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

Page 30