Scc Presentation For Indianapolis 2 March 2012

647 views

Published on

Achieving the Multiplier Effect with Advanced Analytics and the SCOR® Model

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
647
On SlideShare
0
From Embeds
0
Number of Embeds
7
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • A good process orientation and organization are beneficial as is an integrated ERP system and availability of data, but analytics that drive specific improvements through higher quality decisions in less time in each of the processes of Plan, Source, Make Deliver are essential to winning supply chain performance.
  • A couple of releases back, the SCC added a dimension to the SCOR model of Performance Attributes, creating a matrix that relates Level 1 Metrics to those attributes. This can be taken farther as I have done in the Process/Value/Symptom Matrix (see Supply Chain Digest, September 13, 2011). However, to illustrate need and benefits of combining advanced analytics with SCOR, let’s take three Level 1 Metric examples.
  • Perfect Order Fulfillment is a composite performance metric built from several second level metrics as shown here. Let’s drill down into just one of the level 2 metrics, following the SCOR model.
  • Drilling down through Perfect Condition into Orders Delivered Defect Free, gives us a clear understanding of what to measure and of cause and effect. However, it does not tell us exactly how to “move the needle” on Orders Delivered Defect Free. In the case of purchased goods, this will involve a statistical evaluation of the performance of each vendor to determine the sampling and inspection procedures for each class of product from each vendor. There are also considerations of risk management. What are the risk scores of each vendor and product and how should they be computed in your industry? How sensitive are these scores to the factors that make them up such as lead time, availability of alternate sources, contribution to revenue, etc. In the case of manufactured goods, this will require statistical process control and all of the concomitant analytical six sigma tools. The metric is critical, as are the best practices to achieve it, but the excellent execution requires advanced and competent analysis to answer these questions.
  • Taking a look at cascading metrics another way, SCOR gives us many of the constraints that must be considered in this strategic metric which is defined as “The number of days required to achieve an unplanned sustainable 20% increase in production with the assumption of no raw material constraints.” To further illustrate the rather obvious need for advanced analytics in order to arrive at the “upside make flexibility” for your organization that is most strategically valuable, consider the interrelationships in this chart. We have highlighted a few of them with the red arrows. This does not even consider some of the relationships to other SCOR metrics not on this chart such as the expected range for future forecasts and the confidence ranges around those forecasts for each future time period.
  • Many of the decisions on the previous slide are inter-related and depend on your business strategy and the supporting value network design. Having done many of these projects successfully across numerous industries, we can say with confidence that every project we have done has been dependent on unique considerations. For example, some projects are very focused on the capabilities and configurations of the plants and need a model that operates at that level of detail while other projects are much more transportation, inventory, duty, and tax focuses, requiring an entirely different modeling approach.Takeaway:You cannot afford to take a cookie-cutter approach.
  • Scc Presentation For Indianapolis 2 March 2012

    1. 1. Achieving the Multiplier Effectwith Advanced Analytics and theSCOR® Model Arnold Mark Wells CPIM 2 March 2012
    2. 2. The End-to-End Analytics Team Superior performance through data-driven analysis Previously worked at top industrial / consulting firms  Hewlett-Packard  McKinsey & Co.  Procter & Gamble  Philips  Bain & Co.  QVC Recognized as thought leaders  Over 20 published articles:  Harvard Business Review  OR/MS Today  Sloan Management Review  Interfaces  Patents in many cutting-edge areas:  Supply chain risk management  Financial-operational flows  Joint capacity-inventory optimization  Supply contract structuring Analytical work from end-to-end along the supply chain  Supply Chain Strategy  Forecasting  Inventory Optimization  Network Design  Supply Planning  Risk Management  Capacity Planning  Pricing  Service Parts & Support© 2011 End-to-End Analytics, LLC Confidential and Proprietary Page 2
    3. 3. What Are Analytics? A continuum with four basic categories Reports Dashboards Periodically run  Frequently updated displays of performance metrics Pre-aggregated, pre-sorted  Displayed graphically, role-specific Limited user interactivity  Measure performance based on pre-aggregated data Examples: SAP Business Warehouse Reports and Business  Some user selection and drill-down capability Objects Crystal Reports, IBM Cognos Reporting, etc.  Can leverage hierarchies of metrics such as SCOR® Model  Examples SAP Business Objects Xcelsius, SAP SC Performance Management, IBM Cognos Dashboards, etc. Data Analysis Advanced Analytics Interactive software applications  Simulation Dynamically aggregate, sort, plot, and otherwise explore  Optimization and other approaches data, based on metadata.  Multi-criteria decisions which require the application of Fast visualization of data statistics and mathematical modeling and solving Examples: SAP Business Objects Web Intelligence and  Simple visualization of complex analyses. Business Explorer, IBM Cognos Analysis, etc.  Examples: forecasting and planning applications, simulation software, network optimization applications, optimization libraries and solvers, etc. Source: “On Analytics”, Arnold Mark Wells, Friday Forethought (http://wp.me/p1NcfT-T), 1 September, 2011© 2012 End-to-End Analytics, LLC Confidential and Proprietary 3
    4. 4. Where Is the Value? Advanced analytics will deliver more value Executive Report: Analytics: The new path to value MIT Sloan Management Review/IBM Institute for Business Value© 2012 End-to-End Analytics, LLC Confidential and Proprietary 4
    5. 5. How Do Analytics Relate to Metrics? From the Supply Chain Council Metrics quantify results and measure the success of an organization’s programs, operations, and investments. For example, project metrics inform the organization [of] whether the project is on time, on budget, and meeting goals. Supply chain metrics define and quantify the performance of the supply chain. Analytics . . . Analytics focus on predicting what will happen next and then optimizing the related business decisions. Source: SCC White Paper, Driving Sustained Improvements with Supply Chain Metrics and Analytics,© 2012 End-to-End Analytics, LLC Confidential and Proprietary 5
    6. 6. Metrics and Analytics Stated more simply . . . SCOR ® tells us what to do (process model and best practices) and what to measure (cascading metrics). That is the starting point and an important piece of a successful infrastructure. Analytics tells us how to do things better and how to measure most relevantly© 2012 End-to-End Analytics, LLC Confidential and Proprietary 6
    7. 7. The SCOR ® Model and Analytics Leverage and focusP. Trkman, et al., The impact of business analytics on supply chain performance, Decision Support Systems (2010), doi:10.1016/j.dss.2010.03.007 © 2012 End-to-End Analytics, LLC Confidential and Proprietary
    8. 8. SCOR ® Model Process model and best practices plus cascading metrics© 2011 End-to-End Analytics, LLC 8 Mar 14, 2011
    9. 9. The House of Value Foundation, Value Pillars, Business Performance Business Performance Competitive Advantage and Sustainable Value Add Cascading Metrics Better Decisions Process Model & Best Practices in Less Time Value SCOR ® Pillars Model Business Model Competency Infrastructure Foundation© 2012 End-to-End Analytics, LLC Confidential and Proprietary
    10. 10. Today’s Discussion How advanced analytics fit with SCOR ® Business Performance Competitive Advantage and Sustainable Value Add Cascading Metrics Better Decisions Process Model & Best Practices in Less Time Value Pillars Business Model Competency Infrastructure Foundation© 2012 End-to-End Analytics, LLC Confidential and Proprietary
    11. 11. SCOR ® Matrix Metrics and Performance Attributes© 2012 End-to-End Analytics, LLC Confidential and Proprietary 11
    12. 12. Example: Perfect Order One level drill-down© 2012 End-to-End Analytics, LLC Confidential and Proprietary 12
    13. 13. Example: Perfect Order Two level drill-down© 2011 End-to-End Analytics, LLC 13 Mar 14, 2011
    14. 14. Example: Upside Flexibility Many factors require analytical decisions© 2011 End-to-End Analytics, LLC 14 Mar 14, 2011
    15. 15. Example: Return on Working Capital Many factors require analytical decisionsManaging Revenue and Risk• Discount terms?• Risk? Pricing Decisions • By product attribute? • By customer attribute? Safety Stock Planning • Service Level Policy? • Multi-stage? • Postponement? Sourcing Decisions • Regional? • Single? • Sole? Better Decisions in Less Time • Risk? • Efficiency? Manufacturing Strategy • Enterprise Tools? • Location? • Specific Tools? • Automation? • Focus?© 2011 End-to-End Analytics, LLC 15 Mar 14, 2011
    16. 16. © 2012 End-to-End Analytics, LLC Confidential and Proprietary 16
    17. 17. Goals, Tradeoffs, and Options Advanced analytics optimize tradeoffs and evaluate options to meet goals Goals: Targets for improved performance Tradeoffs: Example: Long production run means efficiency, but more inventory and less agility Options: Limited resources mean choosing the best option, given constraints in cash, capacity, demand, etc.© 2012 End-to-End Analytics, LLC Confidential and Proprietary 17
    18. 18. How it Works In other words . . . Advanced Analytics Analysis More Data Inputs Value Demand Resources Costs, Yields, Recipes Modeling Operational Constraints Business Goals Scenarios© 2012 End-to-End Analytics, LLC Confidential and Proprietary 18
    19. 19. When Is Optimization Needed? When the tough decisions need to be made better and faster Facilities Capital √ Locate √ Invest √ Size √ Allocate √ Focus People Vehicles √ Procure √ Assign √ Schedule √ Schedule √ Route Inventory Equipment √ Acquire √ Move √ Locate √ Make √ Utilize √ Buy© 2012 End-to-End Analytics, LLC Confidential and Proprietary 19
    20. 20. Every Advanced Analysis Is Unique Example: Strategic supply network analysis and design Some projects are very focused on the capabilities Experience: and configurations of the plants and need a model that operates at that level of detail  Multiple projects in global hi-tech manufacturing and distribution WIP  Multiple projects in Raw FGI domestic baked good Factory production and distribution  One-offs in other verticals Customer DC Contributors to success: Supplier  Problem formulation DC  Right solution methodology  “Why” is often is as Other projects are much more important “what” transportation, inventory, duty, and tax focuses, requiring an entirely different modeling approach© 2011 End-to-End Analytics, LLC Confidential and Proprietary Page 20
    21. 21. New Analyses Are Emerging Example: Forecast Reality Check for DP and S&OP© 2012 End-to-End Analytics, LLC Confidential and Proprietary 21
    22. 22. Challenges with Analytical Decision SupportAnalytical competence/culture is essential to useful results Source: “What is the Analytical Competence Quotient of Your Organization?”, Arnold Mark Wells, Friday Forethought (http://wp.me/p1NcfT-33) 14 October, 2011© 2012 End-to-End Analytics, LLC Confidential and Proprietary 22
    23. 23. Metrics and Analytics Call to action Comprehending the past is no longer sufficient to compete and win You must understand the real “now”, the likely “next” or “nexts”, and the actions you need to take in order to maximize results© 2011 End-to-End Analytics, LLC 23 Mar 14, 2011
    24. 24. What’s Holding You Back? MIT Sloan Management Review Survey – Top 3 Reasons Executive Report: Analytics: The new path to value MIT Sloan Management Review/IBM Institute for Business ValueNone of these excuses diminish the critical need to make better decisionsfaster, and none of them should delay action. A quality consultancy can . . . 1. Understand how to use analytics and where to apply them in your industry 2. Augment your existing resources for a specific objective or project 3. Consult with a collaborative character and leave your internal capabilities enhanced through example and/or education© 2012 End-to-End Analytics, LLC Confidential and Proprietary 24
    25. 25. A Word on Prioritizing and Accelerating Process/Value/Symptom Matrix SCOR ® Performance Measures – Undesirable Business Symptoms Root Decision Processes Source: “Finding Value in Your Value Network?”, Arnold Mark Wells, Supply Chain Digest 13 September, 2011© 2012 End-to-End Analytics, LLC Confidential and Proprietary 25
    26. 26. Advanced Payoff for Advanced Analytics Engage with SCOR ®, but don’t forget the analytics© 2011 End-to-End Analytics, LLC 26 Mar 14, 2011
    27. 27. Important Decisions Are Integrated Pricing and supply chain decisions are interrelated Most power to raise price with least risk Price Leakage by Volume© 2008-2011 End-to-End Analytics, LLC Proprietary and Confidential 27 3/6/2012
    28. 28. Thank You! Supply Chain Expertise Analytical Know-how Arnold Mark Wells, CPIM Principal End-to-End Analytics, LLC 955 Alma St., Suite B Palo Alto, CA 94310 330 546 2404 mark@e2eanalytics.com Superior Performance Through Data-Driven AnalysisUnsurpassed Skill Uncommon Commitment Remarkable Results © 2012 End-to-End Analytics, LLC Confidential and Proprietary Page 28

    ×