1. Perfecting Visibility
By: Daniele Primavera, Hang Shi

2. Agenda Project Objective and Background Hypothesis Methodology Results
Sensitivity Analysis
Hypothesis Challenges
POS Framework
Conclusion and Lessons Learned

3. Project Objective
Develop a framework using point of sale (POS) data to adjust production planning
Reduce inventory cost
Minimize equipment changeover cost
Maintain high service level

4. Background Sponsor Company:
Fortune 500 Consumer Packaged Goods Manufacturer
Global footprint
POS Data comes from one of our sponsor’s largest retail customers
Product: Staple food that is consumed regularly
Geography of study: US only

5. Assumptions & Hypothesis
Average POS ≈ Average Customer Orders
POS and Customer Orders are Normally Distributed
Customer Orders is the POS with Bullwhip Effect
Use POS data to improve current production plan (POS2Orders) Reduce Bullwhip if we produced to meet POS (POS2POS)
Original Production Plan
Bullwhip:
High Levels of Inventory
Poor Equipment Utilization
POS, Orders
Delay is because the retailer collects sales data then makes order decision
Amplification is called the bullwhip effect
-Due to aggregation in purchasing but primarily due to an overreaction
-Puts stress on us to maintain a higher level of safety stock there by driving up total inventory costs and product spoilage
Also puts stress on manufacturing to fulfill high order quantities while other times we sit idle
Orange plot represents an improvement in our ability to fulfill orders
We will be able to better forecast the orders but it doesn’t remove the bullwhip
VMI completely removes the extra process of the customer orders.
We fulfill to POS, the benefits are we can forecast better, less inventory cost, less stress, less idle time

6. Methodology: Data Collection
From the retailer: 4 SKUs, daily POS data, 6 months
From the manufacturer: 4SKUs, weekly data, 6 months
UPC Code
Quantity Sold
Store Inventory
Warehouse Inventory
UPC Code
Customer Order
Production Quantity

7. Methodology: Conversion Rate
120 80

8. Optimization Objective: Minimize inventory and production cost
Constraints
Choose Model
Days of Supply
Dynamically Integrate
Repeat
Original
Days of Supply
POS2Orders
POS2POS
Production Capacity
“Haniele”
Algorithm
Production Freeze = 3wk
Holding Cost
Changeover Cost

9. SKU 2: POS vs Orders

10. SKU 4: POS vs Orders

11. Inventory: SKU 2

12. Inventory: SKU 4

13. Financial Impact SKU 2 + SKU 4 Total Cost Vs. Original Vs. POS2Orders
$ ,870 --
POS2Orders
$ ,176
5.10%
POS2POS
$ ,293
23.79%
19.70%
SKUs in this platform 40
Savings per SKU ~ $7.5k/6months ~$15k/ year
Total is $600K/year
Only for 1 store and 1 platform
YR 1: Cost $100k employee, $100k IT, 100K for other expenses
YR2: Employee, IT
YR3: Employee

14. Sensitivity Analysis: Changing the Bullwhip Effect Level
What happens if we change the bullwhip effect level for both SKU 1 and SKU 4?
Impact on the POS2Order model
Impact on the POS2POS model

15. Sensitivity Analysis: Impact on the POS2Order model
Bullwhip
Order 2Order
POS2Order
“planning for bullwhip”
Savings
1.5
$47,988
$45,761
4.6%
4.0
$64,020
$60,776
5.1%
different bullwhip
similar savings

16. Sensitivity Analysis: Impact on the POS2POS model
Bullwhip
Order2Order
POS2POS
“reducing bullwhip”
Savings
1.5
$47,988
$40,293
16%
4.0
$64,020
37%
the larger the bullwhip
the larger the savings

17. Sensitivity Analysis: POS2POS Safety Stock Frontier
The key driver is the potential reduction of the variation

18. Hypothesis Denied: SKU 1

19. Hypothesis Denied: SKU 3

20. Hypothesis denied: possible reasons
Potential reasons for the misalignment between POS sales and Customer Orders
Change of the Inventory Policy of the Retailer
Promotional Event
Customized merchandising

21. POS Benefits Framework
High
Engage with retailer to align inventory policy
Misalignment between POS and Customer Orders
Economic Value
Use POS data
to better plan for the bullwhip
Use POS data to influence customer orders
Low
Low
Bullwhip
High

22. Conclusions: the Value of POS data
POS data creates economic value if used to plan for the bullwhip
POS data creates higher economic value if used to influence Customer Orders and reduce the bullwhip
POS data enhances supply chain collaboration between manufacturer and retailer

23. Conclusions: Looking Ahead
Use the framework to prioritize SKU analysis
Integrate analysis with promotional data
Analyze the costs and benefits when relaxing the freeze production period

24. Conclusions: Personal Lessons from the Project
Data analysis is complementary to the qualitative understanding of the business processes
Demand signals must be integrated in the demand planning process to produce benefits in the S&OP process
Increasing visibility in the supply chain requires the involvement of all parties acting in the chain

25. Thank You
Questions?