Forecasting With History Santiago Gallino Tuck School of Business Toni Moreno Kellogg School of Management January 2017 July 2013 LBS London, UK
Learning Modules 1. Demand forecasting 2. Inventory Decisions 3. Assortment Planning 4. Pricing Decisions 5. The omnichannel customer 6. Fulfilling omnichannel demand 7. Omnichannel journeys 8. Supporting an omnichannel strategy 11 May 2017 Retail Fundamentals 2
M1.2 Opening 1 Inaccurate forecast cost Money. We tend to think that this cost only occur when we end up with too much inventory but it actually we can loose money both ways. In this video we will study how to understand historical data to create forecast. We will look at: Trends Seasonality Other factors We will discuss the implications of good and bad forecast for a retailer. 11 May 2017 Retail Fundamentals 3
M1.2 Trends 2 Looking at historical data can be useful to identify trend for the retailer as a whole, for particular categories or individual products. Lets look at three categories in Germany over time 11 May 2017 Retail Fundamentals 4
Sales volume of smartphones in Germany 2008-2016 Sales volume of smartphones in Germany from 2008 to 2016 (in million devices) 30 26.2 25 21.6 22.86 24.4 Sales volume in million devices 20 15 10 5 5 5.7 10.4 15.9 0 2008 2009 2010 2011 2012 2013 2014 2015 Note: Germany; 2008 to 2015 Further information regarding this statistic can be found on page 8. Source: EITO; IDC; ID 461852
Sales volume of camcorders and digital cameras in Germany 2005-2015 Number of camcorders and digital cameras sold on the consumer market in Germany from 2005 to 2015 (in 1,000 devices) Digital cameras Poly. (Digital cameras) 10000 9,320 9000 8,180 8,240 8,250 Sales volume in thousand devices 8000 7000 6000 5000 4000 3000 2000 7,038 5,570 4,012 3,401 1000 0 2008 2009 2010 2011 2012 2013 2014 2015 Note: Germany; 2005 to 2015; private demand Further information regarding this statistic can be found on page 8. Source: GfK; gfu; BVT; ID 462742
Sales volume of camcorders and digital cameras in Germany 2005-2015 Number of camcorders and digital cameras sold on the consumer market in Germany from 2005 to 2015 (in 1,000 devices) Camcorders* Linear (Camcorders*) Sales volume in thousand devices 900 800 700 600 500 400 300 200 718 852 810 712 644 663 660 772 100 0 2008 2009 2010 2011 2012 2013 2014 2015 Note: Germany; 2005 to 2015; private demand Further information regarding this statistic can be found on page 8. Source: GfK; gfu; BVT; ID 462742
M1.2 Trends 3 Looking at historical data can be useful to identify trend for the retailer as a whole, for particular categories or individual products. Lets look at three categories in Germany over time Why should we care about this? If you are Best Buy this is going to be useful information when deciding on what product to carry in the future. However, trends are not the only challenge that retailers face when forecasting. 11 May 2017 Retail Fundamentals 8
M1.2 Seasonality 3 Seasonality can be a big factor too. 11 May 2017 Retail Fundamentals 9
Beer monthly sales in the United Kingdom (UK) 2013-2016 Monthly beer sales volume in the United Kingdom (UK) from May 2013 to May 2016 (in 1,000 hectolitres) 6,000 5,000 4,000 3,000 2,000 1,000 0 May 2013 Jun 2013 Jul 2013 Aug 2013 Sep 2013 Oct 2013 Nov 2013 Dec 2013 Jan 2014 Feb 2014 Mar 2014 Apr 2014 May 2014 Jun 2014 Jul 2014 Aug 2014 Sep 2014 Oct 2014 Nov 2014 Dec 2014 Jan 2015 Feb 2015 Mar 2015 Apr 2015 May 2015 Jun 2015 Jul 2015 Aug 2015 Sep 2015 Oct 2015 Nov 2015 Dec 2015 Jan 2016 Feb 2016 Mar 2016 Apr 2016 May 2016 Note: United Kingdom; January 2013 to May 2016 Further information regarding this statistic can be found on page 8. Source: British Beer & Pub Association; ID 308989
Monthly revenue of the U.S. video game industry 2014-2016, by segment Total and segment revenue of the U.S. video game industry from November 2014 to November 2016 (in billion U.S. dollars) 3.5 Total 3 Revenue in billion U.S. dollars 2.5 2 1.5 1 0.5 0 Nov Dec Jan Feb Mar Apr May Jun '14 '14 '15 '15 '15 '15 '15 '15 Jul '15 Aug '15 Sept Oct '15 '15 Nov Dec Jan Feb Mar Apr May Jun '15 '15 '16 '16 '16 '16 '16 '16 Jul '16 Aug '16 Sept Oct Nov '16 '16 '16 Note: United States; November 2014 to November 2016 ; console and portable (excluding PC games); physical and full game digital formats from the PSN and Xbox live platforms Further information regarding this statistic can be found on page 8. Source: NPD Group; AFJV; VentureBeat; ID 201073
M1.4 Other Factors and Regression 4 Other known decision affecting sales. Promotions, discounts, competition, etc The main tool used to analyze historical data and generate forecasts based on this data is regression. Let s how a regression can help us understand historical data and how we can generate a good forecast based on that data 11 May 2017 Retail Fundamentals 12
4 Let s look at a particular store total sales over time. Sales 50000 100000 150000 200000 2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1 Year Week 11 May 2017 Retail Fundamentals 13
4 Let s look at a particular store total sales over time. Trend Removed -50000 0 Sales 50000 100000 150000 2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1 Year Week 11 May 2017 Retail Fundamentals 14
M1.2 Level of aggregation and Trends 4 Let s look at a particular store total sales over time. Trend Removed and promo removed 0 Sales 50000 100000-50000 2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1 Year Week 11 May 2017 Retail Fundamentals 15
M1.2 Level of aggregation and Trends 4 Let s look at a particular store total sales over time. Trend Removed and month removed and promo removed Sales 0 20000 40000-40000 -20000 2011w1 2011w26 2012w1 2012w27 2013w1 2013w26 2014w1 Year Week 11 May 2017 Retail Fundamentals 16
M1.5 Closing 5 Now we are ready to generate a forecast for this store. What information we need? We are going to combine these components to obtain our forecast. Trend Seasonality (Holidays) Promotions This can be done at the at different aggregation levels in terms of chain, store, category or product Annual, quarterly, monthly, weekly or daily data Work with the tools to understand the different factors that can affect your forecast and how to implement it. 11 May 2017 Retail Fundamentals 17
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