Attaining Supply Chain Analytical Literacy. Sr. Director of Analytics Wal Mart (Bentonville, AR)

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1 Attaining Supply Chain Analytical Literacy Rhonda R. Lummus F. Robert Jacobs Indiana University Bloomington Kelley School of Business Sr. Director of Analytics Wal Mart (Bentonville, AR) The Senior Director of Analytics is responsible to ensure Sam's Club has best-in-class analytics, measurement, and data strategies to inform enterprise-wide id business initiative iti focused on enhancing the overall member experience. Lead the design and development of analytical projects designed to understand key business behaviors such as member acquisition, retention, and engagement; member lifetime value; member satisfaction; and drivers of loyalty Manage and lead advanced analyses to assess relationships and patterns within member data, and create tools which h can be leveraged to build actionable strategies Direct development and integration of advanced analytics, statistical modeling, and optimization 1

2 SAS Analyst, Charlotte, NC Serve as an analytical consultant to various merchandising and supply chain initiatives with a primary role of supporting the strategic and tactical decision-making process General Summary: The SAS Analyst serves as an analytical consultant to various merchandising and supply chain initiatives with a primary role of supporting the strategic and tactical decision-making process, specifically as it relates to leveraging statistical concepts along with general business intelligence to derive effective crossfunctional business solutions. o s Moreover, e this position o will assist st the Director of Merchandise Forecasting with day-to-day project development, which includes compiling data, conducting exploratory data analysis, building models, analyzing results, and presenting technical findings to a non-technical audience. Agenda Introduction to supply chain analytics Supply chain questions analytics can address Necessary analytic skills Case exercise using supply chain analytics Acquiring analytic skills Adding analytics skills to operations and supply chain training Summary 2

3 Business Analytics The process of collecting, storing, and analyzing data in a way that enables better business decisions Business intelligence the analysis and organization of historical data and the delivery of meaningful business information in convenient forms Executive dashboards high level measures of corporate performance On line information allows executives to drill down to details David Carr Combines Data Mining and Analysis Data Mining Gather data from customer touch points and other sources Mine the data for patterns and priorities Analytics Decision Optimization Predictive Analytics Forecasting Statistical Models What s the best that can happen? What will happen next? What if these trends continue? Why is this happening? 3

4 Supply Chain Analytics Definition The ability of supply chain professionals to analyze increasingly larger sets of data using proven analytical and mathematic techniques (regression analysis, stochastic modeling, linear and non linear optimization, etc.) allowing them to spot patterns and correlations, perform comparisons and highlight opportunities. Real time data is used to model and predict the future. Leverages investments in enterprise applications, web technologies, data warehouses and external data Why Analytics? Accenture surveyed 500 UK and U.S. blue chip organizations Two thirds respondents cited getting their data in order as an immediate priority Long term develop the ability to model and predict behavior and actions and make decisions i in real time, based on the analysis at hand Accenture, Separating the Masters from the Majority: Supply Chain Optimization in the New Analytics Economy 4

5 IT Companies Lead the Initiative IBM announced the creation of the Services Innovation Lb(J Lab (July 2011) A global lab of 200 technology experts handpicked from around the company The lab will accelerate the expansion of realtime analytics and software automation in both hibm' IBM's technology services offerings and its global services capabilities IT Companies Lead the Initiative IBM Services Innovation Lab Will invent new ways to tightly integrate analytic services with business processes The goal create new types of services and applications that can be injected quickly into client accounts Example IBM s Tax Collections Optimizer Uses IBM patented analytics technology to help governments identify the most effective and efficient methods to collect taxes from delinquent debtors 5

6 Agenda Introduction to supply chain analytics Supply chain questions analytics can address Necessary analytic skills Case exercise using supply chain analytics Acquiring analytic skills Adding analytics skills to operations and supply chain training Summary Questions Analytics Can Address How to optimize sourcing and increase supplier reliability (provide details on supplier quality performance, cost and delivery)? How to improve inventory levels (track obsolescence, optimize on hand, utilize safety stock models, evaluate inventory movement patterns and order policies)? How to monitor spending (by item category or supplier)? Howto evaluate markdowns? How to evaluate markdowns? How to evaluate material outages? How to analyze quality failures? 6

7 Questions Appropriate for Analysis Can we consolidate our supply base without increasing risks to our supply chain? Whatwould bethe impact ofadding anothersales promotion on product profitability? What will be the impact of changes in fuel prices, weather or a competitive promotion on product demand? How can we minimize our inventory carrying costs without affecting customer service? How can we identify product quality issues earlier to minimize our warranty claims? Agenda Introduction to supply chain analytics Supply chain questions analytics can address Necessary analytic skills Case exercise using supply chain analytics Acquiring analytic skills Adding analytics skills to operations and supply chain training Summary 7

8 Necessary Analytic Skills Database Queries, extraction, sorting, etc Data mining tools (large files, complex formats) Graphics Chart construction Statistics Descriptive, correlation, regression, discriminate analysis, etc. Math Modeling, linear and non linear optimization, search procedures, queuing, simulation, etc. Necessary Technology Data availability Problem size Example problem total cost modeling of a supply chain network Customer locations Order size and frequency Transport costs Transport modes and vehicle types Distribution Center size, location, resources, costs... Service level requirements Factory and supplier locations Ports of entry for imported products 8

9 Customer DC Allocation Agenda Introduction to supply chain analytics Supply chain questions analytics can address Necessary analytic skills Case exercise using supply chain analytics Acquiring analytic skills Adding analytics skills to operations and supply chain training Summary 9

10 An Analytic Exercise The Disaster Drug Allocation Decision Three Scenarios: 1. Little data Seat of the pants decisions 2. Tracking status Limited information decisions 3. Simple model driven by data Model based decisions Agenda Introduction to supply chain analytics Supply chain questions analytics can address Necessary analytic skills Case exercise using supply chain analytics Acquiring analytic skills Adding analytics skills to operations and supply chain training Summary 10

11 Analytics Program Indiana University MBA in Business Analytics PhD in Decision Si Sciences Built around data and application modeling Excel for modeling along with more advanced tools such as SAS, SPSS, and CPLEX Includes optimization, computer simulation, decision analysis, statistics, predictive modeling, data mining and visualization, applied probabilistic modeling, and artificial intelligence Analytics at Other Universities Masters Degree in Business Analytics Drexel MastersDegreein Business Analytics University of Tennessee Supply Chain Analytics: Matching Supply with Demand course MBA Ohio State Supply Chain Analytics course MBA University of Michigan Supply Chain Analysis undergraduate course Auburn University 11

12 Agenda Introduction to supply chain analytics Supply chain questions analytics can address Necessary analytic skills Case exercise using supply chain analytics Acquiring analytic skills Adding analytics skills to operations and supply chain training Summary Map of Topics/Decision Approach Simple Models: Forecasting, Capacity, Layout, Location, Quality, Inventory, Aggregate Planning, MRP, Scheduling Setup then solve a model Simple Processes: Manufacturing, Service, Planning and Control Flow chart then improve Complex Decisions: Strategy, Process, Inventory, Scheduling hdl Setup multi step model dlh then simulate or analyze with scenarios 12

13 Conventional Approaches to Teaching Practice and Test and Cases Practice and Test Present a model, dlshow an example, practice with problems Case Introduce a dilemma, supply extensive background, provide some qualitative issues and quantitative data, make a decision The Analytics Teaching Approach Analytics Present a problem requiring a decision, i provide quantitative data, analyze the data, obtain an analytical result, discover qualitative issues, make a decision 13

14 Example Sets of Analytic Cases 1. Supply Chain Analytics (SCA) Will show some examples of these next 2. Operations and Supply Chain Analytics Common Characteristics Focus on a specific decision Data driven Significant strategic issues Supply Chain Analytics Cases A common company W.W. Grainger leading supplier of Maintenance, Repair and Operating (MRO) products 900,000 products, 3,000 suppliers, 1.8 million customers. Product scope: industrial adhesives used in manufacturing, to hand tools, janitorial supplier, lighting equipment, and power tools. Four cases involving North American sourcing and distribution from China and Taiwan. 14

15 SCA Case One China/Taiwan Logistics Suppliers 4 port locations 2 U.S. US ports 20 vs. 40 containers Direct ship vs. through consolidation centers. Utilization of container space, shipping cost, port operations. Decision: Should consolidation centers be expanded to increase efficiency? SCA Case Two US Distribution 2 U.S. ports Kansas City DC 9 US Warehouses Rail and Trucks Shipping cost, DC cost. D i i Sh ld 2 d DC b b il i L Decision: Should a 2 nd DC be built in Los Angeles to prevent doubletransportation? 15

16 SCA Case Three Optimal Distribution Center Location Optimal site(s) for 1 DC, 2 DCs, etc. Dynamic demand Shipping cost, DC costs, inventory. Decision: How many DCs should be used and where should they be located to service North America? SCA Case Four Could domestic production be attractive? Given expected changes in wages, port costs, fuel, etc. Logistics costs, purchase costs (China vs. U.S.). Decision: How much change would make Decision: How much change would make our current sourcing strategy obsolete? 16

17 Operations and Supply Chain Analytics Cases Nine cases Similar structure Quick minute student preparation Explain the course material extended example Companies/settings familiar to students t Gradable Company Support for Analytics Education Applying analytics through a case competition W.W. Grainger Inc. Undergraduate competition sponsorship included: Assisting in writing a real world case Providing judges for the final round and selecting the winning team Hosting a reception for the students Providing prize money for the top teams. 17

18 Grainger Case Competition Case task Provide recommendations on how Grainger can improve its Far Eastern supply chain Consider both qualitative and quantitative aspects Show the cost implications of your recommendations as they would play out over the next five years Make (reasonable) assumptions for your quantitative analysis Complete a sensitivity analysis: How would your analysis change in a best case or worst case scenario? Agenda Introduction to supply chain analytics Supply chain questions analytics can address Necessary analytic skills Case exercise using supply chain analytics Acquiring analytic skills Adding analytics skills to operations and supply chain training Summary 18

19 Summary Using supply chain analytics results in better decision making through: Using real time data to model and predict the future Leveraging investments in enterprise applications, web technologies, data warehouses and external data The modeling tools aren t new, but the access to information has opened the door for new uses of the models Final Thoughts Through h predictive analytics, lti decision i makers have the tools to keep their supply chain fit, agile and competitive. In the new economy, insight based on hindsight is, quite simply, not good enough. Frode Huse Gjendem, Sr. Director of Supply Chain Analytics, Accenture Analytics 19