Smart Inventory Management - EN

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A new look at familiar demand phenomena from forecasting perspective

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Smart Inventory Management - EN

  1. 1. Smart Inventory Management in RetailA new look at familiar demand phenomena from forecasting perspective Alexey Ivasyuk, De Novo© 2012 www.de-novo.biz
  2. 2. Some Results Of Our Survey• How do you run the process of ordering goods to stores? – This is done by a store personnel… 60% – Ordering process done by the centralized replenishment team 30%... – Automated ordering procedure, with manual but centralized ordering process for seasonal, new and promotional goods <10% – Why does it matter? 1%• Do you have a clear ordering and delivery calendar for each supplier in your IT-system? – The calendar is stated in agreements and is known to people who order goods … 60% – We maintain shipment terms in the system and refer to the terms for information purpose when ordering <30% – We have a clear ordering and delivery calendar and use it in the automated ordering procedure <10%. 2
  3. 3. Some Results Of Our Survey • How many days/weeks of stock do you usually order for the goods delivered daily, weekly and long-term supply – It depends… 60% – We order 2-3 delivery cycles plus redundant… 30% – We have an automated ordering procedure which uses historical sales, promotional and delivery factors multiplied by the delivery cycle plus… <10% • How many stock weeks do you have for your TOP-items? – 2-3 weeks of stock….60% • How many stock weeks/months do you have in total? – 2-3 months of stock… 60% – Oh, you’d better not to know this… 1% 3
  4. 4. Do You Have The Best Stock Levels YouCould Have? 17.7Average out-of-stock levels (%) 8.3 8.6 7.9 All EU North America Russia, CIS* Source: ECR * Including our observations 90% of our respondents answered «No»4
  5. 5. Customer Reaction to Stockouts (%) Source: ECR Doesnt buy 9 anything Buys a different size 16 Total losses: Revisits the shop later 17 47%!!! Buys the same item 21 in a competitor shop Buys a different 37 brand Most of the retailers order redundant stock just to avoid the 5 losses
  6. 6. Top Root Causes of Stockouts (%) All Countries 30% 59% 11% Others Shelf replenishment Ordering and replenishment issues Source: ECR Europe 6
  7. 7. An Open Secret:Retail Store Ordering Formula • Order volume = [Demand forecast] * [Influencing factors] * [Delivery Cycle] + [Safety Stock] + [Presentation Stock] – [Current stock] • Safety Stock = Forecasting Error (MAD/RMSE) multiplied by the delivery cycle period The best forecast we know is about 10-15% of error, but: The more precise the forecast, the more prone it is to error 30% of a forecast error means a good forecast 50% of a forecast error is still acceptable in FMCG because it requires you to keep just 12 days of stock with weekly delivery cycle Make a guess what is the forecasting error for the survey respondents? 7
  8. 8. How the survey respondents predict thedemand? • Orders are done based on the latest weeks • Seasonal orders are done based on a previous season – no consideration of assortment changes 60% • No detailed review at the past promotions while ordering for the new promotions • Out-of-stocks are not estimated • Centralized ordering using moving average forecast • Seasonal, new items, and promo orders are done 30% manually based on analytics and excel sheets • Out-of-stocks are estimated based on the last sales • Several forecasting methods are in use • Seasonal patterns are calculated and verified ?% • Promotional performance is estimated and used for future promotions • Out-of-stocks are estimated based on forecast 8
  9. 9. Choosing a Forecasting ApproachWhat do mathematicians sayTime Frame Forecast Horizon Best ApproachShort Up to 19 months Time Series Analysis - methods are based on the premise that you can predict the future performance by analyzing the past behaviorMedium 6-36 months Casual Analysis – uses the forecast for several independent factors to predict a dependant measureLong 19 months – 5 years Expert Opinion - As the time horizon for the forecast moves further out into the future, expert opinion becomes the most reliable predictor Longer-range forecasts should generate data at higher levels to offset the increasing likelihood of error 9
  10. 10. What Time Series Analysis is Actual Sales Holt-Winters Method (takes the seasonality into account) 10
  11. 11. What Time Series Analysis is Sales Double Exponential Smoothing 11
  12. 12. What Time Series Analysis is Sales Single Exponential Smoothing 12
  13. 13. Demand Influences • ChristmasSales •Promotion • Unknown • Unknown •Advertisement •Price decrease • Unknown •Competitor •Cross-elasticity raises price Seasonality Influencing Events factors Sales Time • Out-of-stock Clean sales 13 Baseline forecast Exceptions
  14. 14. Forecasting and Category Management Forecast at an item-group level Consolidated forecast at SKU-levelA common case with assortment• Group-level forecast shows upward trend• Consolidated SKU-level forecast shows downward trendWhat could it mean? Got to review the SKU range! Demand forecasting is a baseline for the category management and assortment planning 14
  15. 15. Promotional Activity Example Promo-sales Seasonal-sales Baseline forecast Promo periodQuestion:• How to estimate the promotional impact?• How to split a promotional sales uplift and seasonal demand?• How to do all of this, if promotional activity took place for 500 SKUs in 100 stores?• How to re-use the experience in the future? Tracking the forecast vs. actual sales will allow to do it regularly 15 in a proper manner
  16. 16. Price Elasticity of Demand Normal case Cross-elasticity • Price elasticities are almost always negative except for a few types of goods such luxury goods • Unclean sales history is not always telling this 16
  17. 17. Price Elasticity of Demand SKU Sales SKU Forecast Competitor We We raised raised raised prices prices a price for for another SKUs some SKUs in the same range Optimal price • Analyzing the forecast vs. actual What if the increase? sales is a basis for understandingDemand $4.99 price elasticity 855 pcs. • The elasticity can be considered at $5.59 550 pcs. item-group level as well as at SKU level 17 Price
  18. 18. Common Misconceptions «Complicated forecasts cannot be verified» «Need to hire highly qualified analysts in order to do forecast» «Users will never understand it – they will just have to accept it as is» «Forecasts should be directly generated at the lowest level of execution» «Time Series Forecast is not suitable for such industries as fashion, boutiques and jewellery» «Our sales are so heavily dependent on unpredictable factors that automated forecast will never help us» 18
  19. 19. Thank You! Alexey Ivasyuk +38 (044) 200-93-39 alexey.ivasyuk@de-novo-bizThere are huge opportunities to minimize costs and maximize profits if weknow what tomorrow will bring - but we dont!Therefore we forecast!

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