7 Demand Forecasting in a Supply Chain PowerPoint presentation to accompany Chopra and Meindl Supply Chain Management, 6e Copyright © 2016 Pearson Education, Inc. 7–1 Learning Objectives 1. Understand the role of forecasting for both an enterprise and a supply chain. 2. Identify the components of a demand forecast. 3. Forecast demand in a supply chain given historical demand data using time-series methodologies. 4. Analyze demand forecasts to estimate forecast error. Copyright © 2016 Pearson Education, Inc. 7–2 Role of Forecasting in a Supply Chain • Forecasting is the basis for all planning decisions • in a supply chain Used for both push and pull processes – Production scheduling, procurement, inventory, aggregate planning – Sales force allocation, promotions, new production introduction – Plant/equipment investment, budgetary planning – Workforce planning, hiring, layoffs • All of these decisions are interrelated Copyright © 2016 Pearson Education, Inc. 7–3 Characteristics of Forecasts 1. Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error 2. Long-term forecasts are usually less accurate than shortterm forecasts 3. Aggregate (looking at the combined whole) forecasts are usually more accurate than disaggregate (specific SKU;s/product lines) forecasts 4. In general, the farther up (back) the supply chain a company is, the greater is the distortion of information it receives – Material suppliers are up stream and can have highly distorted forecasts Copyright © 2016 Pearson Education, Inc. 7–4 Components and Methods • Companies must identify the factors that influence future demand, and then ascertain the relationship between these factors and future demand – Past demand (can be a starting point) – Lead time of product replenishment (the longer the lead time, the less accurate the forecast) – Planned advertising or marketing efforts (+/-) – Planned price discounts (+/-) – State of the economy (+/-) – Actions that competitors have taken (+/-) Copyright © 2016 Pearson Education, Inc. 7–5 Components and Methods 1. Qualitative (vs quantitative) – Primarily subjective (observations, expectations, trends) – Based on market knowledge and judgment 2. Time Series Methods – Use historical demand only (can be quantitative) – Best with stable demand 3. Causal – Affected by a relationship between demand and some other factor 4. Simulation – Imitate consumer choices that give rise to demand Copyright © 2016 Pearson Education, Inc. 7–6 Components of an Observation Observed demand (O) = Systematic component (S) + Random component (R) • Systematic component – expected value of demand • • − Level (baseline; current de-seasonalized demand) − Trend (growth or decline in demand over time) − Seasonality (predictable seasonal fluctuation) Random component – part of forecast that deviates from systematic part Forecast error – difference between forecast and actual demand Copyright © 2016 Pearson Education, Inc. 7–7 Basic Approach 1. Understand the objective of forecasting. 2. Integrate demand planning and forecasting throughout the supply chain. 3. Identify the major factors that influence the demand forecast. 4. Forecast at the appropriate level of aggregation – What product lines are best to group together, or segregate?..... 5. Establish performance and error measures for the forecast. Copyright © 2016 Pearson Education, Inc. 7–8 Adaptive Forecasting • The estimates of level, trend, and • seasonality are updated after each demand observation (actual demand) Estimates incorporate all of the new data that are observed – The most recent actual demand results are plugged in to the forecast model, thus updating future forecasts with the most recent data. Copyright © 2016 Pearson Education, Inc. 7–9 Forecasting In Practice • Forecasting is fluid – Results from each new period should be used to determine forecast error, then reevaluate estimates for subsequent periods • Collaborate in building forecasts (CPFR) • Share only the data that truly provides • value Be sure to distinguish between demand (wants) and sales (needs, sales actualized) Copyright © 2016 Pearson Education, Inc. 7 – 10 Moving Average Based on Most Recent Periods • • • • Used when demand has no observable trend or seasonality Systematic component of demand = level The level in period t is the average demand over the last N periods Lt = (Dt + Dt-1 + … + Dt–N+1) / N Ft+1 = Lt and Ft+n = Lt After observing the demand for period t + 1, revise the estimates Lt+1 = (Dt+1 + Dt + … + Dt-N+2) / N, Ft+2 = Lt+1 Once actuals of a new period are posted, it replaces the oldest in the series, and so on. Copyright © 2016 Pearson Education, Inc. 7 – 11 Moving Average Example • A supermarket has experienced weekly demand of milk of D1 = 120, D2 = 127, D3 = 114, and D4 = 122 gallons over the past four weeks – Forecast demand for Period 5 (D5) using a four-period moving average – What is the forecast error if demand in Period 5 turns out to be 125 gallons? Copyright © 2016 Pearson Education, Inc. 7 – 12 Moving Average Example L4 = (D4 + D3 + D2 + D1)/4 = (122 + 114 + 127 + 120)/4 = 120.75 • Forecast demand for Period 5 F5 = L4 = 120.75 gallons • Error if demand in Period 5 = 125 gallons E5 = F5 – D5 = 120.75 – 125 = – 4.25 • Revised demand L5 = (D5 + D4 + D3 + D2)/4 = (125 + 122 + 114 + 127)/4 = 122 Copyright © 2016 Pearson Education, Inc. 7 – 13 Moving Average Example Using Excel Copyright © 2016 Pearson Education, Inc. 7 – 14 Tahoe Salt Year Quarter Period, t Demand, Dt 1 2 1 8,000 1 3 2 13,000 1 4 3 23,000 2 1 4 34,000 2 2 5 10,000 2 3 6 18,000 2 4 7 23,000 3 1 8 38,000 3 2 9 12,000 3 3 10 13,000 3 4 11 32,000 4 1 12 41,000 TABLE 7-1 Copyright © 2016 Pearson Education, Inc. 7 – 15 Tahoe Salt, Actual by Quarter Based on Year, and Quarter…. Copyright © 2016 Pearson Education, Inc. 7 – 16 Tahoe Salt, Deseasonalized A linear relationship exists between the deseasonalized demand and time based on the change in demand over time Dt = L + Tt Copyright © 2016 Pearson Education, Inc. 7 – 17 Using Excel for Linear Regression Trend Line and Determining “Fit” for Forecasting • Select "Data" from the toolbar. The "Data" menu displays. • Select "Data Analysis". The Data Analysis - Analysis Tools dialog box displays. • From the menu, select "Regression" and click "OK". • In the Regression dialog box, click the "Input Y Range" box and select the dependent variable data (usually the data). • Click the "Input X Range" box and select the independent variable data (usually the time period). • Click "OK" to run the results. Copyright © 2016 Pearson Education, Inc. 7 – 18 Simple Exponential Smoothing • Used when demand has no observable trend or seasonality Systematic component of demand = level • Initial estimate of level, L0, assumed to be the average of all historical data Copyright © 2016 Pearson Education, Inc. 7 – 19 Simple Exponential Smoothing • Supermarket data 4 L0 = å Di / 4 = 120.75 i=1 F1 = L0 = 120.75 E1 = F1 – D1 = 120.75 –120 = 0.75 L1 = a D1 + (1– a )L0 = 0.1´120 + 0.9 ´120.75 = 120.68 Copyright © 2016 Pearson Education, Inc. 7 – 20 The Role of IT in Forecasting • Forecasting modules are available in in many ERP’s on the market today • They can be used to best determine forecasting • • • methods for the firm and by product categories and markets These forecasts can assist operations and supply chain functions translate the forecasted sales volume into an operational plan (S&OP’s) Real time updates help firms respond quickly to changes in marketplace They help facilitate demand planning Copyright © 2016 Pearson Education, Inc. 7 – 21 Summary of Learning Objectives 1. Understand the role of forecasting for both an enterprise and a supply chain 2. Identify the components of a demand forecast 3. Forecast demand in a supply chain given historical demand data using time-series methodologies 4. Analyze demand forecasts to estimate forecast error Copyright © 2016 Pearson Education, Inc. 7 – 22 END Copyright © 2016 Pearson Education, Inc. 7 – 23
