Download Do SKU and Network Complexity Drive Inventory Levels?

Do SKU and Network Complexity Drive
Inventory Levels?
JOSEPH MCCORD & DAVID NOVOA GARNICA
SCM 2015
Take-aways
1. Greater complexity does not translate into higher inventory levels.
2. Inventory quantities mirror simulated inventory management
heuristics rather than traditional optimal inventory models.
3. Two potential measurements of complexity: number of SKUs per
brand and number of stocking locations per SKU.
Outline
Introduction
Methodology
Results
Conclusion
The Challenge of Complexity in the CPG Industry
• Products are added to categories faster than
they are removed
• Many production and stocking locations
• Wide product variety
How does this affect inventory requirements?
Project Sponsor: Unilever
• Two billion interactions per day
• +400 brands in 190 countries
Introduction
Methodology
Results
Conclusion
Two Specific Forms of Complexity
Stock-Keeping Unit (SKU) Complexity
• How many SKUs are in each brand?
• The more SKUs in a brand, the greater the forecast error for each SKU
• How many stocking locations are used for each SKU
• The more customer-facing locations for an SKU, the greater the forecast
error for that SKU
Mechanism
Weighted Days of Stock in Brand
Network Complexity
14
12
10
8
6
4
2
0
0
• “Square root law” – going from 1 to n locations, IOH increases by sqrt(n)
(under several assumptions)
Introduction
Methodology
100
200
300
SKUs in Brand
Results
Conclusion
400
Research Question
Do increases in SKU or network complexity result in anticipated increases in observed inventory
levels?
Introduction
Methodology
Results
Conclusion
Overall Study Design
Ordinary least-squares regression against a power curve
Replicated across geographies, product categories, and time periods
Weighted Days of Stock in
14
R² = 0.9495
12
10
8
6
4
2
0
0
100
200
300
400
SKUs in Brand
Also: developed a simulation exercise to model inventory levels under varying hypothetical
inventory management approaches
Introduction
Methodology
Results
Conclusion
Data Applied
Source
Content
Use
“MIO” Inventory Optimization
Software
Ave daily demand quantities,
forecast error per SKU and
SKU location
Calculate complexity, demand
data
Inventory Records
Inventory quantities per SKU
location
Calculate inventory days on
hand
Sales Records
Sales quantities per SKU
Eliminate obsolete SKUs
Introduction
Methodology
Results
Conclusion
Analysis Process
For each iteration of the analysis:
1.
Removed discontinued and obsolete SKUs
2.
Linked inventory and demand datasets
3.
Generated pivot tables
4.
Created scatterplots and calculated regression statistics using spreadsheet software
Simulation process: created database with similar characteristics to actual datasets, calculated
inventory quantities according to various common inventory models:
•Base-stock policy
•“ABC” (sales-based inventory control)
Introduction
Methodology
Results
Conclusion
Data Summaries – Daily Demand
Cluster 1:
7,000
•Strong skew toward a daily demand of zero units.
5,000
• 10% of SKUs have a daily demand of 500 units or more,
4,000
3,000
• More than 2000 SKUs have a daily demand of 25 units or less
2,000
1,000
4001
3801
3601
3401
3201
3001
2801
2601
2401
2201
2001
1801
1601
1401
1201
801
1001
601
401
201
-
1
Average Daily Demand (units)
6,000
Other cluster present the same characteristics
SKU rank by demand
Introduction
Methodology
Results
Conclusion
Nodes in the network
Cluster 1:
Cluster 2:
800
3000
700
2500
2000
500
No. SKUs
No. SKUs
600
400
300
1500
1000
200
500
100
0
0
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
11
Nodes in Network
Nodes in Network
Network complexities differ across clusters. Geographies and markets dictate network design
Introduction
Methodology
Results
Conclusion
Regression Results – SKU complexity
Cluster 1:
•Strong skew toward a daily demand of zero units.
• 10% of SKUs have a daily demand of 500 units or more,
• More than 2000 SKUs have a daily demand of 25 units
or less
Other cluster present the same characteristics
Introduction
Methodology
Results
Conclusion
Regression Results – Network complexity
Cluster 1:
• No correlation
• Network complexity does not appear to act as an
influencing factor.
• Very wide range of inventory levels at each discrete
network size
Introduction
Methodology
Results
Conclusion
Simulation results
14
R² = 0.9495
Weighted Days of Stock in Brand
12
10
y = 3.1149x0.2058
Expected outcomes if inventory levels were in fact
managed according to various known inventory control
policies:
8
• Inventory managed safety Stock Equation (CSL 95%)
6
• Inventory Managed over Lead Time Using Safety Stock
Equation
4
• “ABC” Method
2
0
0
100
200
300
400
SKUs in Brand
Introduction
Methodology
Results
Conclusion
Simulation results (2)
18
R² = 0.5483
16
- Base level driven by SKU complexity and lead time
14
Weighted Days of Stock in Brand
Inventory managed using:
12
y = 5.9533x0.1486
10
- Safety Stock Equation (CSL 95%)
8
6
4
2
0
0
100
200
300
400
SKUs in Brand
Introduction
Methodology
Results
Conclusion
Simulation results (3)
80
“ABC” Method
Weighted Days of Stock in Brand
70
60
• A - Fast movers: low inventory levels (managed)
50
• B – Intermediate movers: higher inventory (managed)
40
• C – Slow movers: high inventory levels
30
20
10
Similar results to the actual results
0
0
100
200
300
400
SKUs in Brand
Introduction
Methodology
Results
Conclusion
Interpretation
Do increases in SKU or network complexity result in anticipated increases in observed inventory levels?
Introduction
Methodology
Results
Conclusion
Limitations
Several factors limit the ability to extend the results of this research:
1.
Data analyzed only represents the experiences of one firm within the consumer packaged
goods industry.
2.
This study assumes independence of demand between products.
3.
Other unexplored sources of complexity which could drive inventory levels could be
production or sourcing lead time variance, frequency of product mix change, and overall
variance of demand within brands.
Introduction
Methodology
Results
Conclusion
Take-aways
1. Greater complexity does not translate into higher inventory levels.
2. Inventory quantities mirror simulated inventory management
heuristics rather than traditional optimal inventory models.
3. Two potential measurements of complexity: number of SKUs per
brand and number of stocking locations per SKU.
Introduction
Methodology
Results
Conclusion
Thank you!
Questions?