B2B Omnichannel Network Design and Inventory Positioning

Editor’s Note: The SCM thesis B2B Omnichannel Network Design and Inventory Positioning was authored by Geoffrey Allen and Shoichi Ishida and supervised by Dr. Eva Ponce ([email protected]). For more information on the research, please contact the thesis supervisor.

The B2B foodservice distribution industry in the United States is projected to witness substantial growth over the next five years, but like many B2B industries, it is just starting to experiment with omnichannel fulfillment. Some companies have taken the plunge and borrowed from the B2C playbook, offering fulfillment through stores in addition to the more traditional direct delivery to their customers, while others are doubling down on delivery with new and more flexible delivery options. In either case, the challenge lies in redesigning supply chain networks to accommodate increased complexity and flexibility without radically inflating costs. Companies must consider factors such as inventory positioning, shrink, lead time, and more when determining how many products should be transported between suppliers, warehouses, and customers. Unfortunately, while B2C omnichannel network design is a popular research topic, B2B applications to date make up only 4% of reviewed studies and pose unique challenges.

Why don’t existing approaches or popular software packages work for us?

Implementing familiar software solutions to address these challenges may seem like a straightforward decision, but this comes with its fair share of challenges. Let’s explore three key issues.

Firstly, complicated and interrelated omnichannel options can stress existing software packages to the breaking point. Common network design approaches assume a hierarchical and sequential flow of goods from supplier to customer. However, true omnichannel designs, where all order channels for all items can potentially be fulfilled across all delivery options, can overwhelm the design user interface for common packages due to the combinations involved. This is especially true for companies with a large number of items, suppliers, and/or customers. More exotic, but potentially cost-saving, designs involving options like lateral transshipments between nodes exacerbate the problem further or may not be supported at all.

Secondly, inventory plays a crucial role in the overall supply chain strategy, yet it’s not always adequately addressed by existing software. Many packages handle the nonlinear nature of inventory equations by solving for inventory positioning separate from network design. However, this frequently leaves money on the table. Items with high inventory holding costs and/or variable demand are typically held at too many locations with sequential solve approaches, while items with the opposite characteristics could be further decentralized. Some companies attempt to get around this trade-off by iterating between network design and inventory modeling with the hope of converging towards a reasonable answer, but this then increases the complexity of the overall effort.

Lastly, solve time can be a limiting factor. As models grow more complex to handle all the flexibility required, the time required to solve problems increases significantly. This can make the intelligence from these efforts less useful to decision-makers—or less realistic by requiring unpalatable aggregations to model realistic item, supplier, and customer counts.

Is there a better way?

To tackle these challenges, we developed a specialized mathematical optimization proof-of-concept model tailored specifically for a large B2B food distribution company in the United States looking to expand omnichannel distribution options. Putting it to the test with real company data, this model can solve for a global optimal solution for both network design and inventory positioning simultaneously, at close to 10 times the speed of alternative benchmarks.

Our tailored and integrated approach has shown a significant 7.6% reduction in total supply chain costs, encompassing product cost, transportation cost, warehouse handling, inventory holding, and omnichannel expenses like cross-docking or direct ship. Across the items examined, suggested inventory holding costs reduced by up to 50%.

The secret sauce lies in our custom algorithm, which leverages new capabilities in commercial solvers to effectively handle quadratic equations. We reformulate the nonlinear inventory equations into a form that allows for simultaneous solutions, and then improve the solve time using outer approximation techniques. This enables us to efficiently and quickly solve complex, real-world network optimization problems without long delays or sacrifices in realism. While our initial proof-of-concept assumed the current network assets, this model could easily be expanded to include facility open/close decisions as well.

Omnichannel network design is a complex endeavor, but with the fusion of the latest solver technology, new advanced mathematical algorithms, and a solid foundation of supply chain theory, companies can squeeze new life and greater efficiency out of their existing networks. In industries like foodservice distribution, with razor-thin margins of 2% or less, this can be a critical differentiator between success and failure.

Every year, approximately 80 students in the MIT Center for Transportation & Logistics’s (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.

These students are early-career business professionals from multiple countries, with two 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 real-world problems. In this series, they summarize a selection of the latest SCM research.

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