forecasting 101 - peel.ascm.org

FORECASTING

101

IT’S ALL ABOUT

THE CUSTOMER

Ed White CPIM, CIRM, CSCP, CPF, LSSBB

Chief Value Officer

Jade Trillium Consulting April 01, 2015

Making processes and organizations more effective.

Ed White CPIM CIRM CSCP CPF LSSBB is the founder of Jade Trillium

Consulting. Over the course of his career he has worked in several

industries ranging from food and pharmaceutical to chemicals and

plastics. He has held positions at many different management levels

and been responsible for numerous projects in Supply Chain, cost

saving, education and value enhancement,

Ed is an active member of APICS as an instructor and Chapter Board

member. In addition Ed is a published author and sought after

speaker on various Supply Chain topics. He has spoken at several

international conferences for APICS, IBF & SAPICS.

Biography

Making Organizations More Effective!


FORECASTING IS LIKE BREATHING,

EVERYONE DOES IT AND YOU

ALMOST NEVER THINK ABOUT IT


How long will it take to get to work?

• What time do you have to leave

• Based on history, modified by weather conditions, current

road construction and traffic conditions

Will I run out of money before I run out of

month?

• Decide whether to go see a movie with family

• Based on outstanding bills, modified by current cash

situation and understanding of potential new bills


There are two types of forecasts:

FORMAL:

• Defined process and procedures

• Used by everyone in the defined group

• THIS IS WHAT YOU WANT!!!

INFORMAL:

• No defined process

• Everyone making their best guess as to what will be needed in their

own areas


Because we want to:

to keep our customers happy and

profitable

so we can stay happy and

profitable

•and employed

Making processes and organizations more effective. 9

to ensure material is available

for sale

at an acceptable customer

service level

while maintaining stock levels

at a financial level that is

acceptable to the company.

Main functions of Materials Management is:

Making processes and organizations more effective. 10

in the right place

at the right time

in the right quantity

at the right cost

Purpose of MRP is to recommend changes to

material stock levels that will; To ensure material is:


Forecasting is the prelude to planning

Little planning can be done without some

form of estimation

Forecasts are at best an informed guess

Forecast is NOT the budget divided by 12

Forecast is related to the LE not the

budget


Are almost always wrong

• If they are right it’s a mistake

Should include an estimate of error


Are almost always wrong

• If they are right it’s a mistake

Should include an estimate of error

Are more accurate for groups of products

Are more accurate for nearer periods of

time


There are two types of formal forecasts:

Quantitative

• Calculated based on

someone’s past sales data

Qualitative

• Someone’s best guess

You should be doing both


The problem with only using

a calculated forecast is that it

is like driving using only the

rear view mirror.

You’re OK until the first

curve in the road and

then you’re in the ditch.


• Efficient production sequencing which will lead to more

available capacity.

• Less rework

• Better run size distribution

• Reduced Dead and Slow Inventory due to a more accurate

product mix in the inventories and greater inventory turnover

• Improved product quality

• Improved customer confidence

• Cost containment through improved freight logistics

• Optimized inventories

• More accurate raw material planning


• Inventory carrying cost on Dead and Slow

Moving Stock

• Dead and Slow Write-Downs

• Extra warehousing costs for ineffective

inventory

• Lost production due to rush inserts

• Reworked inventory


Short answer – everyone that has

information on future demand.

System can generate a calculated

forecast based on history

Sales must modify based on Cust.

knowledge

SALES & OPERATIONS PLANNING (S&OP) process

is used to arrive at a consensus forecast

– Only sales knows Cust. long

term plans.


Trend – General movement upwards or

downwards

Seasonal – repeating pattern over a period

of time

Bias – artificially introduced trend

Outlier – data point that is significantly

different than the norm


General business and economic conditions

Competitive factors

Market trends

• such as changing demands in the marketplace

Firm’s own plans

• pricing

• product changes

Regulatory compliance


Jan 100

Feb 90

Mar 110

Apr 105

May 95

Jun 90

July 90

Aug 120

Sept ?

Simplistic – Last month sales repeated

• This is the simplest form of formal

forecast available.

• Only useable if sales very steady

Since Aug sales was 120 units the forecast for Sept is 120

• To allow for planning lead time you could always

offset by one or more months. For example a one

month offset would use July sales of 90 units as the

forecast for Sept.


Jan 100

Feb 90

Mar 110

Apr 105

May 95

Jun 90

July 90

Aug 120

Sept ?

Simplistic – Last month sales repeated (Forecast =120)

Average – Average preceding 12 months sales

• Ignores seasonality and variability

In this example we only have 8 months of data so an

8 month average would be (100 + 90 + 110 + 105 +

95 + 90 + 90 + 120)/8 = 800/8 = 100


Jan 100

Feb 90

Mar 110

Apr 105

May 95

Jun 90

July 90

Aug 120

Sept ?


Average – Average preceding 12 months sales (Forecast = 100)

Moving 3 month Average – Average only

previous 3 month sales

• Captures seasonality but not trends

Previous three months (Jun, July and Aug) give an

average of (90 + 90 + 120)/3 = 300/3 = 100


Jan 100

Feb 90

Mar 110

Apr 105

May 95

Jun 90

July 90

Aug 120

Sept ?


Average – Average preceding 12 months sales (Forecast = 100)

Moving 3 month Average – Average only previous 3

month sales (Forecast = 100)

Winters, Student T, Multiple Linear Regression, etc

• Potentially more accurate but requires

statistical background to set up and interpret

• Definitely more expensive


1. You want the formula and process that gives you

at least the lowest acceptable accuracy

2. This should be balanced against the cost of the

process. If you are spending more than you are

saving, find a simpler process.

3. Should always compare your accuracy against

the accuracy of simplistic formula. If no major

improvement, why use the more complicated

formula / methodology.


1.Are there any one time events like shutdowns

planned by the customer?

2.Are there any anticipated increases in demand?

3.Are there any anticipated decreases in demand?

4. Is there any chance of an existing product / size

being replaced by a new combination?

5. Is there any chance of a change to customer /

location / etc?


• No more than 3 hours a month

• Critical customers first

• Critical materials second

• Low f/c accuracy combinations

before high accuracy combinations

• If you must guess - guess high


• Forecasts are always wrong

• We don’t care if they are wrong

• The important point is how wrong

are they!

By monitoring the forecast accuracy report we

can track which material / customer

combinations or other groups of forecasts

have the most opportunity for improvement.


REMEMBER: FORECASTS ARE MORE ACCURATE

• FOR LARGER GROUPS

• Therefore more accurate at material level rather than at

material / Cust. Level

• IN THE NEAR FUTURE RATHER THAN THE DISTANT FUTURE

• 3 months out more accurate than 12 months out, but 12

months still important for tentative capacity planning

• FOR A CUMULATIVE TIME FRAME RATHER THAN A SINGLE

TIME FRAME

• By reporting sales over rolling 3 months rather than each

month individually we eliminate the issue of sales that fall just

over the line into another month


Formula: Forecast Accuracy (%) = x 100

1st Step:

Calculation of absolute forecast error with respect to forecast lowest level ONLY

2nd Step:

Examine result for special cases at the article level

• If Forecast = 0 and Actual 0, then Forecast Accuracy = 0%

• If Actual = 0 and Forecast = 0, then Forecast Accuracy = 100%

• If Forecast 0 and Actual = 0, then Forecast Accuracy = 0%

2.

1.

Sales - Sales- Forecast

Sales

Month

Jan

Forecast from Dec

2000 1800

Actual Sales

200

Sales - Forecast

3.

3rd Step:

Calculation of Forecast Accuracy by using given formula

Forecast Accuracy (%) = x 100) = 89,9%

1800 - 200

1800

4. 4th Step:

Aggregation according to predefined reporting levels

• Aggregation of SALES and absolute deviations on all aggregation levels; calculation of forecast accuracy on all aggregation levels

• Consequence: Deviations grow on every aggregation level (net out effect of positive and negative errors is eliminated)

(absolute variance)


• The important factor is not technique, it is process control

and management

• The S&OP process allows everyone to participate in

creating a consensus Demand & Supply plan

• Anyone can forecast but the right person with the right

information can significantly increase accuracy

• Small changes in forecast accuracy can translate to big $

improvement in profitability

• Bottom line – it’s all about customer satisfaction


FORECASTING IS JUST ANOTHER

COMMUNICATIONS PROCESS

Remember,

IT’S ALL ABOUT

THE CUSTOMER


For more information or if you have any

comments about this presentation please

contact me at:

[email protected]

Please remember to give your completed

feedback form to our room monitor.


Forecasting 101

It’s all about the customer

[email protected]

905-483-5984

http:jadetrilliumconsulting.com

Ed White@JadeTrillium

ca.linkedin.com/in/edwhitesupplychain/

Ed White CPIM CIRM CSCP CPF LSSBB

Value Catalyst

Jade Trillium Consulting

Supply Chain and Management Consulting


FORECAST PROCESSES

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

Last year sales 9,250 9,100 9,000 9,050 8,950 9,250 9,250 9,400 9,550 9,675 9,900 9,850 112,225

Simplistic

last yr + 10% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

This year f/c 10,175 10,010 9,900 9,955 9,845 10,175 10,175 10,340 10,505 10,643 10,890 10,835 123,448


Last year sales 9,250 9,100 9,000 9,050 8,950 9,250 9,250 9,400 9,550 9,675 9,900 9,850 112,225

Adjusted

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

This year f/c 10,175 10,010 9,900 9,955 9,845 10,175 10,175 10,340 10,505 10,643 10,890 10,835 123,448

Sales Adjustment 100- 100- - 50 50 - - 100 200 250 250 400 1,100

Final f/c 10,075 9,910 9,900 10,005 9,895 10,175 10,175 10,440 10,705 10,893 11,140 11,235 124,548


Last year sales 9,250 9,100 9,000 9,050 8,950 9,250 9,250 9,400 9,550 9,675 9,900 9,850 112,225

Tiered

This year f/c Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

Promotional 100- - - 100 50- - - - - 100 400 400 850

Holiday - - - - - - - - - - 100 200 300

Seasonal 100 100- 200- 300- 200- 100- - 100 300 500 400 300 800

Internal 50 50 50 50 50 50 50 50 50 50 50 50 600

Service 100 100 100 100 100 100 100 100 100 100 100 100 1,200

Base 10,000 10,000 10,000 10,000 10,000 10,000 10,000 10,000 10,000 10,000 10,000 10,000 120,000

Forecast 10,150 10,050 9,950 9,950 9,900 10,050 10,150 10,250 10,450 10,750 11,050 11,050 123,750

Forecast Processes

forecasting 101 - peel.ascm.org

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