Demand Planning Leadership Exchange: 10 tips for SAP APO DP | Part 2


Published on

For More Information visit | Call 866-P4D-INFO | or Email

Whether you are just implementing SAP's Demand Planning Module or have been "Live" for ages, Part 2 of this 2 Part series will cover SAP DP Forecasting and design tips for all occasions.

Watch to learn practical tips and gain real world insights into these specific areas!
- Developing a Working Prototype
- Defining Roles & Responsibilities
- Managing the Forecast Process
- Exception Management
- Measuring Forecast Performance

Presented by Gary D. Griffith and Ed Neville.

Check out this webinar on-demand at

Published in: Business, Technology
1 Comment
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Demand Planning Leadership Exchange: 10 tips for SAP APO DP | Part 2

  1. 1. DEMAND PLANNING LEADERSHIP EXCHANGEPRESENTS: The web event will begin momentarily with your host: & Guest CommentatorApril 17th, 2013 plan4demand
  2. 2. Proven SAP Partner “Plan4Demand has consistently put in extra effort to ensure our Griffin  More than 500 successful SCP plant consolidation and demand engagements in the past decade. planning projects were successful.” -Scott Strickland, VP Information Systems  We’re known for driving measurable Black & Decker results in tools that are adopted across our client organizations.  Our experts have an average of 10 years supply chain experience.  Our SAP team is deep in both technology and supply chain planning expertise; have managed multiple implementations; have a functional specialty.
  3. 3. 3 Session 1 explored decisions that effect the technical design of the your system Guides you toward design aspects that builds a strong DP foundation A phase II redesign for these points will be painful. Session 2 has a functional slant that will allow you to better leverage the application Sets the stage for a successful go-live Guides you on successful forecast performance tracking and improved process management
  4. 4. 4 #6 Developing a Working Prototype #7 Manage the Forecasting Process #8 Change Management – Start with Roles & Responsibilities #9 Exception Management #10 Forecast Performance
  5. 5. 5 Improve the quality of your decisions and decrease idle time between decisions by building a “Working Prototype” Planning solutions are difficult to depict in PowerPoint and often clients face change management and alignment issues when they first see the configured solution during testing While a working prototype expands the initial design phase of the project it provides a payout throughout the rest of the project.  Helps mitigate risk and institutionalizes a learning organization better prepared for system hand-off  Moves people more quickly from a “Look & Feel” focus to a “Use of Data” focus  Provides a better understanding of how transactions are interrelated and the impact on the overall system design
  6. 6. 6 Client used prototype Client did not use prototype  Used DP Bill of Material  Interactive demand planning capabilities which could have graphical capabilities were resulted in a delayed design expected when most Data Views decision were built in a tabular format  Allowed planners opportunity to  Less efficient Data Views existed see the configured solution and because user navigational needs assess alignment with business not well understood (e.g. drilling needs down on selected key figures)  Built early negative attitudes  Facilitated Fit/Gap analysis resulting from a perceived  Started the user adoption inefficient design process early and kept team  Delayed Go Live caused by users engaged advancing the learning curve, high risk of reverting to old ways
  7. 7. 7 The process, by it’s nature of being part art and part science is vulnerable to subjectivity. The process needs to be tightly defined and managed Understand the drivers of uncertainty and apply focus to those critical areas  Cleaning historical demand for one time events or other anomalies  Managing statistical forecasting exceptions and tuning models  Incorporating judgmental input and measure/understand the impacts such as potential bias This focus reduces clutter in the forecasting process and leads to improved forecast results
  8. 8. 8 Impacts of maintaining structure in the forecasting process: Reduces likelihood of bias entering the forecast process Builds a high level of credibility in the forecast  Remember since the output of forecasting is an estimate (i.e. “best guess”) there is a certain amount of “leap of faith” that everyone takes. Allows for faster development of action plans to close “forecasting gaps”, such as significant gaps to the Budget / Quarterly Revisions  Forecast is better understood and respected Creates an ecosystem for sustainable change  Shapes the way people think about and approach the forecasting process, addressing issues and making trade off decisions Drives standardized workflow – locally & globally
  9. 9. 9 Focused approach achieved by aligning planning book / data views with the key steps in the forecasting process:  Clean History Planning Book  Statistical Forecast Planning Book  Consensus Forecast development, validation and modifications Planning Book Less art & more science is better at this stage It’s a building block process that is standards-driven to ensure quality results
  10. 10. 10 Our Demand Planning Process Roles & Responsibilities are: Answer on your screen A. Not Well Defined B. Defined For Supply Chain Only C. Defined For All Demand Planning Process Key Stakeholders, But Lack Adherence D. Defined, Well Structured & Adhered To E. Do Not Know
  11. 11. 11 Change Management is an area that needs constant care and feeding. The most effective ways to ease the transition to a new or improved DP process are: Have a solid understanding of the core competencies required for the Demand Planning Process Ensure roles and responsibilities are defined or redefined properly to meet the goals of the new or improved design Assess your demand planning team’s fit to these roles and responsibilities  Identify team’s strengths and improvement opportunities
  12. 12. Gain an understanding of the core competencies yourcompany needs to succeed at Demand Planning Determine the team’s process and technology Consensus Based strengths and identify opportunities for Demand Planning improvement. Examples include:  Collaboration & Generation of the Consensus APO Analytical Forecast Demand Skills Planning  Improved Statistical Forecasting Capabilities  Effective use of exception based management Mainly accomplished through attrition hire demand planners familiar with DP best practice process knowledge as well as having prior APO DP experience. This can be a major asset throughout the implementation.
  13. 13. 13 It is not uncommon for projects to shortcut or eliminate efforts around Roles and Responsibilities  You see this happening more if the tasks are to redefine the roles and responsibilities  For example, redefining existing position roles and responsibilities can be quite the endeavor as HR will need to be involved and senior management approval is often required  The impact of not doing this however is on the ability to measure demand planner performance and adherence to the new process Take the time to define or redefine in order to meet the new or changing goals  Captures “Tribal Knowledge” and “Know-How”  Rationalizes common best practices and captures critical knowledge to pass on to others in a systematic way  Drives a “change in thinking” within the team from a production culture to a performance culture through understanding “end game” and “personal measurements”  Engages the team to think beyond their specific “silo area”
  14. 14. 14 Build transparency in R = Responsible, A = Accountable, C = Consulted, I = Informed the communication of Activity Demand Planner Sr. Demand Demand Sales Planner Manager Finance Mktg SPS CCID the Roles and Drive highest level of item/location forecast accuracy Publish regular forecast accuracy updates and forecast R R R C, I A A C, I actualization to cross functional partners to drive Responsibilities consensus on problem areas and messaging Understand and challenge the assumptions of R R R C, I A A C, I assessments or changes to the forecasts based on cross  Clear Job Descriptions functional inputs Inquire about forecast changes and request for R R A C, I C, I C, I C, I and use of RACI additional investigation if changes do not meet expectations A A A C,I R R R Matrix are tools at Identifying the impact of trade spend and any assumptions of that impact on sales lifts A A C, I A C, I R C, I your disposal Provide DSMP volumetric impact by customer A A C, I A C, I R C, I Provide merchandising calendars and explanation to specific promotions and sales lifts A A C, I A R C, I C, I  Do not fear an Provide recommendations for overriding changes in forecast inputs based on understanding of implications evolving process, and assessments of impacts R R A C, I C, I C, I C, I Gain consensus and alignment of forecast inputs to be entered into APO DP from cross functional team R R A C, I A A A allow it to change and Provide input to Demand Review meeting based on pre-defined templates R R A C, I C, I C, I C, I become a driver for Facilitate Demand Review meeting to Brand Manager Make the decision on the forecast volume based on A A R C, I C, I C, I C, I enhancing supply facts and consensus from respective cross functional inputs. R R A C, I C, I C, I C, I chain performance
  15. 15. 15 Any DP roles and responsibilities assessment needs to cover the analytical and statistical competency of the team to determine how to best focus training and / or skill enhancements Do not be afraid to bring in a statistician to help analyze historical demand patterns and recommend model selection and tuning approaches for go live and post go live optimization
  16. 16. 16 Our Exception Based Management Process is Best Described As Being: Answer on your screen A. Not Well Defined B. In General More Reactive C. More Proactive D. No Exception Based Management Used
  17. 17. 17 Become reliant on exception management to surface future issues today. Look for recent trends in alerts  For example, are we consistently under or over forecasting for recent time periods implying potential forecast bias? Do not overreact to an exception but rather look for repeated patterns (i.e. pattern recognition)  Do not be afraid to use Excel pivot tables to help identify patterns in alerts by looking at product and customer groupings
  18. 18. 18 Build a strong Management by Exception (MBE) process Ensure the process is skewed towards proactive MBE which anticipates exceptions within the forecasts  Maintain the highest focus in this area Continue to use reactive MBE or ones which deal with detection of exceptions that already have occurred  If proper focus is delivered to proactive MBE the need for the reactive MBE will lessen over time Example of identifying potential obsolete products
  19. 19. 19 Statistical forecast thresholds are defined via forecast alert profiles and contain Information / Warning / Error status capabilities to help with prioritization Define alert threshold values that result in a manageable number of alerts being reviewed by a demand planner  One of the major pain points expressed by planners across industry verticals is “too many alerts”  For macro driven alerts the macro itself may need to be modified which involves some configuration  A working prototype would provide good insight on alert threshold tuning needs prior to go live  Use of pattern recognition is critical
  20. 20. 20 Build flexibility into your Forecast Performance Baseline & Consensus Forecast Measurement & Tracking Process 7000 6000 Provide the ability to 5000 4000 measure all inputs that 3000 make up the consensus 2000 forecast number 1000 0  Statistical forecast Sep-11 Sep-12 Sep-13 Mar-11 May-11 Mar-12 May-12 Mar-13 May-13 Jul-12 Jan-11 Jul-11 Jan-12 Jan-13 Jul-13 Nov-13 Nov-11 Nov-12 performance to get the historical / objective Baseline Fcst Consensus Fcst perspective  Sales & Marketing inputs/overrides to gauge where working well and where to focus
  21. 21. 21 Track performance at the input/influence level of aggregation Provide drill down capability that will allow you to isolate the contributors of the error  The statistical forecast is often measured at multiple levels of the forecast hierarchy for the supply chain - Item x Location for Distribution Planning - Item for Manufacturing & Purchasing  An example from a Food company for Sales & Marketing inputs: - Sales: Item x Key Account for next 3 months - Marketing: Item for months 3 -18
  22. 22. 22 FVA is defined as the change in a forecasting performance metric (whatever metric you happen to be using, such as MAPE, forecast accuracy or bias) that can be attributed to each particular step and participant in your forecasting process FVA analysis also compares both the statistical forecast and the analyst forecast to what’s called a naïve forecast In FVA analysis, you would compare the analyst’s override to the statistically generated forecast to determine if the override makes the forecast better In this case, the naïve model was able to achieve MAPE of 25% • The statistical forecast added value by reducing MAPE five percentage points to 20% • However, the analyst override actually made the forecast worse, increasing MAPE to 30% • The override’s FVA was five percentage points less than the naïve model’s FVA, and was 10 percentage points less than the statistical forecast’s FVASource: Michael Gilliland SAS Chicago APICS 2011
  23. 23. 23 Measure lagged forecast error and lagged For a chemicals company this meant: forecast bias to - Lag1 (i.e. Forecast for April; determine what is not Developed in March) for working and what are the distribution and manufacturing implications - Lag 6 (i.e. Forecast for September;  The lags must reflect the Developed in March) for Purchasing distribution, manufacturing and purchasing lead time requirements Enables ongoing improvement of forecast performance as we measure not only the statistical forecast but also all of the intelligence components and at the levels provided
  24. 24. 24 Adults learn in a very different way, the new social media is training us to communicate in short rapid bursts  With a prototype that builds out throughout the project we are able to capture and validate changes and move at a rapid pace The Forecast Process, by it’s nature of being part art and part science is vulnerable to subjectivity and an easy process to run-off course  Maintain enough control to enable consistency without stifling change An Exception Management process is very similar to a diet. Hard to start, keep on track and moving, but pays off in the long run Forecast performance is key to a successful Demand Planning process. Solid KPIs will allow you to insure continuous progress. Follow the trend line not the headline
  25. 25. Join us on LinkedIn: Demand Planning Leadership ExchangeFollow us on Twitter: @Plan4Demand THANK YOU! Save the Date or Click Below to Register! S&OP HANA | May 8th Presented by Andrew McCall, S&OP Solution Leader If you use SAP to Plan… Think
  26. 26. For Additional Information or a PDF Copy Contact: Jaime Reints 412.733.5011