Demand Forecasting in Supply Chains PDF

Demand Forecasting in Supply Chains Amit Upadhyay Department of Management Studies 1  What is Forecasting? ► Process of predicting a future event ► Underlying basis of all business decisions ► Production ►...

Demand Forecasting in Supply Chains Amit Upadhyay Department of Management Studies 1  What is Forecasting? ► Process of predicting a future event ► Underlying basis of all business decisions ► Production ► Inventory ► Personnel ► Facilities 2  Forecasting Time Horizons 1. Short-range forecast ► 1-3 months ► Purchasing, job scheduling, workforce levels, job assignments, production levels 2. Medium-range forecast ► 3 months to 2 years ► Sales and production planning, budgeting 3. Long-range forecast ► 2-3+ years ► New product planning, facility location, research and development 3  Distinguishing Differences Medium/long range forecasts deal with more comprehensive issues and aggregate level data Short-term forecasting usually employs different methodologies than longer-term forecasting Short-term forecasts tend to be more accurate than longer-term forecasts 4  Types of Forecasts 1. Economic forecasts ► Address business cycle – inflation rate, money supply, housing, etc. 2. Technological forecasts ► Predict rate of technological progress ► Impacts development of new products 3. Demand forecasts ► Predict sales of existing products and services 5  Seven Steps in Forecasting 1. Determine the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of the forecast 4. Select the forecasting model(s) 5. Gather the data needed to make the forecast 6. Make the forecast 7. Validate and implement the results 6  The Reality ! ► Forecasts are seldom perfect ► unpredictable outside factors may impact the forecast ► Most techniques assume an underlying stability in the system ► Product family and aggregated forecasts are more accurate than individual product forecasts 7  Forecasting Approaches Qualitative Quantitative Methods Methods ► Used when situation is ‘stable’ ► Used when situation is and historical data exist vague and little data exist ► New products ► Existing products ► New technology ► Current technology ► Involves intuition, ► Involves mathematical experience techniques ► e.g., forecasting sales of LED TVs 8  Overview of Qualitative Methods 1. Jury of executive opinion ► Pool opinions of high-level experts, sometimes augmented by statistical models 2. Delphi method ► Panel of experts, queried iteratively 3. Sales force composite ► Estimates from individual salespersons are reviewed for reasonableness, then aggregated 4. Market Survey ► Ask the customer 9 Overview of Quantitative Approaches 1. Naive approach Time-series 2. Moving averages models 3. Exponential smoothing 4. Trend Projection 5. Linear regression Associative model 10  Time-Series Forecasting ► Set of evenly spaced numerical data ► Obtained by observing response variable at regular time periods ► Forecast based only on past values; no other variables considered ► Assumes that factors influencing the past will continue to influence in future 11  Time-Series Components Four components of variations: (i) Trend, (ii) Seasonality, (iii) Cyclic, (iv) Random Trend component Demand for product or service Seasonal peaks Actual demand line Average demand over 4 years Random variation | | | | 1 2 3 4 Time (years) 12  Trend Component ► Overall upward or downward pattern ► Changes due to population, age, PLC stage, etc. ► Typically, long duration trend 13  Seasonal Component ► Regular pattern of up and down fluctuations ► Due to weather, customs, etc. ► Occurs within a single year PERIOD LENGTH “SEASON” LENGTH NUMBER OF “SEASONS” IN PATTERN Week Day 7 Month Week 4 – 4.5 Month Day 28 – 31 Year Quarter 4 Year Month 12 Year Week 52 14  Cyclical Component ► Recurring up and down movements ► Affected by the business cycle, and economic factors ► Typically, multiple years duration 0 5 10 15 20 15  Random Component ► Random, uncertain fluctuations ► Due to unknown or inexplicable reasons ► Short duration and nonrepeating 1 2 3 4 5 6 7 8 9 10 11 16  Naive Approach ► Assumes demand in the next period is the same as demand in the most recent period ► If January sales were 70, then February sales will be 70 ► Sometimes cost-effective and efficient ► Can be a good starting point 17  Moving Averages ► Moving Average is a series of arithmetic means 18  Moving Average: An example MONTH ACTUAL SALES 3-MONTH MOVING AVERAGE January 10 February 12 March 13 April 16 May 19 June 23 July 26 August 30 September 28 October 18 November 16 December 14 19  Moving Averages… ► Used if there is little or no trend ► Used often for smoothing 20  Weighted Moving Average ► Used when some trend might be present ► Usually, older data less important ► Weights based on experience and intuition Weighted moving average 21 Weighted Moving Average: An example MONTH ACTUAL SALES 3-MONTH WEIGHTED MOVING AVERAGE January 10 10 February 12 12 March 13 13 April 16 [(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6 May 19 June WEIGHTS 23 APPLIED PERIOD July 26 3 Last month August 30 2 Two months ago September 28 1 Three months ago October 18 6 Sum of the weights November Forecast for 16this month = December 3 x Sales 14 last mo. + 2 x Sales 2 mos. ago + 1 x Sales 3 mos. ago Sum of the weights 22  Weighted Moving Average MONTH ACTUAL SALES 3-MONTH WEIGHTED MOVING AVERAGE January 10 10 February 12 12 March 13 13 April 16 [(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6 May 19 June 23 July 26 August 30 September 28 October 18 November 16 December 14 23  Graph of Moving Averages Weighted moving average (Ex. 2) 30 – 25 – Sales demand 20 – Actual 15 – sales Moving average (Ex. 1) 10 – Potential Problems With Moving 5– Average? | | | | | | | | | | | | J F M A M J J A S O N D Month  Potential Problems With Moving Average 1. Increasing n smooths the forecast but makes it less sensitive to changes 2. Does not forecast trends well 25  Common Measures of Error The objective is to obtain the most accurate forecast irrespective of the technique. Select a method that gives us the lowest forecast error. Three commonly used measures: ► Mean Absolute Deviation (MAD) ► Mean Squared Error (MSE) ► Mean Absolute Percent Error (MAPE) 26 Mean Absolute Deviation (MAD) S. Name of Observed Forecasted Rainfall Deviatio Absolute mm) X X- |X - | No. Catchmen Rainfall (in (in mm) n Deviation t 1. Roorkee 114.8 92.94 21.86 21.86 2. Nainital 75 63.14 11.86 11.86 3. Tehri 92.5 110.64 -18.14 18.14 4. Dehradun 80.3 63.44 16.86 16.86 5. Chamoli 55.9 82.04 -26.14 26.14 6. Rudrapray -0.14 0.14 121.88 122.02 ag -6.14 6.14 Total Absolute deviation = 101.14 mm 7. Pithoragar Total No. of Catchments (N) = 7 63.4 69.54 h Mean Absolute Deviation (M.A.D) = = = 14.44 mm 𝛴 𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝑣𝑎𝑙𝑢𝑒𝑠𝑜𝑓 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 101.14 𝑇𝑜𝑡𝑎𝑙𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠 7 27 Mean Squared Error (MSE) X- (X - )^2 S.N Name of Observed Forecasted Rainfall Deviation Squared Deviation X o. Catchment Rainfall (in mm) (in mm) 1. Roorkee 114.8 92.94 21.86 477.86 2. Nainital 75 63.14 11.86 140.66 3. Tehri 92.5 110.64 -18.14 329.06 4. Dehradun 80.3 63.44 16.86 284.26 5. Chamoli 55.9 82.04 -26.14 683.3 6. Rudrapray -0.14 0.019 121.88 122.02 ag -6.14 37.7 Total No. of Catchments (N) Total Squared deviation = 1952.86 7. Pithoragar 63.4 69.54 h =7 Mean Squared Error (M.S.E) = = = 278.98 𝛴 𝑆𝑞𝑢𝑎𝑟𝑒𝑑 𝑣𝑎𝑙𝑢𝑒𝑠 𝑜𝑓 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 1952.86 𝑇𝑜𝑡𝑎𝑙𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠 7 28 Thanks 29 Mean Absolute Percent Error (MAPE) X- S. Name of Observed Forecasted Deviation Absolute Relative Absolut X |X - | No Catchmen Rainfall (in mm) Rainfall (in mm) Deviation Error e%. t Error 1. Roorkee 114.8 92.94 21.86 21.86 0.190 19.0 2. Nainital 75 63.14 11.86 11.86 0.158 15.8 3. Tehri 92.5 110.64 -18.14 18.14 0.196 19.6 4. Dehradun 16.86 16.86 0.209 20.9 80.3 63.44 5. Chamoli 55.9 82.04 -26.14 26.14 0.467 46.7 6. Rudrapray -0.14 0.14 0.001 0.1 121.88 122.02 ag 7. Pithoragar -6.14 6.14 0.096 9.6 63.4 69.54 h M.A.P.E = 𝛴 𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 percent 𝑣𝑎𝑙𝑢𝑒𝑠𝑜𝑓 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑓𝑜𝑟𝑒𝑐𝑎𝑠𝑡 131.7= 18.81 = 𝑇𝑜𝑡𝑎𝑙𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠 7 30