Perfecting Visibility By: Daniele Primavera, Hang Shi

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

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

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

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

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

Methodology: Conversion Rate 120 80

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

SKU 2: POS vs Orders

SKU 4: POS vs Orders

Inventory: SKU 2

Inventory: SKU 4

Financial Impact SKU 2 + SKU 4 Total Cost Vs. Original Vs. POS2Orders $ 52,870 -- POS2Orders $ 50,176 5.10% POS2POS $ 40,293 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

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

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

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

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

Hypothesis Denied: SKU 1

Hypothesis Denied: SKU 3

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

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

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

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

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

Thank You Questions?