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866.P4D.INFO | Plan4Demand.com | [email protected] 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
Citation preview
August 22nd, 2012 plan4demand
DEMAND PLANNING LEADERSHIP EXCHANGE PRESENTS:
The web event will begin momentarily with your host:
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
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
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?
4
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
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!
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
“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
“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!
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!
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?
That other theme !
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.
Sporadic Dynamic
Seasonal Fuzzy Seasonal
Which algorithm should I use for the differing types
of historical sales patterns?
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 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”
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
22
Mimics the thought process of an Analyst to test for:
Zeros
Continuity
Outliers
Seasonality
Off-peak Seasonality
Trend
Step Changes
23
24
Classify products in terms of their historical demand pattern
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
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
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
Sept 12th
Sales & Operations Leadership Exchange: S&OP Technology
A Tool? or a Strategy?
Hosted by: Andrew McCall
Sept 26th Supply Planning Leadership Exchange:
JDA’s Master Planning vs. Fulfillment
Hosted by: Mike Walker
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