Chapter COLLABORATIVE PLANNING, FORECASTING, & REPLENISHMENT LEARNING OBJECTIVES You should be able to: • Explain the role of demand forecasting in a supply chain • Identify the components of a forecast • Compare & contrast qualitative & quantitative forecasting techniques • Assess the accuracy of forecasts • Explain collaborative planning, forecasting, & replenishment MBA Nguyen Phi Hoang©2015_SCM CHAPTER OUTLINE • Introduction • Demand Forecasting • Forecasting Techniques • • Qualitative Methods Quantitative Methods • • • • • Components of Time Series Data Time Series Forecasting Methods Forecast Accuracy Useful Forecasting Websites Collaborative Planning, Forecasting, & Replenishment (CPFR) • Software Solutions MBA Nguyen Phi Hoang©2015_SCM Introduction • Supply chain members find it important to manage demand, especially in pull manufacturing environments • Suppliers must find ways to better match supply & demand to achieve optimal levels of cost, quality & customer service to enable them to compete with other supply chains • Improved forecasts benefit all trading partners in the supply chain & mitigates/decrease supply-demand mismatch problems MBA Nguyen Phi Hoang©2015_SCM Demand Forecasting A forecast is an estimate of future demand & provides the basis for planning decisions The goal is to minimize forecast error The factors that influence demand must be considered when forecasting Managing demand requires timely & accurate forecasts Good forecasting provides reduced inventories, costs, & stockouts, & improved production plans & customer service MBA Nguyen Phi Hoang©2015_SCM Forecasting Techniques Qualitative forecasting is based on opinion & intuition Quantitative forecasting uses mathematical models & historical data to make forecasts Time series models are the most frequently used among all the forecasting models MBA Nguyen Phi Hoang©2015_SCM Forecasting Techniques (Continued) Qualitative Forecasting Methods Generally used when data are limited, unavailable, or not currently relevant Forecast depends on skill & experience of forecaster(s) & available information Four qualitative models used are – Jury of executive opinion Delphi method Sales force composite Consumer survey MBA Nguyen Phi Hoang©2015_SCM Forecasting Techniques (Continued) Quantitative Methods Time series forecasting – based on the assumption that the future is an extension of the past Historical data is used to predict future demand Cause & Effect forecasting – assumes that one or more factors (independent variables) predict future demand It is generally recommended to use a combination of quantitative & qualitative techniques MBA Nguyen Phi Hoang©2015_SCM Forecasting Techniques (Continued) Components of Time Series Data should be plotted to detect for the following components – Trend variations: increasing or decreasing Cyclical variations: wavelike movements that are longer than a year (e.g., business cycle) Seasonal variations: show peaks & valleys that repeat over a consistent interval such as hours, days, weeks, months, seasons, or years Random variations: due to unexpected or unpredictable events MBA Nguyen Phi Hoang©2015_SCM Forecasting Techniques (Continued) Time Series Forecasting Models Naïve Forecast – the estimate of the next period is equal to the demand in the past period Ft+1 = At Where MBA Nguyen Phi Hoang©2015_SCM Ft+1 = forecast for period t+1 At = actual demand for period t 10 Forecast Accuracy The formula for forecast error, defined as the difference between actual quantity & the forecast – Forecast error, et = At - Ft Where et = forecast error for Period t At = actual demand for Period t Ft = forecast for Period t MBA Nguyen Phi Hoang©2015_SCM 17 Forecast Accuracy (Continued) Several measures of forecasting accuracy follow – Mean absolute deviation (MAD)- a MAD of indicates the forecast exactly predicted demand Mean absolute percentage error (MAPE)- provides a perspective of the true magnitude of the forecast error Mean squared error (MSE)- analogous to variance, large forecast errors are heavily penalized MBA Nguyen Phi Hoang©2015_SCM 18 Forecast Accuracy (Continued) Mean absolute deviation (MAD)MAD of indicates the forecast exactly predicted demand Where MBA Nguyen Phi Hoang©2015_SCM et = forecast error for period t At = actual demand for period t n = number of periods of evaluation 19 Forecast Accuracy (Continued) Mean absolute percentage error (MAPE) – provides a perspective of the true magnitude of the forecast error Where MBA Nguyen Phi Hoang©2015_SCM et = forecast error for period t At = actual demand for period t n = number of periods of evaluation 20 Forecast Accuracy (Continued) Mean squared error (MSE) – analogous to variance, large forecast errors are heavily penalized Where MBA Nguyen Phi Hoang©2015_SCM et = forecast error for period t n = number of periods of evaluation 21 Forecast Accuracy (Continued) Running Sum of Forecast Errors (RSFE) – indicates bias in the forecasts or the tendency of a forecast to be consistently higher or lower than actual demand n Running Sum of Forecast Errors, RSFE = ∑e t =1 Where MBA Nguyen Phi Hoang©2015_SCM t et = forecast error for period t 22 RSFE MAD Forecast Accuracy (Continued) Tracking signal – determines if forecast is within acceptable control limits If the tracking signal falls outside the pre-set control limits, there is a bias problem with the forecasting method and an evaluation of the way forecasts are generated is warranted Tracking Signal = MBA Nguyen Phi Hoang©2015_SCM RSFE MAD 23 Useful Forecasting Websites Institute for Forecasting Education www.forecastingeducation.com International Institute of Forecasters www.forecasters.org Forecasting Principles www.forecastingprinciples.com Stata (Data analysis & statistical software) www.stata.com/links/stat_software.html MBA Nguyen Phi Hoang©2015_SCM 24 Collaborative Planning, Forecasting, & Replenishment (CPFR) CPFR is a concept that aims to enhance supply chain integration by supporting & assisting joint practices CPFR seeks cooperative management of inventory through joint visibility & replenishment of product throughout the supply chain Information shared btw suppliers & retailers aids in planning & satisfying customers demands through a supportive system of shared information This allows for continuous updating of inventory & upcoming requirements, essentially making the end-to-end supply chain process more efficient Efficiency is also created through the decreased expenditures for merchandising inventory, logistics and transportation across all trading partners ( CSCMP) MBA Nguyen Phi Hoang©2015_SCM 25 Objectives of CPFR • Improve demand forecast accuracy • Deliver the right products at the right time to the right location • Reduce inventory across the supply chain, • Avoid stock-out • Improve customer service MBA Nguyen Phi Hoang©2015_SCM 26 CPFR’s Benefits • Strengthen partner relationships • Provide analysis of sales & order forecast • Uses point of sales data, seasonal activities, promotion, new product introductions & store openings or closing to improve forecast accuracy • Manage the demand chain & proactively eliminate problems before they appear • Allow cooperation on future requirements and plans • Use joint planning & promotion managements • Integrate planning, forecasting & logistics activities • Provide efficient catogory management & understanding of consumer purchasing pattern • Provide analysis of key performance metrics to reduce supply chain ineffeciencies, improve customer service & increase revenues & profitability • MBA Nguyen Phi Hoang©2015_SCMr 27 Collaborative Planning, Forecasting, & Replenishment (CPFR) In short, we can see: • CPFR is a business practice that combines the intelligence of multiple trading partners in the planning & fulfillment of customer demands • Real value of CPFR comes from sharing of forecasts among firms rather than sophisticated algorithms from only one firm • CPFR provides the supply chain with a plenty of benefits but requires a fundamental change in the way that buyers & sellers work together MBA Nguyen Phi Hoang©2015_SCM 28 Collaborative Planning, Forecasting, & Replenishment VICS’s CPFR Model (Continued) MBA Nguyen Phi Hoang©2015_SCM (Fig 5.5) 29 CPFR Model Collaborative Planning, Forecasting, & Replenishment (Continued) Step 1: Collaboration Arrangement Step 2: Joint Business Plan Step 3: Sales Forecasting Step 4: Order Planning/Forecasting Step 5: Order Generation Step 6: Order Fulfillment Step 7: Exception Management Step 8: Performance Assessment MBA Nguyen Phi Hoang©2015_SCM 30 Software Solutions Forecasting Software Business Forecast Systems www.forecastpro.com John Galt www.johngalt.com Just Enough www.justenough.com SAS www.sas.com JDA Software Group www.jda.com i2 Technologies www.i2.com Oracle MBA Nguyen Phi Hoang©2015_SCM www.oracle.com 31 ... actual demand for Period t α MBA Nguyen Phi Hoang©20 15_ SCM = smoothing constant (0 ≤ α ≤1) 15 Forecasting Techniques (Continued) Exponential Smoothing MBA Nguyen Phi Hoang©20 15_ SCM (Fig 5. 3) 16... supply chain & mitigates/decrease supply -demand mismatch problems MBA Nguyen Phi Hoang©20 15_ SCM Demand Forecasting A forecast is an estimate of future demand & provides the basis for planning... together MBA Nguyen Phi Hoang©20 15_ SCM 28 Collaborative Planning, Forecasting, & Replenishment VICS’s CPFR Model (Continued) MBA Nguyen Phi Hoang©20 15_ SCM (Fig 5. 5) 29 CPFR Model Collaborative