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Sales and Operations Planning Las Vegas 2011
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Enablers for Maturing your S&OP Processes
Gregory L. Schlegel CPIM, CSP, Jonah VP Business Development [email protected]
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 [email protected]
January 27, 2011