Transcript Inventory Optimization

Inventory Optimization
User Group 17th August 2011
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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
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Why Optimize Inventory?
Inventory costs a lot to acquire
Inventory often in top two assets
Inventory costs a lot to keep
Cost
Storage costs 20% - 40% of its cost value
Poor stock turns
a low return
Cost
Inventory affects service levels
Customer satisfaction
Without customers you don’t have a business
Supply chains don’t work without inventory
Inventory represents an opportunity for big gains
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Benefits of Inventory Optimization
Improved forecast accuracy
A better estimate of the demand
Lower safety stock
Reduces investment in inventory
Typical reductions of 20% - 30%
Improved stock turns
Cost
Cost
Improves achieved service levels
Typical increases of 3% - 30%
Improved sales turnover
Improved ROI
Identifies waste in the supply chain
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Focuses on where value is delivered
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Why Inventory Optimization?
Actual Demand
Level of
Inventory
Forecast
Inventory
Optimization
Forecast Error
Lead Time Error
Lead
Time
Time
The primary drivers of Inventory Optimization are uncertainties in Supply and Demand
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Causes of inventory
Uncertainties in demand
Uncertainties in supply
Lead time greater than service time
Demand & supply quantities not equal
Limitations on order frequency
Supply
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Higher service levels
Demand
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There are three kinds of inventory
Excess stock
Result of poor forecasts, policy
settings or management
Maximum
Level of Inventory
Average Cycle stock
Stock
To meet demand levels
Constrained by batch size, EBQ,
MOQ, or shipping constraints
Minimum
Safety or buffer stock
Lowers risk of stock outs
Balance between cost and service
Time
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The Four Steps to Inventory
Optimization
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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
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The inventory variables
Stock Policy
Supply side
Order frequency
Lead time
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Demand side
Target service level
Modeling
Demand Forecast
Order or lot size
Actual Demand
Delivery reliability
Forecast Accuracy
Time-phased Target Inventory
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Graphic illustration of the 4 Steps
Let’s take a look at what the IO Suite does
Inventory Forecasting (and Families & Groupings)
Inventory Optimization
Remember the 4 Steps?
After this we will delve into a more detailed view
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Step1: Identifying key items through Pareto analysis –
importance and behaviour
Percentage sales value
1. Importance
and Behaviour
Sales value ranking of items
95%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
80%
A
17% of
items
0%
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100%
98%
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B
21% of
items
20%
D 46%
of items
C
16% of
items
40%
60%
Percentage active stock codes
80%
100%
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Step 2: Get the best possible Forecast
Actual Demand
Best Fit Algorithm
Demand Forecast
2. Best estimate
of Demand
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
1. Input Historical Data (in this Example We Have 2 Years)
Mar
Feb
Jan
Dec
Nov
Sep
t
Oct
Aug
Jun
e
July
May
April
Mar
Feb
Jan
Dec
Nov
Sep
t
Oct
Aug
Jun
e
July
May
April
Mar
Feb
Jan
Dec
Nov
Sep
t
Oct
Aug
Jun
e
July
May
0.00
April
4 000.00
Now
2. Use Inventory Forecasting or Families & Groupings to Find the Best Suited Algorithm
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3.Copyright
Use ©the
to Forecast into the Future
2011 Algorithm
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Step3: What is the right stock level for this Demand?
Actual Demand
Best Fit Algorithm
Demand Forecast
2. How Much Stock
For This Demand?
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
Mar
Feb
Jan
Dec
Nov
Sep
t
Oct
Aug
Jun
e
July
May
April
Mar
Feb
Jan
Dec
Nov
Sep
t
Oct
Aug
Jun
e
July
May
April
Mar
Feb
Jan
Dec
Nov
Sep
t
Oct
Aug
Jun
e
July
May
0.00
April
4 000.00
Now
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Step3: Set the Appropriate Stock Levels
Demand Forecast
Max
Min
Resulting Stock (Average)
52 000.00
48 000.00
44 000.00
3. Set Appropriate
Stock Levels
40 000.00
36 000.00
32 000.00
28 000.00
24 000.00
20 000.00
16 000.00
Cost
12 000.00
Stock policy
8 000.00
4 000.00
0.00
April
May
June
July
Aug
Sept
Oct
Nov
Dec
Jan
Feb
Mar
Now
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
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Step 4: Replenish Timeously to the Plan
Demand Forecast
Max
Min
Resulting Stock (Average)
52 000.00
48 000.00
44 000.00
4. MRP Calculates How
Much Replenishment &
By When
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
April
May
June
July
Aug
Sept
Oct
Nov
Dec
Jan
Feb
Mar
Now
1. Once we are satisfied with the Min/Max Levels – this becomes input to MRP
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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
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Measurement of Inventory
Performance
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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
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The IO Suite and Other SYSPRO Modules
1. Importance
and Behaviour
Sales Orders
Historic
Demand
2. Best estimate
of Demand
Inventory
Forecasting
Work in
Progress
Forecast
Families and
Groupings
Open Jobs
Forecast
Master Data,
Setup, Stock on
Hand
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Requirements
Planning
Master Data,
Setup, Stock on
Hand
Inventory
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Open Jobs
4. Replenish
Timeously to Plan
Open Purchase
Orders
Min / Max levels
Inventory
Optimization
Purchase
Orders
Open Purchase
Orders
3. Set Appropriate
Stock Levels
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Behaviour and Importance of Stock
Codes
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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
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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:
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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)
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Examples of the Combined Ranking
Item
RF12
VH72
H7F3
F114
P99
RF81
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Hits
3 107
2 079
6
2 720
11
1 013
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Qty
16 460
19 540
6
9 005
12 100
7 306
Sales Value
7 035 336
4 511 775
3 970 154
32 871
3 342 089
8 449
Hits
ABC
Sales
ABC
Both
A
A
D
A
C
A
A
A
A
C
A
D
AA
AA
DA
AC
CA
AD
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Getting the Best Possible Forecast
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Forecast without Seasonality
250
Max Demand
200
200
Double the
Average
Demand
150
Forecast = Mean
100
Half the 100
Average
Demand
Min Demand
50
50
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Demand Forecasting Based on Average Demand
Leads to Poor Forecast Accuracy
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Forecast with Seasonality
250
Max Demand
200
200
150
Average Demand
100
100
Actual (+)
Revised
Forecast
Min
Demand
Actual (-)
50
50
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Demand Forecasting Based on Accurate Seasonal or
Other Trends Leads to Better Forecast Accuracy
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A more complex demand pattern
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Looking at 3 Years of Demand
Average for forecast
Average stock 2.5 months
Stock Turns 4.8
Service level < 85%
Forecast Accuracy < 60%
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Using a more suitable Algorithm
Holt-Winters Multiplicative
Average stock 2 months
Stock Turns 6
Service level 90%
Forecast Accuracy > 90%
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Forecast Accuracy
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Set Policy and Model Stock
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Setting Dynamic Min / Max Levels
Average stock 1.2 months
Stock Turns 10.2
Target Service level 95%
Forecast Accuracy > 90%
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Safety Stock vs Service Level
100 %
Service Level
95 %
90 %
3%
10 %
85 %
80 %
75 %
70 %
2 weeks
4 weeks
6 weeks
Investment
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Achieved Service Levels
Achieved Service Level for a Selection Set
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Achieved Service Levels
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Sort on Actual Shortfall Descending
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Manage the Plan through MRP
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IO Levels in Requirements Planning
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