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DEMAND PLANNING LEADERSHIP EXCHANGEPRESENTS:                    The web event will begin momentarily                      ...
   Goals for the Session   A Definition   The Two themes       Business Classification           Case Study: Wine & S...
   Goal:       Marrying the concept of Manufacturing definitions of inventory (i.e. ABC        product classification) w...
4       Most Classification is a form of pattern recognition in which we        attempt to assign for each input value to...
   Traditionally Business has a fragmented approach to    Classifying products depending on function       Finance : Cos...
   To answer this challenge we need to do the following:       Get a C-level sponsor if possible (CEO CFO etc)       Ma...
   Has its origins in Operations and Inventory control costing   Uses Pareto & ABC terminology       Current on–hand qu...
   “A” items are the most critical ones. These items require:           tight inventory controls           frequent rev...
   “C” items have the least impact in terms of warehouse activity    and financials, and therefore require minimal invent...
Do you have a Demand Classification      Methodology in place? Answer on the right hand side of your screen        A.   Ye...
   Sales view point       Revenue targets Key accounts/customers   Marketing view point       Brand management, Catego...
   That other theme !
16     Models        Models are defined as forecasts with explicit causal         assumptions that may be mathematically ...
Which algorithm should I use for the differing types           of historical sales patterns?   Sporadic                   ...
18        Picking the right Model/Algorithm          too many choices! lets work with just 5 types
Sales patterns are not the same across all products    What type of products do you deal with?          Answer on the righ...
   Different demand patterns require different    forecasting techniques   Massive volumes of data are becoming more    ...
22     Mimics the thought process of an Analyst to test for:          Zeros          Continuity          Outliers      ...
23
24     Classify products in terms of their historical demand pattern
25        Automatically assign the recommended algorithm and         starting parameters based on history patterns      ...
   So we have…     A corporate wide classification     A statistical forecast model classification           From the ...
27        Forecast Accuracy (3 periods):            The weighted period by period percentage of the absolute value of th...
28
   Classifying Demand make sense if one gets it right       Business Classification drives:           Collaborative wor...
Page 30                 Sept 12th                             Sept 26th Sales & Operations Leadership Exchange:   Supply P...
Demand Planning Leadership Exchange: Developing a Demand Classification Matrix for Forecasting KPIs
Demand Planning Leadership Exchange: Developing a Demand Classification Matrix for Forecasting KPIs
Demand Planning Leadership Exchange: Developing a Demand Classification Matrix for Forecasting KPIs
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Demand Planning Leadership Exchange: Developing a Demand Classification Matrix for Forecasting KPIs

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866.P4D.INFO | Plan4Demand.com | Info@plan4demand.com

Demand Planning Leadership Exchange presents:

Developing a Demand Classification Matrix


with John George, Demand Solution Leader

Developing the Right Matrix for Forecasting KPI’S
Demand Planning teams can lack a clear understanding of where to gain the biggest financial BANG for their time investment.

Classification is a critical enabler that can drive simplification and focus. For example, a 1% forecast improvement for an “A” item can drop $2.0M to the bottom line vs. another “C” item’s 20% improvement only adding $200K. Defining critical items re-focuses demand planning efforts efficiently, all while still delivering desired results.

This session will focus on two themes:
Aligning the rest of the business to a corporate view of Demand Classification
Specifics needed around Demand Planning itself and weaving in forecasting metrics


Key Take-A-Ways include:
Overview of Demand Classification Best Practices
How to run a Best Pick Algorithm Methodology
How to build a corporate view of Demand Classification

Put your demand planning focus where the money is!

Check out this webinar on-demand at http://www.plan4demand.com/Video-Developing-a-Demand-Classifcation-Matrix-for-Forecasting-KPIs

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Transcript of "Demand Planning Leadership Exchange: Developing a Demand Classification Matrix for Forecasting KPIs"

  1. 1. DEMAND PLANNING LEADERSHIP EXCHANGEPRESENTS: The web event will begin momentarily with your host:August 22nd, 2012 plan4demand
  2. 2.  Goals for the Session A Definition The Two themes  Business Classification  Case Study: Wine & Spirits  Forecasting Classification  Case Study: JDA’s Demand Class tool Putting the Themes together with KPI’s The Matrix! An example The Bottom line Q&A/Closing
  3. 3.  Goal:  Marrying the concept of Manufacturing definitions of inventory (i.e. ABC product classification) with the technical classification of Forecasting KPI’s Objectives:  Talk through the business challenges when building a corporate view of shared classification  Discuss the design considerations when implementing a combined Demand Classification Matrix  Key Take-a-ways
  4. 4. 4  Most Classification is a form of pattern recognition in which we attempt to assign for each input value to one of a given set of Classes in a dataset of interest  For Demand Classification, in a forecasting sense, this can give us two themes to consider within the context of this topic  Attribute based classification  (we are going to call this Business classification)  Best pick for Statistical modeling of demand and Forecast Metrics  (we are going to refer to this as Forecasting classification)  How do we combine the two themes into a data driven scenario to convince the business of the value of adoption?
  5. 5.  Traditionally Business has a fragmented approach to Classifying products depending on function  Finance : Cost of goods sold, Average Selling price, Contributive Margin  Sales: Revenue, Customer relationship/size  Marketing: New Launch, Brand, Campaign  Operations: Volume, Material Cost, Storage Cost, Physical nature Often these measures compete with each other and typically the function with the most “political clout” has the major influence rather then a data driven approach bring used The challenge is gathering the data and presenting it to the right groups to persuade them of its merits
  6. 6.  To answer this challenge we need to do the following:  Get a C-level sponsor if possible (CEO CFO etc)  Manage the conversation to the things important to them  Profitability, Productivity, Return on Investment (ROI), Cross functional team working, etc…  Pick the team of people from appropriate disciplines and make the technology choices  Settle on a plan and approach but be flexible  Gather the data to test and build the classification and levels of reporting Let us examine the Methodologies!
  7. 7.  Has its origins in Operations and Inventory control costing Uses Pareto & ABC terminology  Current on–hand quantity uses the current on–hand quantity of inventory  Current on–hand value uses the current on–hand quantity of inventory times the cost for the cost type  Historical usage value uses the historical usage value (transaction history). This is the sum of the transaction quantities times the unit cost of the transactions for the time period you specify  Historical usage quantity uses the historical usage quantity (transaction history) for the time period you specify  Historical number of transactions Uses the historical number of transactions (transaction history) for the time period you specify Typically, a minimum of 1 year’s history is required, but if available, 3 years’ worth of data is probably sufficient
  8. 8.  “A” items are the most critical ones. These items require:  tight inventory controls  frequent review of demand forecasts and usage rates  highly accurate part data  frequent cycle counts to verify perpetual inventory balance accuracy  Typically, these comprise 5% of the total item count, and represent the top 75 – 85% of the total annual dollar value of usage “B” items are of lesser criticality. These items require:  nominal inventory controls  occasional reviews of demand forecasts and usage rates  reasonably accurate part data  less frequent but regular cycle counting  Typically these comprise the next 5 – 15% of the total item count and represent the next 10 – 20% of the total annual dollar value of usage
  9. 9.  “C” items have the least impact in terms of warehouse activity and financials, and therefore require minimal inventory controls  Analysis of demand forecasts and usage rates on “C” items is sometimes waived in favor of placing infrequent orders – often in large quantities – to maintain plenty of stock on hand.  “C” items typically comprise 75 – 80% of the total item count and represent the last 5 – 10% of the total annual dollar value of usage. Because of low usage, any dead or inactive inventory will normally fall into the “C” category The problem is Sales, Marketing, R&D, and often Finance (though involved in costing for the above ABC methods) have different view points to these classifications!
  10. 10. Do you have a Demand Classification Methodology in place? Answer on the right hand side of your screen A. Yes - but its not corporate wide B. Yes - but its not data driven C. Yes - it works for us D. No - its just Operations - ABC E. I don’t know!
  11. 11.  Sales view point  Revenue targets Key accounts/customers Marketing view point  Brand management, Category Management, (with R&D if applicable – New Product Launches) Corporate Finance (as opposed to Operations finance)  Profitability, Margin, Cost of goods sold Miscellaneous/Cross functional  Regional vs. Global factors, contractual penalties, legal considerations on movements of goods and services How do we weave all these things together & what about Forecasting KPI’s?
  12. 12.  That other theme !
  13. 13. 16 Models  Models are defined as forecasts with explicit causal assumptions that may be mathematically stated  These models could also be known as rule-based forecasting, but at least one forecasting expert (Armstrong, 2001) reserved this term for forecasts of time series data.
  14. 14. Which algorithm should I use for the differing types of historical sales patterns? Sporadic Dynamic Seasonal Fuzzy Seasonal
  15. 15. 18  Picking the right Model/Algorithm  too many choices! lets work with just 5 types
  16. 16. Sales patterns are not the same across all products What type of products do you deal with? Answer on the right hand side of your screen A. Continuous vs. Intermittent B. Seasonal vs. Non-Seasonal C. Trend vs. Constant D. Stable vs. Highly Variable E. A mixture of “all of the above”
  17. 17.  Different demand patterns require different forecasting techniques Massive volumes of data are becoming more prevalent  Store Level Forecasting (Retailers: tens to hundreds of millions of DFUs)  Product Proliferation Lack of statistical expertise in planning groups Not enough time or money for statistical research Demand Planning groups are operating lean
  18. 18. 22 Mimics the thought process of an Analyst to test for:  Zeros  Continuity  Outliers  Seasonality  Off-peak Seasonality  Trend  Step Changes
  19. 19. 23
  20. 20. 24 Classify products in terms of their historical demand pattern
  21. 21. 25  Automatically assign the recommended algorithm and starting parameters based on history patterns  Reduce planner fine-tuning time
  22. 22.  So we have…  A corporate wide classification  A statistical forecast model classification  From the latter we can collect the metrics/KPI’S - Automatically - if the tool allows - Manually - if it doesnt  What metrics? - Accuracy - Bias - Volatility
  23. 23. 27  Forecast Accuracy (3 periods):  The weighted period by period percentage of the absolute value of the forecast minus history divided by the forecast  It is subtracted from 1 to define forecast accuracy  Bias (3 periods):  The weighted period by period percentage of the signed value of the forecast minus history divided by the forecast  Volatility (3 periods):  The percentage calculation used to measure the volatility of the forecast over a period  The current forecast minus the 3 period lag forecast for the same period divided by the 3 period lag forecast
  24. 24. 28
  25. 25.  Classifying Demand make sense if one gets it right  Business Classification drives:  Collaborative working practices  Common goals and targets  Forecasting Classification drives:  An easing of the Demand planners workload  Management by exception processing  Putting them together drives:  Alignment with your S&OP Processes  Data driven Executive decision making  Focus on Financial goals
  26. 26. Page 30 Sept 12th Sept 26th Sales & Operations Leadership Exchange: Supply Planning Leadership Exchange: S&OP Technology JDA’s Master Planning vs. A Tool? or a Strategy? Fulfillment Hosted by: Andrew McCall Hosted by: Mike Walker
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