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Analytics in Supply Chain Management

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Analytics in Supply Chain Management

  1. 1. BigSCM™Shaping Demand using Supply side Big Data
  2. 2. Contents Executive Summary Background Retail Domain at a glance Retail Domain at a glance Supply chain demystified Opportunity dimensions Introducing BigSCM BigSCM Product features- Adaptive Inventory with RFID BigSCM Product features- Predicting Inventory with Geo Loc BigSCM Product features- Intelligent usage of PoS data BigSCM Product features- Optimize SCM with Social media inputs Target Audience Value Proposition Vendors in this space Next Steps What do we require? • Assumptions • Investment Required • Development Period Challenges Questions
  3. 3. Executive summary While retailers are focusing more on understanding the customer preferences to better manage their merchandise and enhance the business profitability, there is a definite play on leveraging the big data in supply chain functions as well to enhance operational efficiencies and reduce costs This presentation prescribes a product concept henceforth known as “BigSCM” which fits in to the market vacuum of using Big Data technologies in the SCM realm and optimize SCM processes to the tune of 10 million per year and thus ensuring that the retail customer of this product will • Enhance productivity • Optimize Supply chain workflows • Reduce costs • Improve customer happiness index • Shape demand This product consumes Big data exhaust from the retail industry like • RFID, POS data, Geo location of inventory to optimize directly any lacunae in the SCM operations • Social media feeds, customer complains, call center logs, returns logs, warranty issues to fix any process related issues in the SCM workflows. •3
  4. 4. Background Pentagon uses social media analytics to Traditionally social media usage has infer and predict political unrest in other focused mostly on marketing, countries and take decisions on it. advertising and customer relationship Google uses search based analytics to management. Relatively few have comment on the pattern of epidemic tapped into the social networks as a outbreaks means of communicating both internally and with key supply-chain partners. We look at possibility to use Big Data for supply side rather than demand side. A more recent report by Aberdeen Companies are in the very early stages Group, Inc. 44 percent of industry were of adopting Big Data within their supply already using some types of social media chains. Its hard time tracking down in some manner to manage their supply great examples outside of customer chains, while 37 percent said they relations for using Big Data. intended to begin within the next one to SCM and Big data is Virgin territory and two years. as an early adaptor we are poised to gain unassailable lead in the market. 4
  5. 5. Retail Domain at a glance •5
  6. 6. Supply chain demystified •Supply chain management (SCM) is the management of a network of interconnected businesses involved in the ultimate provision of product and service packages required by end customers •Supply Chain Management spans all movement and storage of raw materials, work-in-process inventory, and finished goods from point-of-origin to point-of consumption •6
  7. 7. Supply chain in real life •7
  8. 8. Opportunity dimension- Where is Big Data in Retail •8
  9. 9. Opportunity dimension- Mapping domain to opportunity •9
  10. 10. Opportunity dimension- Benefits of Big Data analytics in Retail •10
  11. 11. Introducing BigSCM SCM recommendations• RFID• GeoLoc• POS• Call Center• Warranty• Cust Service• Return Info• Cust Rating SCM Optimization suggestions SCM Hadoop workflow Processing Framework reference Conversation Tracking framework
  12. 12. BigSCM Product features- Adaptive Inventory with RFID Real time RFID analysis • BigSCM will enable tracking of real-time inventory of any item in any location: • BigSCM will help in automated replenishment signals integrating with SCM workflow. • BigSCM will would aid in automated receiving and verification of items and quantities received at stores and warehouse, • BigSCM will make it easy for automated validation of fulfilled orders. •12
  13. 13. BigSCM Product features- Predicting Inventory with Geo Loc GPS-based location services • BigSCM will enable Real-time visibility of in-transit inventory and would allow sensing real-time demand signals to make this inventory “productive”. • BigSCM will offer Real-time location sensing for better supply-demand match, and reduce the fulfillment lead-time and inventory levels required without affecting service the customers. • BigSCM can help combining the RFID for cold-chain perishable goods and real-time GPS location, improve efficiencies to reduce the goods damaged due to temperature variations and expiration dates. •13
  14. 14. BigSCM Product features- Intelligent usage of PoS data PoS Data • BigSCM will use POS data to provide a real-time demand signal with price information. This will help in intelligent inventory deployments to optimize the inventory in the system, • BigSCM will use early trends detected for seasonal goods can help better manage the open orders when demand goes up and reduce potential clearance losses when it goes down. • BigSCM will help in Price optimization that can be fine-tuned with real-time POS data to optimize the profitability. •14
  15. 15. BigSCM Product features- Optimize SCM with Social media inputs Social Media helps SCM • BigSCM will use Big Data from unstructured data sources like call center logs, customer complaints, warranty returns, Twitter, Face Book to glean for Customer service related discussions, statuses etc. • BigSCM will create a context out of it and find the pattern of the discussion, get the gist of it, get the sentiment, get the prevalent complaint on a product line make a decision if intervention has to be made, notify to relevant link in the Supply chain workflow. • By sharing information instantly BigSCM can connect with virtually everyone involved in a supply chain, retailers can more quickly take actions such as ordering more of the popular products or alerting warehouses when orders are not getting fulfilled and delivered to customers on time. •15
  16. 16. Target Audience Suppliers Customers Distributors BigSCM™ Marketing and Manufacturers sales
  17. 17. Value Proposition NLP engine at the heart of BigSCM BigSCM would reduce lead time and would integrate could be patented and generate tighter feedback from customer to market demand residual revenues BigSCM can be used to generate Increase in on time shipments compared with to revenues from diverse streams those not BigSCM. An average out-of-stock rate of lessened compared to for those not using BigSCM; A remarkable increase in fulfillment costs, compared BigSCM usage result in happier to those not using BigSCM. customers, fewer out-of-stock products and lower fulfillment costs, Analytical insights from the customer The ability to collect, analyze, and use real-time demand base can be converted actions that and inventory will open new opportunities to optimizing the could be applied to fine tune the SCM supply chain operations and provide competitive cost- processes. advantage.
  18. 18. Vendors in this Space or Ecosystem Competition As far as I searched, competitors in the SCM ecosystem have not adopted a similar platform to enable SCM optimization. There are pockets in academia that are performing research on the core NLP technology that would be useful.
  19. 19. Next Steps BigSCM™ Software and People readiness Collaboration Hardware: Hadoop, Mahout, Competency in Real Probe if any existing time Searching, NLP, NLP stack can be Optimization algorithms leveraged Dependency Assumption Assumption Assumption A retail company’s data Algorithms for NLP is of Social data would be Social data would be Retail companies rely Retail companies rely assets can be used for medium complexity relevant to aa relevant to on RFID Geo Loc and on RFID Geo Loc and building the search companies retail companies retail PoS data for their PoS data for their index workflow workflow operations operations Development Team Development period Single Agile team of 8- Around 6-8 months for 10 people a working pilot in production 19
  20. 20. Consulting Solutions - Retail
  21. 21. Business Flow
  22. 22. Challenges in Retail domain Lack of Supply Chain Management optimization Decrease in sales Marketing and sales Demand forecasting Logistics
  23. 23. Problem AnalysisAnalyze sales data and cost incurred by the stores. Analyze if there is any seasonal pattern in the demand /sales. Analyze demographic locations and shipping informationof the stores.Objective: Reduce operational costs
  24. 24. Insights Forecast future inventory demand and safety stock perproduct per store thereby optimizing inventory costs. Supply Chain Management optimization.
  25. 25. Blueprint By analyzing the sales information from each store wepredict the future demand for each store and will come upwith safety stock per product per store thereby optimizinginventory costs. Analyze the transport route, source and destination,capacity of the vehicle and cost for each trip and suggestways for optimizing transportation cost. Analyze vendor data and come up with arecommendation on which vendor to procure products forlow cost thereby optimizing the procurement cost
  26. 26. Data attributes Transportation and distribution costs Inventory Inventory_ID CategoricalInventory_ID Categorical Source_Location CategoricalOrder_ID Categorical Destination_Location CategoricalOrder_Quantity Numeric Distance NumericLead_Time Categorical Time_Taken_To_Travel NumericWarehouse_maintenance_Cost Numeric Vehicle_Capacity Numeric Cost_Per_Trip Numeric No_of_Trips_Per_Month NumericProduct_Wise_Shelf_Life Numeric Mode_of_Transport Categorical
  27. 27. Data attributes Procurement_Costs POS Item_ID CategoricalOrder_ID Categorical Cost NumericNo_Of_Units_Sold Numeric Lead_Time NumericTotal_Cost Numeric No_Of_Items_Produced_In_a_Item_IDs Categorical Month Numeric
  28. 28. Solution Analysis Class of the problem: OptimizationTechniques:PCA, Random Forests to reduce dimensions.Multi objective optimization (Goal Programming), LinearProgramming.Supply chain problems are characterized by decisions that areconflicting by nature. Modeling these problems using multipleobjectives gives the decision maker a set of pareto optimal solutionsfrom which to choose.
  29. 29. ROINo. of stores: 150Avg. profit for 80% stores per month = 10 Millions Per storeLess profit stores: 20% = 30 storesAvg. profit for remaining 20% stores per month = 2 MillionsPer store.Let us assume optimization will increase revenue by 100%Each of 20% stores will be making a profit of 10 Millions,there is a 200% increase in the profit for these 30 stores.
  30. 30. International School of Engineering 2-56/2/19, Khanamet, Madhapur, Hyderabad - 500 081 For Individuals: +91-9177585755 or 040-65743991 For Corporates: +91-9618483483 Web: http://www.insofe.edu.in Facebook: http://www.facebook.com/insofe Twitter: https://twitter.com/INSOFEedu YouTube: http://www.youtube.com/InsofeVideos SlideShare: http://www.slideshare.net/INSOFE LinkedIn: http://www.linkedin.com/company/international- school-of-engineering•This presentation may contain references to findings of various reports available in the public domain. INSOFE makes no representation as to their accuracy or that theorganization subscribes to those findings.•The best place for students to learn Applied Engineering

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