Finding Synergies through Synchronization

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. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on the real-world problems. In this series, we summarize a selection of the latest SCM research.

Traditionally, production and transportation planning processes are managed separately in most companies. A shipment of products from a plant to a warehouse typically entails three activities: order, plan, and ship. Once orders are received from customers, a load plan is initiated. The load planning process involves building a truckload of SKUs to be shipped. A transportation provider is then contacted to transfer the products from the plant to the warehouse. The ordered products then ship to the customer through the shipper's distribution network.

What if you could speed up this process, increase internal efficiencies, and reduce overall supply chain costs? We developed a model that enables a company to do just that. The model synchronizes the production and transportation planning processes through implementing a steady flow of products.

Finding the Right Balance

The steady flow of products brings greater stability and cost savings to the supplier network. The theory of having a steady flow or constant stream of products is simple to grasp. A steady flow of products would allow a shipper to reduce overall transportation costs via fixed-volume contracts. It would also allow a shipper to increase warehouse productivity by increasing cross-docking opportunities. However, if too much of any product was shipped on steady flow, then downstream inventories could be bloated. This theory provided an opportunity to build a model that would take these considerations into account.

Which SKUs should be placed on steady flow? What should be their optimal quantities? This model utilizes historical and forecasted demand to select eligible SKUs and optimize their level of steady flow. The model bridges the gap between the steady flow theory and actual implementation.

We developed an analytical framework to maximize the benefits as well as minimize the risks of implementing a steady flow. This framework takes into account the major savings and cost factors of the process. Several other savings and cost factors could have also been included. However, the core factors were used in order to ensure a simple, robust analysis rather than a comprehensive analysis.

Analysis and Application

The resulting output is a list of recommended SKUs that should go on steady flow with their optimal flow quantities. This output was iterated over a six-month period, and compared to the actual demand for that same period. Making such a comparison allows the overall performance of the model to be analyzed.

As stated earlier, the model's purpose was to be a simple, robust analysis of the steady flow process. A sensitivity analysis of all user-defined input parameters was performed by testing the impact on overall performance of the model. The lower the demand variation, the closer the steady flow was to the mean of the demand. As variation increases, the steady flow becomes closer to the minimum of the demand. Further tuning of these parameters would result in better overall results. Extensions could be added to include any additional savings or costs that were not originally included.

This analytical framework can be utilized by any shipper that has a high-volume lane within its own network. The model can help a shipper dampen the bullwhip effect and bring more stability to supply chain nodes.

The SCM thesis Balancing Product Flow and Synchronizing Transportation was authored by Priya Andleigh and Jeffrey Scott Bullock, and supervised by Dr. Ahmad Hemmati and Dr. Chris Caplice, MIT Center for Transportation & Logistics. For more information on the research please contact Chris Caplice at [email protected].

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