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Presentation by Lora Cecere at the Foundation of Strategic Sourcing

  1. Improving Supply Chain Effectiveness March 2, 2020
  2. I Am A Social Scientist
  3. I Write for the Supply Chain Leader
  4. About Lora Cecere Founder of Supply Chain Insights “LinkedIn Influencer” Guest blogger for Forbes Author: Bricks Matter (2012), Supply Chain Metrics That Matter (2014), and Shaman’s Journal (2014-19) Partner at Altimeter Group (leader in open research) 8 years Leading Analyst Teams at Gartner and AMR Research 8 years Experience in Marketing and Selling Supply Chain Software at Descartes Systems Group and Manugistics (now JDA/Blue Yonder) 15 Years Leading teams in Manufacturing and Distribution for Clorox, Kraft/General Foods, Nestle/Dreyers Grand Ice Cream and Procter & Gamble. Contact Information: • Email: • Blog: (18,000 pageviews/month) • Forbes: • Twitter: (9,600 followers) • LinkedIn: (318,000 followers) • LinkedIn Influencer:
  5. Technology In Transition Traditional supply chain thinking is: Functional Linear Transactional Inside-out Moving forward, the thinking becomes: Cross-functional Non-linear Focus on sense, learn and act Outside-in
  6. What Is An Effective Supply Chain? Outcome: Performance Improvement Resilience Goal: Efficient Responsive Agile Fit for Function
  7. Email from a Financial Analyst I think one of the huge problems is that US-centric food companies (Kellogg, General Mills, Smucker’s, Conagra etc.) are working on a “if all you have is a hammer, all you see is nails” problem – they have been making boxed, canned and otherwise shelf- stable packaged food for well over a century but now consumers and retailers are looking for fresh foods and they don’t know how to adapt. Certainly, Campbell’s foray into its c-Fresh business ended in tears. We have the rise of new channels, particularly eCommerce. And again, companies aren’t sure how to configure themselves to play profitably in there. Meanwhile, retailers are getting far more sophisticated in their analytical capabilities, which is reducing the importance of the category captain role for the largest CPG brands in each category. Everything is getting faster – new products are introduced and eliminated more quickly, better analytics are enabling better real-time feedback on what should go where on a shelf and at what price at a much more granular level – may be down to individual stores. Yet, companies are blind to these insights.
  8. Supply Chains Are Stuck
  9. What I Believed…. …companies spent 1.7% of revenue on information technology investments and drove improvement.
  10. I Was Wrong….
  11. [CELLRANGE] [CELLRANGE] 4.2 4.7 0.07 0.08 0.09 0.10 0.11 0.12 InventoryTurns Operating Margin Apparel Retail Best Scenario Apparel Retail 0.10, 4.51 Average (Operating Margin, Inventory Turns) Source: Supply Chain Insights LLC, Corporate Annual Reports 2010-2018 from YCharts Apparel Retail Operating Margin vs. Inventory Turns (2010 - 2018)
  12. [CELLRANGE] [CELLRANGE] 11.0 12.0 13.0 14.0 0.02 0.03 0.04 0.05 InventoryTurns Operating Margin Food Retail Best Scenario Food Retail 0.04, 12.76 Average (Operating Margin, Inventory Turns) Source: Supply Chain Insights LLC, Corporate Annual Reports 2010-2018 from YCharts Grocery Retailing Operating Margin vs. Inventory Turns (2010 - 2018) Supply Chain Insights Global Summit 2019 Twitter: #Supplychain2030
  13. [CELLRANGE] [CELLRANGE] 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 InventoryTurns Operating Margin Personal Products Best Scenario Personal Products 0.07, 2.13 Average (Operating Margin, Inventory Turns) Source: Supply Chain Insights LLC, Corporate Annual Reports 2010-2018 from YCharts Personal Products Operating Margin vs. Inventory Turns (2010 - 2018)
  14. [CELLRANGE] [CELLRANGE] 4.5 5.0 5.5 6.0 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 InventoryTurns Operating Margin Chemical Best Scenario Chemical 0.07, 5.20 Average (Operating Margin, Inventory Turns) Source: Supply Chain Insights LLC, Corporate Annual Reports 2010-2018 from YCharts Chemical Operating Margin vs. Inventory Turns (2010 - 2018)
  15. [CELLRANGE] 2018 [CELLRANGE] 2018 4.0 5.0 6.0 7.0 8.0 0.08 0.09 0.10 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.20 0.21 0.22 InventoryTurns Operating Margin Procter & Gamble Co. Kimberly-Clark Corp. PG 0.20, 6.05 Average (Operating Margin, Inventory Turns) Source: Supply Chain Insights LLC, Corporate Annual Reports 2006-2018 from YCharts KMB 0.14, 6.13 Kimberly Clark and P&G Operating Margin vs. Inventory Turns(2006 – 2018) Best Scenario
  16. [CELLRANGE] 2018[CELLRANGE] 2018 4.0 5.0 6.0 7.0 8.0 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.26 InventoryTurns Operating Margin Nestle SA Unilever PLC UL 0.15, 6.46 Average (Operating Margin, Inventory Turns) Source: Supply Chain Insights LLC, Corporate Annual Reports 2006-2018 from YCharts NSRGY 0.15, 5.17 Example: Nestle & Unilever Operating Margin vs. Inventory Turns (2006 – 2018) Best Scenario
  17. Focus on A Balanced Scorecard Focused to Drive Value
  18. Functional Metrics Shift to Focus on Reliability Plan Sell Deliver Make Source Forecast improvement (FVA) Minimization of slow and obsolete inventory Inventory mix quality Promotion timing adherence Forecast bias On time shipments Orders shipped full Hands-free orders First pass yield Schedule adherence On time materials to plants Schedule adherence Supplier quality Define a Balanced Scorecard Align Functional Metrics to Reliability Redefine S&OP
  19. Supply Chains Are Stuck
  20. Are Supply Chains Effective? ____________________________________________________________________ Source: Supply Chain Insights LLC, Sales & Operations Study ((Mar-May, 2019)) Base: HAVE A S&OP PROCESS -- Total (n=107) Q29. For each of the following pair of words, please pick the one that best describes your company’s supply chain today.  Higher than other answer at 90% or higher level of confidence Aligned Risk-taking Agile Proactive Controlled Working well Outside-in 40% 39% 37% 37% 37% 33% 28% 27% 29% 23% 33% 25% 45% 31% Independent silos Cautious Fixed Reactive Uncontrollable Room for improvement Inside-out Supply Chain Descriptors* WORS E BETTER Strengths Challenges
  21. Organizational Silos Are Not Aligned
  22. US Productivity Stopped in 2004
  23. Inventory Levels Higher Now Than 2007
  24. Normal Distribution SKU/L SalesVolume Anything But Normal Complexity: The “Long Tail” Is Growing The Long Tail of the Supply Chain Increases Complexity Reducing Margin, Increasing Inventory Turns and Reducing Asset Utilization.
  25. Supply Chains Are Stuck
  26. Why? • Belief in Best Practices • Focus on Shiny Objects • Focus on Functional Excellence • One Size Fits All • Inside-Out
  27. Why? The Reasons We Are Stuck How Do We Unstick the Supply Chain The How Belief in Best Practices Recognize These as Historic Practices. Learn to Unlearn to Rethink Outcomes. Focus on Shiny Objects Test but Verify. Fail Forward. Functional Metrics Drive Balance Sheet Excellence. Focus on Cross-functional Alignment and Focus the Functions on Reliability. One Size Fits All Multiple Supply Chains. Build Capabilities based on Rhythms and Cycles. Inside-out Outside-in. Build Value Networks.
  28. A Case Study • One out of two orders shorted. Deductions for issues on case fill rate expected to be 9M$/year in 2020. • Order shortages not measured. Five ERP systems. Email and Excel environment. • Took 6 months to begin the process.
  29. Order Short Data 5% 45% 5% 41% 4% Packaging Plant Underperformance Scheduling Quality Issue Demand
  30. View Before Sprint
  31. Demand Analysis
  32. Managing Multiple Supply Chains Supply Chain United States Canada COV 0-<.5 .5-1.25 >1.25 0-<.5 .5-1.25 >1.25 Make to stock 83.6% of volume 83.5% of items 5.1% of volume 11% of the items 83.3% of volume 59.8% of items 14.7% of volume 27% of items New product launch 10.8% of volume 4.8% of items 1.8 % of volume 12.8% of items Make to order .5% of volume .7% of items .1% of volume .2% of items
  33. Tactics Sell Deliver Make Source Make-to-Stock Item/location forecasting Demand Sensing Allocation VMI Safety stock buffers Full truck load shipments Cycle stock planning using rhythm wheel logic JIT/Reliable supply Potential for contract manufacturing Phase-in Phase Out Attribute-based forecasting Use of customer data Air/small shipments Attribute-based planning Agile manufacturing work centers Platform rationalization Postponement local sourcing Make-to-Order ATP Air from factory Shorten cycles Agile manufacturing work centers Platform rationalization Postponement DDMRP Managing Multiple Supply Chains
  34. Digital Supply Chain: Transforming the Atoms and Electrons of the Supply Chain through the Confluence of new Capabilities/Technologies. Digitization: Making signals and processes digital. What Does Digital Transformation Mean for You? There Is No One Definition
  35. Data Needs to Move Securely Through Value Networks Clouds Streams Pools
  36. Analytics Adoption
  37. Data Types
  38. The Need to Listen
  39. Evolution of Cognitive Computing
  40. Progressing in the World of Analytics Known/Known Unknown/Unknown Visualization Flexible/Easy to Use by Business Leaders Discovery/Learning Questions The Questions You Know to Ask The Answers You Need, but the Questions You Don’t Know to Ask Data Known Unknown
  41. Answers the Questions You Do Not Know to Ask
  42. Data Inputs Engines Plan Outputs Align Engines with Outcomes Planning Master Data
  43. Evolution of Insights
  44. Impact of Localized Assortment
  45. Looking Forward In the future, companies will not compete company against company, but value chain to value chain. Today’s focus is on organizational efficiency is making value networks fragile and less resilient.
  46. What Is A Network?
  47. Current State
  48. Maturity
  49. Vendor Master Case study Task: • Research ALEIs for 24,245 suppliers in 81 countries Results: • ALEIs could be verified for 17,111 suppliers (71%) in 33 countries • 7,134 (29%) of the suppliers will need to be asked for more information to verify their legal status • Of the 17,111 verified suppliers 7,060 (41%) were duplicate records ALEIs researched 24,245 100% ALEIs verified 17,111 71% Further information required from vendor to validate legal entity 7,134 29% Verified ALEIs 17,111 100% Verified Legal Entities 10,051 59% Duplicate records linked to master Legal Entity records 7,060 41%
  50. Can Blockchain Help?
  51. Is Blockchain the Answer?
  52. Write once and use company and contact information many times.Community Directory • Reduce onboarding through once source of data. Blockchain redefines visibility and track and trace.Traceability • Confluence of Blockchain and Spark/Internet of Things Redefine Lineage. Bitcoin and Blockchain disintermediate traditional banking.Supply Chain Finance • Emergence of the supply chain digital wallet. • An alternative to EDI. Cognitive computing eliminates the need for master data management and standards.Interoperability • Data is mined through patterns and translated for context through cognitive computing. Shift from standards to process canonicals. What Could Change?
  53. Integration Data portability and synchronization Linear flows Network bidirectional flows Functional focus Need for plan, source, make and delivery to work together Master data Need for authoritative identifiers Insights?
  54. What Can We Learn? 1. Leadership matters. There is no significant difference in technologies deployed. 2. Outperforming supply chains are fit for purpose. They change with shifts in business strategy. 3. 67% of supply chains drive performance in single metrics throwing the supply chain out of balance and reducing value. 4. Companies focusing on innovation outperform. There is a clear mission and understanding of the customer. 5. Question the status quo. Only 3% of companies outperform their peer groups. 6. We cannot save our way to value.
  55. ENGAGE WITH US! Visit Us Online Follow Us on Twitter @scinsightsllc @lcecere Attend a Live Event
  56. What To Learn More?

Notes de l'éditeur

  1. Why the long tail is getting bigger