copyright © 2011 syspro all rights reserved. inventory optimization user group 17 th august 2011

Copyright © 2011 SYSPRO All rights reserved.www.syspro.com

Inventory Optimization

User Group 17th August 2011

http://www.syspro.com/

www.syspro.com Copyright © 2011 SYSPRO All rights reserved. Simply Smarter

What we’ll cover

Why Optimize Inventory?

The Four Steps to Inventory Optimization

Measurement of Inventory Performance

The IO Suite in Relation to other SYSPRO Modules

Looking at the Four Steps in more detail



Why Optimize Inventory?

Inventory costs a lot to acquireInventory often in top two assets

Inventory costs a lot to keepStorage costs 20% - 40% of its cost valuePoor stock turns a low return

Inventory affects service levelsCustomer satisfactionWithout customers you don’t have a business

Supply chains don’t work without inventory

Cost

Cost

Inventory represents an opportunity for big gains



Benefits of Inventory Optimization

Improved forecast accuracyA better estimate of the demandLower safety stock

Reduces investment in inventoryTypical reductions of 20% - 30%Improved stock turns

Improves achieved service levelsTypical increases of 3% - 30%Improved sales turnoverImproved ROI

Identifies waste in the supply chain

Focuses on where value is delivered

Cost Cost



Forecast Error

Why Inventory Optimization?

Time

Level of

Inven

tor

y

Forecast

Lead Time

Lead Time Error


Actual Demand

The primary drivers of Inventory Optimization are uncertainties in Supply and Demand



Causes of inventory

Uncertainties in demand

Uncertainties in supply

Lead time greater than service time

Demand & supply quantities not equal

Limitations on order frequency

Higher service levels DemandSupply



There are three kinds of inventory

Time

Lev

el o

f In

ven

tory

Safety or buffer stockLowers risk of stock outsBalance between cost and service

Cycle stockTo meet demand levelsConstrained by batch size, EBQ, MOQ, or shipping constraints

Excess stock Result of poor forecasts, policy settings or management

Minimum

Maximum

Average Stock


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The Four Steps to Inventory Optimization



The 4 Steps to Inventory Optimization

Understand the importance and behaviour of the stock codes in each location

Get the best possible estimate of Demand (Forecast)

Set the appropriate stock levels to meet this demand (Stock Policy and Modelling)

Replenish Timeously to Plan



The inventory variables

Actual Demand

Demand Forecast

Forecast Accuracy

Order frequency

Lead time

Order or lot size

Delivery reliability

Target service level

Time-phased Target Inventory

Stock PolicySupply side Demand side

Modeling



Graphic illustration of the 4 Steps

Let’s take a look at what the IO Suite does

Inventory Forecasting (and Families & Groupings)


Remember the 4 Steps?

After this we will delve into a more detailed view



Step1: Identifying key items through Pareto analysis – importance and behaviour

0%10%20%30%40%50%60%70%80%90%100%

0% 20% 40% 60% 80% 100%

D 46%of itemsC

16% of items

80%

A17% of items

95%100%

Sales value ranking of items

Per

cent

age

sale

s va

lue

Percentage active stock codes

98%

B21% of items

1. Importance and Behaviour



Step 2: Get the best possible Forecast

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

Now

Actual DemandBest Fit AlgorithmDemand Forecast

1. Input Historical Data (in this Example We Have 2 Years)

2. Use Inventory Forecasting or Families & Groupings to Find the Best Suited Algorithm 3. Use the Algorithm to Forecast into the Future

2. Best estimate of

Demand52 000.00

48 000.00

44 000.00

40 000.00

36 000.00

32 000.00

28 000.00

24 000.00

20 000.00

16 000.00

12 000.00

8 000.00

4 000.00

0.00



Step3: What is the right stock level for this Demand?

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

April

May

June

July

Aug

Sept

Oct

Nov

Dec

Jan

Feb

Mar

Now

Actual DemandBest Fit AlgorithmDemand Forecast

52 000.00

48 000.00

44 000.00

40 000.00

36 000.00

32 000.00

28 000.00

24 000.00

20 000.00

16 000.00

12 000.00

8 000.00

4 000.00

0.00

2. How Much Stock For This

Demand?



Step3: Set the Appropriate Stock Levels

April May June July Aug Sept Oct Nov Dec Jan Feb Mar

Now

Demand Forecast

MinMax

Resulting Stock (Average)

52 000.00

48 000.00

44 000.00

40 000.00

36 000.00

32 000.00

28 000.00

24 000.00

20 000.00

16 000.00

12 000.00

8 000.00

4 000.00

0.00

1. Depending on Your Stock Policy IO Calculates Min/Max Levels for the Forecast

2. Let’s say we decide the Min/Max Levels were not ideal so we try different policy options

3. Set Appropriate Stock Levels

Cost

Stock policy



Step 4: Replenish Timeously to the Plan

April May June July Aug Sept Oct Nov Dec Jan Feb Mar

Now

4. MRP Calculates How Much

Replenishment & By When

Demand Forecast

MinMax

Resulting Stock (Average)52 000.00

48 000.00

44 000.00

40 000.00

36 000.00

32 000.00

28 000.00

24 000.00

20 000.00

16 000.00

12 000.00

8 000.00

4 000.00

0.00

1. Once we are satisfied with the Min/Max Levels – this becomes input to MRP

2. MRP Takes Calculated Min/Max Levels, the Opening Stock, Orders In Process and Lead Time, and Suggests Replenishment To Comply With The Stock Policy



Measurement of Inventory Performance



Determining Inventory Performance

Try to find a balance between Service and the investment in inventory to provide it

Measure the Service delivered to the customer

Measure the quality of the Forecast (Forecast Error or Accuracy)

Measure how fast the inventory “turns over”Stock Turns = Annual Cost of Sales / Cost of Stock

An alternate view of this is the “stock cover”Months Cover = 12 / Stock Turns



The IO Suite and Other SYSPRO Modules

Inventory Forecastin

gSales Orders

Families and

Groupings

Inventory Optimizati

on

Requirements PlanningInventory

Forecast

Master Data, Setup, Stock

on Hand

Min / Max levels

Work in Progress

Purchase Orders

Open Purchase Orders

Open Jobs

Historic Deman

d

Forecast

Master Data, Setup, Stock

on Hand

Open Jobs

Open Purchase Orders

2. Best estimate of

Demand

3. Set Appropriate Stock Levels

4. Replenish Timeously to

Plan

1. Importance and Behaviour



Behaviour and Importance of Stock Codes



Analyse for Importance and Behaviour

Example from a SYSPRO customer

Over 3 000 SKUs

Many of these have erratic demand

Use a Pareto (ABC) based on sales value or gross profit to analyse importance

Use a Pareto based on hits (demands) as a simple indicator of forecastability

Combine these to manage in like groups



Use a combination of Hits and Sales Value

Hits (Demands):

A > 36 p.a.

B 13 to 35 p.a.

C 7 to 12 p.a.

D 1 to 6 p.a.

Sales Value:

A 80% (> R275 000 p.a.)

B 95% (R34 000 to R275 000 p.a.)

C 98% (R11 800 to R34 000 p.a.)

D 100% (R1 to R11 800 p.a)



Examples of the Combined Ranking

Item Hits Qty Sales ValueHits ABC

Sales ABC Both

RF12 3 107 16 460 7 035 336 A A AA

VH72 2 079 19 540 4 511 775 A A AA

H7F3 6 6 3 970 154 D A DA

F114 2 720 9 005 32 871 A C AC

P99 11 12 100 3 342 089 C A CA

RF81 1 013 7 306 8 449 A D AD



Getting the Best Possible Forecast



Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

250

Forecast = Mean 100

Max Demand 200

Min Demand 50

Half the Average Demand

Double the Average Demand

Forecast without Seasonality

Demand Forecasting Based on Average Demand Leads to Poor Forecast Accuracy

Average Demand = 100 unitsDemand Varies from 50 - 200 units



Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50

100

150

200

250

Average Demand 100

Min Demand 50

Max Demand 200

Actual (+)

Actual (-)Revised Forecast

Forecast with Seasonality

Demand Forecasting Based on Accurate Seasonal or Other Trends Leads to Better Forecast

Accuracy



A more complex demand pattern



Looking at 3 Years of Demand

Average for forecastAverage stock 2.5 monthsStock Turns 4.8Service level < 85%Forecast Accuracy < 60%



Using a more suitable Algorithm

Holt-Winters MultiplicativeAverage stock 2 monthsStock Turns 6Service level 90%Forecast Accuracy > 90%



Forecast Accuracy



Set Policy and Model Stock



Setting Dynamic Min / Max Levels

Average stock 1.2 monthsStock Turns 10.2Target Service level 95%Forecast Accuracy > 90%



Safety Stock vs Service Level

Investment

70 %

75 %

80 %

85 %

90 %

95 %

100 %

Ser

vice

Lev

el

2 weeks 4 weeks 6 weeks

10 %

3 %



Achieved Service Levels

Achieved Service Level for a Selection Set



Achieved Service Levels



Sort on Actual Shortfall Descending



Manage the Plan through MRP



IO Levels in Requirements Planning


copyright © 2011 syspro all rights reserved. inventory optimization user group 17 th august 2011

Documents

inventory cost inventory

inventory optimization

assets inventory

demand slide

inventory typical reductions

demand stock policy

low return inventory

demand levels