Supply Chain’s New Imperative: Designing for Value Creation
Enterprises that fail to take advantage of these opportunities will be at a competitive disadvantage.
A new white paper published by the MIT Center for Transportation & Logistics and software provider Coupa, titled The New Competitive Advantage: Analytics-Driven Supply Chain Design, An Executive Guide, shows how companies can leverage a new generation of supply chain design methods to increase competitiveness. Based on research and extensive work with companies, the paper presents a detailed guide to designing future-proof supply chains.
The white paper launched last week along with the newly created MIT Supply Chain Design Initiative, chaired by MIT CTL Research Scientist Milena Janjevic.
Four paths to design innovation
Innovations in the fields of optimization, simulation, and data science have advanced supply chain design — also called strategic supply chain planning — way beyond its traditional roots. These advances coupled with a flood of new data on operational performance have created extremely powerful, more accessible, design tools.
Companies need this innovative toolbox more than ever as they grapple with changes that have transformed the competitive landscape. Competing successfully in increasingly volatile, risk-prone markets requires fresh approaches to supply chain design and redesign to keep pace with fast-changing customer needs. Also, traditional design criteria such as cost minimization are giving way to more relevant performance yardsticks like value creation. And the dynamism of markets requires companies to revisit the design of supply chains more frequently.
These challenges are creating unique opportunities for improving performance and profitability. The white paper offers four primary examples:
- Extend the scope of supply chain design beyond cost minimization to embrace a broader set of factors including the diversity of global markets, value creation, and sustainability. In value creation, for instance, design models should encompass a more extensive set of financial objectives such as capital expenditure and working capital.
- Integrate more granular, tactical components into strategic decision-support tools to achieve a closer match between the anticipated and actual performance of supply chain networks. This approach is especially effective in areas such as sourcing optimization, managing replenishment and inventory strategies, and last-mile network design.
- Give more consideration to sources of risk and uncertainty and incorporate risk-mitigation and resilience measures into analytical decision-making tools. For example, stochastic programming methods enable models to account for a range of likely outcomes associated with the main sources of uncertainty.
- Incorporate new technologies and business models in the design process to support the development of digitally enabled, customer-centric supply chains that are both flexible and profitable. Examples include the latest production, warehouse, and transportation technologies, and digitally enabled operational capabilities.
Each opportunity is illustrated with real-world cases. MIT CTL helped a chemical company adapt to customers’ demands for shorter delivery lead times. The team proposed a redesigned network that optimized the trade-off between logistics costs, market share growth, and revenue. The center also worked with a Brazilian omnichannel retailer to develop a three-tier last-mile distribution network that uses different transportation modes depending on delivery speed, demand, and infrastructure features.
Implanting the new methods in companies
The white paper also explains how companies can tap into the full power of analytics.
Applying the latest techniques in artificial intelligence (AI), machine learning (ML), and network science can turbocharge supply chain design studies. For example, AI and ML can reduce the complexity of models by using clustering rules to perform aggregation tasks.
Studies must also align with the company’s managerial and organizational culture. One way to achieve this is to reframe design problems to sharpen their focus on contemporary issues such as market share and revenue growth.
Also, It is important to choose the right level of customization and complexity when designing studies. Modelers should account for trade-offs between data collection and maintenance needs, the amount of computational time required, and a model’s ability to deliver relevant insights.
Again, the recommendations are supported by real-world examples. The MIT CTL team developed highly customized optimization models for a global pharmaceutical company. The study considered various operational factors such as the numerous cold-chain airfreight shipping solutions employed in the industry.
But even the most insightful supply chain design models fall short if their outputs cannot be implemented. The paper explains how corporate organizational structures and processes can harness modern supply chain design methods by resolving issues such as who owns supply chain analytics and design, and how the resource can support cross-functional collaboration.
Competitive stakes are high
The time has come for companies to rethink the way they design their supply chains. Fortunately, a new generation of tools and practices has emerged that enables them to do just that. Moreover, the case for adopting these methods can only become stronger as the operational challenges companies face — and the supply chain design toolbox — continue to evolve.
Enterprises that fail to take advantage of these opportunities will be at a competitive disadvantage. Moreover, the gap between the laggards and leaders will widen as enlightened companies embed cutting-edge supply chain design approaches in their corporate strategies.
The white paper Analytics-Driven Supply Chain Design was authored by Milena Janjevic of the MIT Supply Chain Design Initiative, Carlos Valderrama and Michael Bucci of Coupa Software, and Prof. Walid Klibi of KEDGE Business School in Bordeaux, France.
