This document discusses demand management in supply chain management. It covers objectives of demand management like gathering consumer information and identifying partners. It defines types of demand like independent and dependent demand. It also covers forecasting techniques like qualitative methods like jury of executive opinion and quantitative methods like time series models, exponential smoothing, and regression analysis. The document emphasizes the strategic importance of forecasting for human resources, supply chain management, and capacity planning.

1. Demand Management in SCM Supply Chain Management Presented by Radio Heads Presented to: Mr. Abdullah Al-Amin

2. Introduction focused efforts to estimate and manage customers’ demand with the intention of using this information to shape operating decisions

3. Objectives Gathering and analyzing information about consumers, their problems, and their unmet needs Identifying partners to perform the functions needed in the demand chain Moving the functions that need to be done to the channel member that can perform them most effectively and efficiently

4. Objectives Sharing with other supply chain members information about consumers and customers, available technology, and logistics challenges and opportunities Developing products and services that solve customer’s problems Developing and executing the best logistics methods to deliver products and services to consumers in the desired format

5. Types of Demand Management Professor John Mentzer and Mark Moon introduced different types of demand Independent demand: Independent demand is the amount of product demanded (by time and location) by end-use customers of the supply chain. Dependent or derived demand: An item whose demand is tied directly to the demand or production level of another item.

6. Independent Demand VS Dependent Demand Independent demand items are generally finished goods while dependent demand items are generally components or sub assemblies. Independent demand items are forecasted while dependent demand item requirements can be derived based on demand for finished goods. Example: 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.)

7. What is Forecasting? Forecasting is the art and science of predicting future events Historical data Future projection Mathematical model It may be subjective or intuitive prediction Combination-mathematical model and manager’s judgment.

8. Forecasting Time Horizons Classified on the basis of future time horizon Short-range forecast [less than one year] Example: Planning purchasing, job scheduling, workforce levels, job assignments and production levels Medium-range forecast [one to three years] Example: Sales planning, production planning and budgeting, cash budgeting and analyze operating plans Long-range forecast [more than three years] Example: Planning for new products, capital expenditures, facility location or expansion and R&D

9. Types of Forecast Economic Forecast : address the business cycle by predicting; Inflation rates Money supply Housing starts Other planning indicators

10. Types of Forecast Technological forecast: concerned with rates of technological progress which results in; Birth of new exciting products Requiring new plants and equipment

11. Types of Forecast Demand forecast: Projects demand for a company's products or services Sales forecast Production, capacity ad scheduling systems Serve as inputs to financial, marketing and personnel planning

12. Strategic Importance of Forecasting Human Resource Hiring, training and laying off workers Hiring of additional workers affects training and quality Capacity Inadequate capacity results Undependable delivery Loss of customers Loss of market share

13. Strategic Importance of Forecasting Supply-Chain Management Good relations with suppliers Ensuing price advantages for materials and parts

14. Seven Steps in the Forecasting System Determine the use of forecast Select the items to be forecasted Determine the time horizon of the forecast Select the forecasting models Gather the data needed to make the forecast Make the forecast Validate and implement the results

15. Forecasting Approaches Qualitative analysis Jury of Executive opinion Delphi Method Sales Force Composite Consumer Market Survey Quantitative analysis Time-series Models Naïve Approach Moving Average Exponential Smoothing Trend Projection Associative Models Regression Analysis

16. Qualitative Methods Jury of Executive opinion Group of high-level experts or managers In combination with statistical models Arrive at a group estimate of demand Delphi Method 3 different participants Decision makers: group of 5-10 experts Staff personnel: assist decision makers by preparing, distributing, collecting and summarizing questionnaires and survey results Respondents: provide inputs to the decision makers before forecasting

17. Qualitative Methods Sales Force Composite Estimation of each salesperson Revision to ensure Combined at district and national levels To reach an overall forecast

18. Qualitative Methods Consumer Market Survey Input from customers or potential customers In regards of their future purchasing plans Helpful in preparation of forecast Improving product design and planning for new products Overly optimistic forecast arising from customer input

19. Quantitative Methods Time-series Models: Prediction on the assumption that the future is a function of the past It includes: Naïve approach Moving averages Exponential smoothing Trend projection

20. Decomposition of a Time Series Analysis of time series requires: Breaking down past data into components And then projecting them forward A time series has four components: Trend Seasonality Cycles Random variations

21. Decomposition of a Time Series Trend: Gradual upward and downward movement of the data over time Seasonality: Data pattern that repeats itself after a period of days, weeks, months, or quarters Cycles: Patterns in data that occur every several years Random variations: Blips in data caused by chance and unusual situations

22. Quantitative Methods Naïve Approach Assumption that the demand in the next period will be equal to demand in the most recent period Moving Averages Use a number of historical actual data values to generate a forecast Useful if we can assume that market demands will stay fairly steady over time Moving average = ∑ Demand in previous n periods n

23. Quantitative Methods Exponential Smoothing Involves very little record keeping of past data New forecast = Last period’s forecast + α (last period’s actual demand-last period’s forecast) Trend Projections Fits a trend line to a series of historical data point Projects the line into the future for forecasts

24. Quantitative Methods Associative Models: Incorporate the variables or factors that might influence the quantity being forecasted. Linear Regression Analysis A straight line mathematical model Describes the functional relationship between independent and dependent variables