Unlocking the Power of POS
Each point of sale (POS) in a retail supply chain generates a goldmine of demand data.
Latest NewsProcurement is getting its digitized act together Uncertainty surrounds fine print of new, trilateral North American free trade deal FedEx set to acquire Manton Air-Sea Pty Ltd. Other Voices: Planning and technology can reduce disaster risk iWarehouse wins 2018 MHEFI Exceptional Contribution Award More News
Latest ResourceSee Your Supply Chain Like Never Before Wednesday, October 24, 2018 | 2pm ET
Editor’s Note: Every year, 40 or so students in the MIT Center for Transportation & Logistics’ (MIT CTL) Master of Supply Chain Management (SCM) program complete one-year thesis research projects. The students are early-career business professionals from multiple countries with 2 to 10 years of experience in the industry. The research projects are sponsored by and carried out in collaboration with multinational corporations. Joint teams of company people, MIT SCM students, and MIT CTL faculty work on real-world problems chosen by the sponsoring companies. In this monthly, series we summarize a selection of the latest SCM research. The researchers for the project described below, Daniele Primavera and Hang Shi, created the model for their MIT Supply Chain Management Program master’s thesis Perfecting Visibility with Retailer Data. The work was carried out for General Mills, Inc and the project was supervised by MIT CTL Lecturer Dr. Jarrod Goentzel. For more information on the program, visit http://scm.mit.edu/program.
Each point of sale (POS) in a retail supply chain generates a goldmine of demand data. The data can be used to drive upstream decisions, but the amount of time, effort, and cross-team collaboration needed often frustrates such applications.
The MIT CTL researchers developed models to show how a leading CPG manufacturer can use large volumes of POS data to improve supply chain performance.
Cost cutting potential
The thesis sponsor company, General Mills, Inc (GMI), is a Fortune 500 manufacturer of food products. GMI typically ships to the warehouses of large retailer customers via regional distribution centers. Of particular interest was finding out how POS data can be used to adjust production planning in order to reduce both production and inventory costs while maintaining high levels of service.
Four SKUs produced in the same manufacturing process and supplied to the same retail customers were selected for the purposes of the analysis. The SKUs represent a specific production platform, and as such, provide a good subject for testing the usability and added value of POS data.
To illustrate the value of this data source, the researchers focused on the potential for reducing two key manufacturing costs: change over and inventory holding costs. The research looked at how POS data integration in the supply planning process could produce direct benefits in terms of these costs while maintaining item fill rate targets set by the company.
POS as relationship builder
The researchers designed a multi-period production planning linear program to optimize production scheduling for a given set of weeks. The program minimized total relevant costs subject to capacity and inventory target constraints. An important assumption was that all SKUs were being produced in the same plant. This allowed the linear programming to assign each SKU production quantity to each week for a unique factory location.
Three models were developed.
• The base model used only historical customer order data to plan production schedules.
• Model number two used POS data to forecast orders and adjust production to fulfill customer orders.
• The third model used POS data to adjust production to fulfill future POS demand, eliminating customer orders as an input.
Of the three models, the base option proved to be the most costly. Although the second one performed better, the improvement was relatively modest because this model fulfills according to customer orders and hence does not reduce the bullwhip effect. The third model, which uses POS data to improve demand forecasts and fulfill future demand, delivered the highest cost savings.
The findings suggest that the practical application of POS data can raise supply chain performance – with some riders.
First, companies gain the most benefit from applying POS data in this way when the bullwhip effect is minimal.
Also, in general, as the bullwhip effect increases so do inventory volumes and levels of stress on the production system, and the case for using POS data to offset these effects becomes stronger. However, in such situations manufacturers need to persuade their retailer customers to place orders that are aligned with the POS data, and collaborate with them to address misalignments.
POS data can also be used to alert manufacturers that they need to adjust customer order volumes with respect to actual sales.
Perhaps the most significant lesson is the importance of effective communications between manufacturers and retailers in realizing the value of POS data. The prize is well worth the effort – stronger long-term relationships that enhance competitive advantage.
About the AuthorPatrick Burnson, Executive Editor Patrick Burnson is executive editor for Logistics Management and Supply Chain Management Review magazines and web sites. Patrick is a widely-published writer and editor who has spent most of his career covering international trade, global logistics, and supply chain management. He lives and works in San Francisco, providing readers with a Pacific Rim perspective on industry trends and forecasts. You can reach him directly at [email protected]
Subscribe to Supply Chain Management Review Magazine!Subscribe today. Don't Miss Out!
Get in-depth coverage from industry experts with proven techniques for cutting supply chain costs and case studies in supply chain best practices.
Start Your Subscription Today!
The 2018 Supply Chain Top 25: Follow the leaders NextGen technologies: Building the supply chains of the future View More From this Issue