Jon Schreibfeder EIM Effective Inventory Management, Inc

Demand Forecast Analysis

Jon SchreibfederEIM

Effective Inventory Management, Inc.

A demand forecast is a prediction of the quantity of a

product that will be sold, transferred or otherwise used during a specific time period.

© Effective Inventory Mgmt, Inc. 2

How Much of Each Stocked Item Will be Sold or Used at Each Location?

Comprehensive forecasting software Everyone understanding their

responsibility in achieving the most accurate possible forecast

Someone responsible for assembling all of the elements of the forecast and evaluating forecast accuracy


Best Practices in Forecasting

How Accurate is Your Forecast?

Month

Usage

Forecast

Calculation Error%

Oct 100 50 [ABS(100 – 50)]÷ 50

100.0%

Sep 50 100 [ABS(50-100)]÷ 50

100.0%

Aug 95 100 [ABS[(95-100)]÷ 95

5.3%


[Absolute Value of (Usage – Forecast)] ÷ Lower of Forecast or Usage

Few systems calculate a forecast error Many systems calculate the forecast error

with the formula:◦ Absolute Value of (Usage – Forecast) ÷ Forecast◦ Underestimates error when forecast is too high

Many systems maintain a perpetual forecast error◦ Will tend to overlook recent improvements or

deteriorations in forecast accuracy

Notes of the Forecast Error

© EIM, Inc. 5

In a study done by EIM of a wide range of distributors using a wide range of computer systems:◦ The mean forecast error was 682%◦ The median forecast error was 381%

“Best Practice” companies had an error that was approximately 1/10th of these averages

The better your forecast, the less you need to stock to maintain your desired level of customer service

No computer software on its own can consistently produce accurate forecasts


The “Average” Forecast Error

Recurring vs. Sporadic Usage Items

Dec Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan

50 0 0 0 20 0 0 0 0 50 0 0

Sporadic usage item have a “normal usage quantity” < the average quantity sold in a time period

The normal usage quantity is the quantity normally sold or used in one transaction

Sporadic usage items are not sold or used on a regular basis and cannot be forecast


Average usage is between 10 and 16.7 pieces per month. What is the “normal usage

quantity”?

Target Stock Level = 1 Average Order

Minimum = 9Maximum = 10

© Effective Inventory Mgmt, Inc. 8June

May

April

March

February

January

Usage 10 0 0 0 10 0

Target Stock Level = 2 Average Orders

J une

May

April

March

February

J anuary

Usage 50 0 0 50 0 50

Parameters for this item include a safety stock equal to one sale:

Minimum = 50Maximum = 100


Dependent Demand Independent Demand

Forecasts are Based on Demand



Dependent Demand is end user demand that is known in advance of a time period equal to the sum of the anticipated lead time and replenishment cycle. For example:

◦Manufacturing according to a production plan

◦Preseason ordering◦Projects scheduled far in the future

Dependent Demand


Process of scheduling material listed in a bill of materials is known as Materials Requirement Planning (MRP)

Bills of Material (BOM) list all major inventory items that are needed for an assembly or major project.

Dependent Demand


Seven Week Production Schedule

Da

y 0

Da

y 7

Da

y 1

4

Da

y 2

1

Da

y 2

8

Da

y 3

5

Da

y 4

2

A100 Needed

21 Day LTOrder A100

A200 Needed

21 Day LTOrder A200

A300 Needed

7 Day LT

Order A300Bill of Materials

Provided

ProjectDueDate

Material Requirements Planning (MRP)


Consumption by end users that is not planned in advance

“Advance” is a time period equal to the sum of:◦ Anticipated Lead Time (how long does it take to

acquire the product once it is ordered from a supplier)

◦ Replenishment Cycle (how often is the stock of the item replenished)

Independent Demand


Independent Demand

What software can work with:◦Consideration of past usage of a product◦ Internal trends◦Collaborative elements

The human factor:◦ Defining and accumulating usage◦ Developing collaborative information

High forecast errors are often blamed on the software not the human element. Why?


Independent Demand Forecasts Are Based On Different Elements

When determining whether or not a specific transaction should affect usage ask:

“Do we want to replenish inventory with this quantity of this item in

this warehouse to meet our customers’ future needs.”


The Big Question of Usage

Usage should reflect actual lost sales Usage may not include direct shipments Usage should not include close outs Usage should be recorded in the location

that should restock to meet future demand


Usage is Not Another Word for Sales or Shipments

Usage should be recorded when the customer wanted the product

Usage should be recorded for what the customer wanted

Usage of discontinued products should be added to superseding items

Usage may not include transfers

Usage is Not Another Word for Sales or Shipments



Usage and Lost Sales A lost sale is a request for a product you

couldn’t fill because you were out of stock◦ Record number of times a customer requested a

product but didn’t place an order because you were out of stock

◦ Analyze these requests and decide how many you actually could have sold if stock had been available


Lost Sale Log

Date Customer Quantity

Item Reason

Mar 3 ABC Industries

5 A100 No Stock

Mar 4 ABC Industries

5 A100 No Stock

Mar 4 XYZ Corp 10 A100 No Stock

Mar 4 Ajax Company

25 C190 Price Too High

Push Replenishment: The total usage of the central warehouse includes recurring transfers to the other warehouses it supplies with the product

Roll up Replenishment: The total usage of the central warehouse includes the customer orders and assemblies of the central warehouse as well as the customer orders and assemblies of the other warehouses it supplies with the product.


Usage and Transfers


Push - Recurring Transfers

Central Warehouse or Distribution

Center

Branch #2Branch #1 Branch #3

Vendor

Normal replenishment transfers are included in the usage of the central warehouse or distribution

center


Push - Non-Recurring Transfers

Central Warehouse or Distribution

Center

Branch #2Branch #1 Branch #3

Vendor

One Time Transfer

Non-recurring transfers are not included in the usage of the shipping branch

Central Whse/Dist CenterDmd/Day for Customers =

12.0Total Dmd/Day = 32.0

Branch #2Dmd/Day = 7.0



VendorCustomers

buy 12.0 pieces per

day from the Distribution

Center


Roll-Up: Calculating Total Forecast Demand

Either method of accumulating usage or demand in a

distribution network requires that a single normal path of

replenishment be maintained for every item in every facility


Accurately Maintain the Normal Path of Replenishment

Hypothesis: I think this event or external factor will affect usage

Test: Does it affect usage?

Record results: When this occurs again, I can adjust the forecast to take into account the results of this event or external factor

Clean usage history: Adjust out the effects of the event from usage history. After all, this event will not occur at exactly the same time next year.


Analyze Each Event & External Factor

Measuring the Effect of Events

Event Start Day End Day Effect of

EventCentenni

al Founders

’ Day

04/23/2012

04/30/2012

-25.0%

Promo-1 05/01/2012

05/07/2012

26.8%

Promo-1 09/01/2012

09/07/2012

14.0%

Promo-2 10/01/2012

10/07/2012

13.2%

© EIM, Inc. 28

120

April 02

120

125

April 9

125

119

April 16

119

89

April 23

+30

119

Actual Sales

Adjustment for Event

Normalized Usage


Effect of 100 Year Celebration

The 25% decrease in usage during Centennial Founders’ Day will not reoccur. The 25% decrease in sales is compensated for in Normalized Usage.

Formula:

Actual Usage ÷ (1 ±% Difference from Normal Usage)

488

May 11

488

524

May 18

524

462

May 25

462

595

June 1-126

469

Actual Sales

Adjustment for Promotion

Normalized Usage


Effect of Promotion #1

Promotion #1 resulted in a 26.8% increase in sales. This increase should be adjusted out of usage history.

When Promotion #1 is offered in the future, the forecast for that week should be adjusted by the

average previous results of Promotion #1.

Even after automatic adjustments to usage have been applied there still may be significant differences between the demand forecast and actual usage

Salespeople are closest to the customers

Salespeople can best determine if possible unusual usage is:◦ Activity that will not reoccur◦ Start of a new trend


Buyers Should Bring Possible Unusual Usage to Sales

Usage This Inventory Period > “x%” of the Forecasted Demand

For Example: Usage in June (1500 pieces) is greater than four times forecasted

demand


Usage 1500 410 290 375 450 303

Forecast 368 420 305 368 404 334

Usage this Inventory Period < “y”% of the Forecasted Demand

For Example: Usage in June is less than 20% of the forecasted demand

© Effective Inventory Mgmt, Inc. 33Ju

ne

May

Ap

ril

March

Feb

ruary

Janu

ary

Usage 40 210 260 185 290 160

Forecast 224 208 274 202 269 204

We have noticed that products with an item number starting with “A” or “1” have better performance than products with an item number beginning with “Z”

Examine transactions, talk with salespeople and customers to determine if any unusual activity occurred


List by Rank of Product by Size of the Discrepancy

CPS INDUSTRIAL

POSSIBLE UNUSUAL USAGE REPORT Page: 1

Printed 10/03/12 Month Ending 09/30/12

BUYER : JHS WAREHOUSE: DALLAS

ITEM DESCRIPTION Rnk USAGE DEMAND DIFF ERROR% TREND%

A400 A400 REGULATOR WITH ½” CONNEC A 520 106 414 390.1% 12.5%

TRANSACTIONS COMPRISING USAGE

CUST QTY DATE INVOICE

JONES MFG 20 09/12/12 7623890

ALLBRIGHT CO. 25 09/14/12 7782310

HANSEN INC. 34 09/16/12 7798235

AVON PIERS 400 09/21/12 7801242

DUCKINGS, INC. 41 09/22/12 7813492

© EIM, Inc.

Example of a Possible Unusual Usage Report

35

Quantity sold or used during the month was affected by activity that won’t reoccur

A new sales trend has begun Received collaborative information is not

accurate The wrong forecast formula is being used to

predict future demand of the product


Reasons for Possible Unusual Usage

Compensating for an Unusual Sales Quantity

In reviewing all of the transactions for June, the buyer notice an unusual sales of 1,000 pieces. After talking

to the salesperson or customer it was decided that this transaction was unusual


Usage 1500 410 290 375 450 303

Adjustment -1000 0 0 0 0 0

500 410 290 375 450 303NormalizedUsage

Jun

e

May

Ap

ril

March

Feb

ruary

Janu

ary

Usage 1500 410 290 375 450 303

Adjustment 0 1000 1000 1000 1000 1000

1500 1410 1290 1375 1450 1303Normalized Usage


Compensating for a New Sales Trend

A new customer will continue to buy approximately 1,000 pieces a month for

the foreseeable future

Median Usage in the

Past 12 Months

+300%

33 492

-80%

164


Automatically Adjust Unusual Usage for Previous Months

If usage in a previous month is less than “x”% or more than “y”% of the median usage for the previous 12 months, adjust usage to equal the median usage

quantity

Different Patterns of Usage Require Different Forecasting Methods

Item Sep ‘12

Aug ‘12

Jul ’12

Jun ‘12

May ‘12

Apr ‘12

Mar ‘12

Feb ‘12

Jan ‘12

Dec ‘11

Nov ‘11

Oct ‘11

A100 100 120 80 90 110 105 88 109 98 118 112 108

B200 300 260 220 188 160 142 138 122 109 98 80 76

C300 1020

28 1030

34 990 36 1033

27 1004

39 1034

26

D400 41 85 160 241 370 398 224 129 57 36 24 20

© EIM, Inc. 40

Different Items Have Different Patterns of Usage……

0100200300400

B200

B200

© EIM, Inc. 41

0

200

400

600

800

1000

1200

C300

C300

050

100150200250300350400450

D400

D400

0

20

40

60

80

100

120

140

A100

A100

Identifying the Pattern of Usage Calculate a forecast for each of the past

several months using several forecast formulas

Compare each calculated forecast to actual usage for that month

Calculate a forecast error The formula with the lowest error, wins

42© EIM, Inc.

© Effective Inventory Mgmt, Inc.

Often future demand is best determined by analyzing customers’ predictions of what they will buy or use, rather than past usage history

Inquiries by salespeople can reveal changes in a customer’s anticipated product usage that can be communicated to your buyers

Customers with “dependent” demand are wonderful candidates for collaborative forecasting

Collaborative Forecasting

43


Add to Sales Call Report◦ Previous collaborative estimates along with

actual usage by customer over last several months

◦ Individual transactions that might represent unusual usage

◦ Space to note new collaborative forecasts for specific products

Sample Sales Call Report

Improving Accuracy of Collaborative Forecasts

44


Collect collaborative forecasts and report accuracy to the source of information as well as the forecast personnel

Consider offering incentives for more accurate forecasts

Only add collaborative estimates to forecast if accuracy is > 75%

Do not include collaborative estimates in usage history. That is sales to collaborative customers must be adjusted out of usage history

Collaborative Forecasting

45


Results of a Formula (with/without internal trend percentage)+ Effect of Events+ Subjective Information+ Sum of Dependent Forecasts=Total Demand

Total Forecast for an Item

46


Inaccurate forecasts Lucky forecasts

But more accurate forecasts lead to improved profitability. For example: Better purchasing Better planning of corporate resources Better cooperation between corporate

departments and members of the supply chain

Two types of Forecasts

“Effective Inventory Management enables an organization to meet

or exceed customers’ expectations of product

availability with the amount of each item that will maximize the

net profits or minimize total costs.”


The Goal of Effective Inventory Management

Jon Schreibfeder, PresidentEffective Inventory Management, Inc.

120 South Denton Tap RoadSuite 450 – 200

Coppell, TX 75019Phone: 972 304-3325

Fax: 972 [email protected]


If you have questions…..

mailto:[email protected]

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Jon Schreibfeder EIM Effective Inventory Management, Inc