Enablers for Maturing your S&OP Processes

Gregory L. Schlegel CPIM, CSP, Jonah VP Business Development gschlegel@shertrack.com

January 27, 2011

NEXT GEN S&OP Solution Platform

Lehigh University G 2

We Solve Complex S&OP Enterprise Issues... subject to Conflicting Objectives

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Multivariate digital models are recognized as the methodology of choice for analyzing complex manufacturing environments..

Financial Metrics

  Revenue & Earnings   Working Capital   Contribution Margin / Profit   Return on Assets

Production Metrics

  Overall Equipment Efficiency (OEE)

  Effective Capacity & Yield   Agility (responsiveness)   Variable Expenses

Market Metrics

  On-time Delivery (OTD)   Order Policies:

  Lead times   Minimum order size

  Product Mix   Contribution Margin / Profit

WHAT is Predictive Analytics??   Predictive analytics… encompasses a variety of techniques from

statistics, data mining and game theory that analyze current and historical facts to make predictions about future events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Predictive analytics is used in actuarial science, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields. But NOT Manufacturing or Supply Chain! One of the most well-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. A well-known example would be the FICO Score.

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Stochastic & Probabilistic Modeling

  Stochastic models:   Models where uncertainty is explicitly considered in the analysis

  Probabilistic demand models:   Statistical procedures that represent the uncertainty of demand by

a set of possible outcomes (i.e., a probability distribution) and that suggest inventory management strategies under probabilistic demands

Past & Present Clients (partial))

ROHM & HAAS A Wholly Owned Subsidiary of

S&OP Scenario Planning

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Probabilistic Simulation

Enough Information?

Risk Response

Plan

Supply chain Flow model

Base case data

Decision Logic

Probability Distributions

of uncertain factors

Probability of occurrence & magnitude of

disturbing events

Design of Experiments

Performance Measures

Feasible Tactical Plans

No Yes Determine “most appropriate”

values of decision variables

Our Scenario Outcomes/Dashboard

Scenario/Risk Response Planning

Scenario Probabilities of Occurrence

HIGH

LOW

Risk Associated with Occurrence LOW

HIGH

Take Scenarios & Build A Risk Response Plan

Scenario 1…..

1.  Internal Environment 2.  Objective Setting 3.  Event Identification

4.  Risk Assessment……. 1.  Type of risk & 2.  Magnitude

5.  Risk Response Plan….. 1.  WHAT to do 2.  WHO is responsible & 3.  HOW to manage the risk

6.  Control Activities 7.  Information & Communication 8.  Monitoring

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We Inject an ERM, Enterprise-wide Risk Framework into the S&OP Process: What”

Defining How To Bring Scenario Planning To An Actionable Level

Application – Clarity on how to apply and the level of planning capability needed to support. Define as future “what if” not variations on plans

Control Plan – How to maintain scenario integrity, ensure they are executable, and there is a continuous improvement process.

Probability of Plans and Assumptions

Scenarios for uncertain future conditions

Plan Variation What IF

This New Sophisticated Methodology Leveraged at Bayer

  Combining Design of Experiments (DOE) methodology with Digital Modeling leveraged the power of both methods.

  Bayer utilized SherTrack’s innovative predictive manufacturing technology to support Scenario Planning

  A cross-functional team, in collaboration with SherTrack, configured a SNAPPS™ digital model to simulate customer demand, scheduling and production output of a very complex compounding facility

Validate Shertrack

Model

Design DOE

Run 44

Scenarios

Evaluate Model

Predictions

Business Leader Review

Modify Supply Chain

Demand-Driven SC Execution Delivers Greater Operating Performance (Service, Capacity and Production Efficiency)

Better Service, Less Inventory, Fewer Setups

Source: Connie Conboy, Bayer MaterialScience, ISSSP Leadership Conference, May 2008

Bayer MaterialScience

262 products across 5 production lines

Improved OTD% & OEE% with Less Inventory Variability Reduced & Operated close to Optimum

Basell Advanced Polyolefins

Source: Larry Maynard, Basell NA, SPE Polyolefin Conference, 2/26/2006, Houston

Inventory – Service Exchange Curve Green Shaded area contains feasible operating set-points

DOE Scenario

Live Performance

SNAPPS with capacity increase

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Managing Uncertainty & Complexity’s Impact on Inventory

ROHM & HAAS A Wholly Owned Subsidiary of

  WHY RISK MANAGEMENT in S&OP?  Competitive advantage  Reputation & Branding  Rating agencies & positive analyst

commentary  Ability to reduce cost of capital

Scenario Planning & Risk Management Conclusions

  The combination of digital modeling, discrete event simulation and predictive analytics is a very powerful technique for Scenario Planning in the S&OP arena.

  Very compelling improvements are possible, especially in complex operations.

  Identifying, codifying and prioritizing risk within the S&OP enterprise process becomes a critical competitive advantage

  Developing a Risk Response Plan ensures long term sustainability of Operational Excellence

A powerful environment to evaluate alternatives without experimenting on customers & the bottom line!

SUMMARY……

  Companies NEED to evaluate their supply chain designs, policies & practices on a continuous basis rather than periodically

  Companies SHOULD segment/or Tier their customers and develop efficient and effective supply chains based on Total-Cost-to-Serve models

  Companies NEED to incorporate Risk Management tactics and methods into their S&OP processes

  Companies SHOULD support S&OP Scenario Planning leveraging Probabilistic Predictive Analytics

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Enablers for Maturing your S&OP Processes

Gregory L. Schlegel CPIM, CSP, Jonah VP Business Development gschlegel@shertrack.com

January 27, 2011