Business

Operational B I In Supply Chain Planning

Categories
Published
of 34
All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
Related Documents
Share
Description
1. Solve the Insight Puzzle & See the Entire Picture ! Operational business intelligence in supply chain planning Johan Blomme Business Intelligence Manager, AMP…
Transcript
  • 1. Solve the Insight Puzzle & See the Entire Picture ! Operational business intelligence in supply chain planning Johan Blomme Business Intelligence Manager, AMP email johan.blomme@ampnet.be
  • 2. Agenda Meeting the demand economy Trends in business intelligence Predicting out of stocks with real-time P.O.S.-data
  • 3. Meeting the demand economy
  • 4. Copyright © IRI, 2005. Confidential and proprietary.
  • 5. The future value chain : – capturing of demand signals to estimate true customer demand – real-time visibility and information sharing with partners demand driven value chain Copyright © IRI, 2005. Confidential and proprietary.
  • 6. Trends in business intelligence
  • 7. BI has evolved from its primary purpose of ad hoc query and analysis on a static store of historical information to analyzing transaction data in (near) real-time. MANAGEMENT INFORMATION BUSINESS OPERATIONS DATA DRIVEN PROCESS DRIVEN TIME DELAYED REAL TIME Copyright © IRI, 2005. Confidential and proprietary.
  • 8. MANAGEMENT INFORMATION BUSINESS OPERATIONS prospective, proactive information delivery, actionable analytics alert predictive notification analysis Buisness value data mining (BAM) restrospective information monitor delivery at multiple levels OLAP query & reporting What happened ? What’s happening now ? What might happen ? Copyright © IRI, 2005. Confidential and proprietary.
  • 9. •What has happened ? •e.g. What is M.A.D. of forecasts for product X ? Business •e.g. Why have out of stocks increased in week 20 ? reporting •What’s happening now : Performance measurement and alert notification Responsive •e.g. what is OOS % at the end of day 1 of sales analytics promotion ? •What might happen : business process optimization •e.g. SKU is going to be out of stock ; increase Actionable replenishment frequency to prevent OOS analytics Copyright © IRI, 2005. Confidential and proprietary.
  • 10. DATA DRIVEN PROCESS DRIVEN The emphasis is not on the data itself, but on the business processes that generate the data. « Business intelligence is moving into the context of the business process, not just to make users’ information experience more effective, but also to allow for business process optimization » . Software Macro-Trends : Reshaping Enterprise Software – Sep. 2005 Copyright © IRI, 2005. Confidential and proprietary.
  • 11. a data store is fed by operational systems and then the starting point is the business process in the center delivers reporting (the data and the reporting are determined by the process) the flow of information is two-way : from business processes to analytics and from analytics to business processes (closed-loop approach) operational and analytical processes are converging Copyright © IRI, 2005. Confidential and proprietary.
  • 12. TIME DELAYED REAL TIME analysis happens after fact, using aggregated and analysis of detailed data while event is occurring detailed data (query driven) events are interpreted in real-time : – monitor – interpret – predict Copyright © IRI, 2005. Confidential and proprietary.
  • 13. Predicting out of stocks with real-time P.O.S.-data
  • 14. Publisher Distributor Newsstand Product Flow demand Patterns (bullwhip effect !) Information Flow Fragmented and inefficient due to poor flow of information 1 Copyright © IRI, 2005. Confidential and proprietary.
  • 15. The publishing supply chain is partly inefficient due to a lack of visibility of day-to-day demand and stock positions. Return rates of 60 % and more are not uncommon in the publishing industry. While excess inventory leads to waste, at the same time retailers are often faced with the problem of out of stocks : – it is estimated that out of stocks cause lost sales of about 3-4 % ; – most OOS-problems are caused inside the store. Finding a balance between inventory and service levels will continue to grow as the numer of SKU’s continues to grow (niche marketing), in combination with seasonal effects, frequent promotional activities, etc. To minimize inventory and improve product availability, a better view of real demand is necessary. Copyright © IRI, 2005. Confidential and proprietary.
  • 16. Managing the replenishment process can increase visibility in the supply chain. Generate operational improvements from downstream retail (P.O.S.)-data to reduce out of stocks and improve sales. monitor stock-levels through real-time data gathered at P.O.S. flow of information Store direct Ordering customer supplier VMI, automatic replenishment processes flow of goods Copyright © IRI, 2005. Confidential and proprietary.
  • 17. logistics as a marketing tool logistics as a marketing tool chemical industry machine building paper industry plant constructions automotive electronics logistics as a cost saving tool logistics as a cost saving tool Copyright © IRI, 2005. Confidential and proprietary.
  • 18. In order to develop replenishment models, we need evidence about the relationship between performance variables (e.g. inventory levels, out of stock) and contextual variables (e.g. store and product characteristics) ? What is the power of P.O.S. real-time data to predict out of stock ? Copyright © IRI, 2005. Confidential and proprietary.
  • 19. AMP-Distrishop : daily P.O.S.-data from major retailers Copyright © IRI, 2005. Confidential and proprietary.
  • 20. Visualisation of sales velocity for weekly titles (source : AMP-Distrishop) Copyright © IRI, 2005. Confidential and proprietary.
  • 21. Visualisation of sales velocity for weekly titles (source : AMP-Distrishop) Copyright © IRI, 2005. Confidential and proprietary.
  • 22. Product velocity is the key : – the faster moving the item, the bigger the impact on the business (e.g. negative consumer reactions) ; – the focus needs to be on the fastest moving items. Test : – weekly magazines (392) ; – selection of 25 titles : • fast moving items • P.O.S.-coverage : distributed in at least 1.000 P.O.S. • minimum circulation order : 10.000 copies – measurement of sales velocity for each item in each store during a 10-week period (april-june 2007) ; – Distrishop-P.O.S. (413) : selection of 284 newsstands (413 -> 284 : due to validity control of real-time data). Copyright © IRI, 2005. Confidential and proprietary.
  • 23. A relatively small number of media products constitutes the majority of newsstand sales Copyright © IRI, 2005. Confidential and proprietary.
  • 24. Total sample (combination P.O.S./#weeks/#media products) = 41.521 « balanced » samples (based on incidence of OOS, 12.3%): – training – test POS/ #weeks / # media products 41.521 % OOS (12,3 %) 5.106 training sample (N = 5.106) - c=0.728 random sample of 2.553 from non-OOS combinations (36.415) random sample of 2.553 from OOS-occurrences (5.106) test sample (N = 5.106) - c=0.712 random sample of 2.553 from non-OOS combinations (36.415) 2.553 OOS-occurrences not in training sample Copyright © IRI, 2005. Confidential and proprietary.
  • 25. PREDICTIVE DATA UNCERTAINTY OUTCOME ANALYSIS OOS P.O.S.-features sales history sales velocity logistic regression Copyright © IRI, 2005. Confidential and proprietary.
  • 26. Unit of analysis : P.O.S. x MEDIA PRODUCT (WEEKLY MAGAZINE) AT PARTICULAR OSD IN 10-WEEK PERIOD PRODUCT & P.O.S. CHARACTERISTICS product id (25 media products) CAT 1-25 no. of titles in newsstand CAT . < 500 . 500-1000 . > 1000 P.O.S. development : evolution of P.O.S. turnover (2006 vs. preceeding CAT . expansive years) ; . positive . constant . declining . strongly declining SALES HISTORY history of OOS during 10 weeks preceeding media issue INT # OOS incidences occurring in 10-week period before OSD inventory history during 10 weeks preceeding media issue INT mean % unsolds in 10-week period before OSD SALES VELOCITY sales variance : sales coefficient of variance (calculated by dividing the INT scale value from 1 to 8 standard deviation of sales in a 7 day-period by mean sales value) sales throughput: mean sales in a 7 day-period INT scale value from 1 to 8 inventory range of coverage : relative measure of inventory level, INT scale value from 1 to 8 calculated as the absolute inventory divided by mean sales Copyright © IRI, 2005. Confidential and proprietary.
  • 27. Odds ratios for the risk of OOS : effect size of media products product id (25 products) media product 0,563*** 0,588*** 0,611*** 0,754*** … … 1,385*** 1,447*** 1,603*** 1,749*** no. of titles . < 500 . 500-1000 ® . > 1000 P.O.S. development . expansive . positive . constant ® . declining . strongly declining history of OOS # OOS incidences occurring in 10-week period before OSD inventory history mean % unsolds in 10-week period before OSD sales variance sales throughput inventory range of coverage *** p<0.001 Copyright © IRI, 2005. Confidential and proprietary.
  • 28. Confidence intervals (95 %) for odds ratios of media products Copyright © IRI, 2005. Confidential and proprietary.
  • 29. Odds ratios for the risk of OOS : effect size of media products, no. of titles and P.O.S.-development product id (25 media product 0,563*** 0,571*** products) 0,588*** 0,600*** 0,611*** 0,678*** 0,754*** 0,879*** … … … … 1,385*** 1,301*** 1,447*** 1,500*** 1,603*** 1,588*** 1,749*** 1,678*** no. of titles . < 500 1,003 . 500-1000 ® 1,000 . > 1000 1,115* P.O.S. . expansive 0,895* development . positive 0,966 . constant ® 1,000 . declining 1,062 . strongly declining 1,038 history of OOS # OOS incidences occurring in 10-week period before OSD inventory history mean % unsolds in 10-week period before OSD sales variance sales throughput inventory range of coverage * p<0.05 *** p<0.001 Copyright © IRI, 2005. Confidential and proprietary.
  • 30. Odds ratios for the risk of OOS : effect size of media products, no. of titles, P.O.S.-development and sales history product id (25 media product 0,563*** 0,571*** 0,622*** media products) 0,588*** 0,600*** 0,635*** 0,611*** 0,678*** 0,712*** 0,754*** 0,879*** 0,891*** … … … … … … 1,385*** 1,301*** 1,400*** 1,447*** 1,500*** 1,409*** 1,603*** 1,588*** 1,550*** 1,749*** 1,678*** 1,602*** no. of titles . < 500 1,003 0,998 . 500-1000 ® 1,000 1,000 . > 1000 1,115* 1,015 P.O.S. . expansive 0,895* 0,843* development . positive 0,966 0,920 . constant ® 1,000 1,000 . declining 1,062 1,034 . strongly declining 1,038 1,012 history of OOS # OOS incidences occurring in 10-week 1,127* period before OSD inventory history mean % unsolds in 10-week period 0,988 before OSD sales variance sales throughput inventory range of coverage * p<0.05 *** p<0.001 Copyright © IRI, 2005. Confidential and proprietary.
  • 31. Odds ratios for the risk of OOS : effect size of media products, no. of titles, P.O.S.-development, sales history and sales velocity product (25 media product 0,563*** 0,571*** 0,622*** 0,890* media products) 0,588*** 0,600*** 0,635*** 0,901 0,611*** 0,678*** 0,712*** 0,867* 0,754*** 0,879*** 0,891** 0,850** … … … … … … … … 1,385*** 1,301*** 1,400*** 1,119* 1,447*** 1,500*** 1,409*** 1,246** 1,603*** 1,588*** 1,550*** 1,189** 1,749*** 1,678*** 1,602*** 1,164* no. of titles . < 500 1,003 0,998 1,005 . 500-1000 ® 1,000 1,000 1,000 . > 1000 1,115* 1,015 1,010 P.O.S. . expansive 0,895* 0,843* 0,866* development . positive 0,966 0,920 0,985 . constant ® 1,000 1,000 1,000 . declining 1,062 1,034 1,053 . strongly declining 1,038 1,012 1,076 history of OOS # OOS incidences occurring in 10-week 1,127* 1,109 period before OSD inventory history mean % unsolds in 10-week period 0,988 0,983 before OSD sales variance 1,229** sales throughput 0,890* inventory range 0,846* of coverage * p<0.05 ** p< 0.01 *** p<0.001 Copyright © IRI, 2005. Confidential and proprietary.
  • 32. Model fitted : c= 0.712 The c-statistic represents the proportion of pairs with different observed outcomes (no OOS / OOS) for which the model correctly predicts a higher probability for observations with the event outcome (OOS) than the probability for nonevent observations. For the present model, the value of the c-statistic means that 71,2 % of all possible pairs – one with no OOS and one with OOS – the model correctly assigned a higher probability to the cases in which OOS occurred. The c-statistic provides a basis for comparing different models fitted to the same data : for a model without sales velocity-variables the c-statistic is 0,627. While the incidence of OOS is strongly influenced by media product characteristics, the introduction of sales velocity – reduces the effect of media product ; – independent of all other features, out of stock-occurrences vary significantly by sales velocity : • e.g. sales variance : the odds ratio of 1,229 may seem relatively small ; however if the effect size of sales variance is transformed to a probability, it means that with a one unit increase in sales variance the 00S-probability increases with 2,4 % ; at the highest level of sales variance, the probability of out of stock increases with 16,8%. Copyright © IRI, 2005. Confidential and proprietary.
  • 33. Conclusion Product sales velocity has an influence on OOS, implicating that real time visibility of sales at item level to monitor changes in sales velocity makes it possible to improve in store operations. Real-time P.O.S.-data is therefore a driver for actionable analytics and business process optimalization : – to report and to alert on out of stocks as they happen ; – guide the replenishment process, based on true customer demand (when should which qty be ordered) ; – which results in greater in store availability and visibility of products ; – to enhance the customer experience of shopping. Copyright © IRI, 2005. Confidential and proprietary.
  • 34. Recommendations for future analysis data accuracy operationalization of products characteristics (e.g. promotional events) further examination of store characteristics, e.g. SKU-density development of forecast models based on history data and real-time data : • setup of rules-driven stock management decisions : detection of regular cycles (normal performance varies by hour of the day, day of the week, …) and exceptions on regular cycles • setup of individual (P.O.S.-) profiles : an increase in the velocity of sales may trigger an alert for a P.O.S., but not for a different newsstand Copyright © IRI, 2005. Confidential and proprietary.
  • Search
    Related Search
    We Need Your Support
    Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

    Thanks to everyone for your continued support.

    No, Thanks