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

1. DEMAND PLANNING LEADERSHIP EXCHANGE
PRESENTS:
The web event will begin momentarily
with your host:
August 22nd, 2012 plan4demand

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.  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
 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.  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.  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.  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.  “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

10.  “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!

11. 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!

12.  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?

15.  That other theme !

16. 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.

17. Which algorithm should I use for the differing types
of historical sales patterns?
Sporadic Dynamic
Seasonal Fuzzy Seasonal

18. 18
 Picking the right Model/Algorithm
 too many choices! lets work with just 5 types

19. 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”

20.  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

21. 22
Mimics the thought process of an Analyst to test for:
 Zeros
 Continuity
 Outliers
 Seasonality
 Off-peak Seasonality
 Trend
 Step Changes

22. 23

23. 24
Classify products in terms of their historical demand pattern

24. 25
 Automatically assign the recommended algorithm and
starting parameters based on history patterns
 Reduce planner fine-tuning time

25.  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 doesn't
 What metrics?
- Accuracy
- Bias
- Volatility

26. 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

27. 28

28.  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

29. 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