br />Moving the functions that need to be done to the channel member that can perform them most effectively and efficiently<br />
  4. 4. Objectives<br />Sharing with other supply chain members information about consumers and customers, available technology, and logistics challenges and opportunities<br />Developing products and services that solve customer’s problems<br />Developing and executing the best logistics methods to deliver products and services to consumers in the desired format<br />
  5. 5. Types of Demand Management<br />Professor John Mentzer and Mark Moon introduced different types of demand<br />Independent demand: Independent demand is the amount of product demanded (by time and location) by end-use customers of the supply chain.<br />Dependent or derived demand: An item whose demand is tied directly to the demand or production level of another item. <br />
  6. 6. Independent Demand VS Dependent Demand<br />Independent demand items are generally finished goods while dependent demand items are generally components or sub assemblies.<br />Independent demand items are forecasted while dependent demand item requirements can be derived based on demand for finished goods.<br />Example:<br /> You would forecast demand for refrigerators but you would calculate how many crisper drawers are needed (2 drawers per fridge, 10 fridges next week therefore need 200 crisper drawers.)<br />
  7. 7. What is Forecasting?<br />Forecasting is the art and science of predicting future events<br />Historical data<br />Future projection<br />Mathematical model<br />It may be subjective or intuitive prediction<br />Combination-mathematical model and manager’s judgment.<br />
  8. 8. Forecasting Time Horizons<br />Classified on the basis of future time horizon<br />Short-range forecast [less than one year]<br /> Example: Planning purchasing, job scheduling, workforce levels, job assignments and production levels<br />Medium-range forecast [one to three years]<br /> Example: Sales planning, production planning and budgeting, cash budgeting and analyze operating plans<br />Long-range forecast [more than three years]<br /> Example: Planning for new products, capital expenditures, facility location or expansion and R&D<br />
  9. 9. Types of Forecast<br />Economic Forecast : address the business cycle by predicting;<br />Inflation rates<br />Money supply<br />Housing starts<br />Other planning indicators<br />
  10. 10. Types of Forecast<br />Technological forecast: concerned with rates of technological progress which results in;<br />Birth of new exciting products<br />Requiring new plants and equipment<br />
  11. 11. Types of Forecast<br />Demand forecast: Projects demand for a company's products or services<br />Sales forecast<br />Production, capacity ad scheduling systems<br />Serve as inputs to financial, marketing and personnel planning<br />
  12. 12. Strategic Importance of Forecasting<br />Human Resource<br />Hiring, training and laying off workers<br />Hiring of additional workers affects training and quality<br />Capacity<br />Inadequate capacity results <br />Undependable delivery<br />Loss of customers<br />Loss of market share<br />
  13. 13. Strategic Importance of Forecasting<br />Supply-Chain Management<br />Good relations with suppliers<br />Ensuing price advantages for materials and parts<br />
  14. 14. Seven Steps in the Forecasting System<br />Determine the use of forecast<br />Select the items to be forecasted<br />Determine the time horizon of the forecast<br />Select the forecasting models<br />Gather the data needed to make the forecast<br />Make the forecast<br />Validate and implement the results <br />
  15. 15. Forecasting Approaches<br />Qualitative analysis<br />Jury of Executive opinion<br />Delphi Method<br />Sales Force Composite<br />Consumer Market Survey<br />Quantitative analysis<br />Time-series Models<br />Naïve Approach<br />Moving Average<br />Exponential Smoothing<br />Trend Projection<br />Associative Models<br />Regression Analysis<br />
  16. 16. Qualitative Methods<br />Jury of Executive opinion<br />Group of high-level experts or managers<br />In combination with statistical models<br />Arrive at a group estimate of demand<br />Delphi Method<br />3 different participants<br />Decision makers: group of 5-10 experts<br />Staff personnel: assist decision makers by preparing, distributing, collecting and summarizing questionnaires and survey results<br />Respondents: provide inputs to the decision makers before forecasting<br />
  17. 17. Qualitative Methods<br />Sales Force Composite<br />Estimation of each salesperson<br />Revision to ensure<br />Combined at district and national levels<br />To reach an overall forecast<br />
  18. 18. Qualitative Methods<br />Consumer Market Survey<br />Input from customers or potential customers<br />In regards of their future purchasing plans<br />Helpful in preparation of forecast<br />Improving product design and planning for new products<br />Overly optimistic forecast arising from customer input<br />
  19. 19. Quantitative Methods<br />Time-series Models: <br />Prediction on the assumption that the future is a function of the past<br />It includes:<br />Naïve approach<br />Moving averages<br />Exponential smoothing <br />Trend projection<br />
  20. 20. Decomposition of a Time Series<br />Analysis of time series requires:<br />Breaking down past data into components<br />And then projecting them forward<br />A time series has four components:<br />Trend<br />Seasonality<br />Cycles<br />Random variations<br />
  21. 21. Decomposition of a Time Series<br />Trend: Gradual upward and downward movement of the data over time<br />Seasonality: Data pattern that repeats itself after a period of days, weeks, months, or quarters<br />Cycles: Patterns in data that occur every several years<br />Random variations: Blips in data caused by chance and unusual situations<br />
  22. 22. Quantitative Methods<br />Naïve Approach<br />Assumption that the demand in the next period will be equal to demand in the most recent period<br />Moving Averages<br />Use a number of historical actual data values to generate a forecast<br />Useful if we can assume that market demands will stay fairly steady over time<br />Moving average = ∑ Demand in previous n periods <br />n<br />
  23. 23. Quantitative Methods<br />Exponential Smoothing<br />Involves very little record keeping of past data<br />New forecast = Last period’s forecast + α (last period’s actual demand-last period’s forecast)<br />Trend Projections<br />Fits a trend line to a series of historical data point<br />Projects the line into the future for forecasts<br />
  24. 24. Quantitative Methods<br />Associative Models:<br />Incorporate the variables or factors that might influence the quantity being forecasted.<br />Linear Regression Analysis<br />A straight line mathematical model<br />Describes the functional relationship between independent and dependent variables<br />
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