Big Data Analytics: The Future of Supply Chain Management
By Kelly Mi, Columbia GRC
The volatility of the global supply chain has never been more evident than now. By definition, a supply chain is the “network between a company and its suppliers to produce and distribute a specific product to the final buyer.” From the heightened production of personal protective equipment since the onset of the COVID-19 pandemic to the blockage of the Suez canal which held up US $9.2 billion of goods a day, such crises demonstrate that the supply chain is a carefully orchestrated act with trillions of dollars tied up between its lines. In the midst of the holiday season when demand inevitably surges, shoppers face familiar signs and notifications: ”sold out” and “delayed shipping.” Disruptions to the supply chain affect every stakeholder. For example, semiconductor shortages impacted Samsung’s ability to manufacture the 2021 Galaxy Note. Small business owners have been forced to either overspend and bulk up inventory, or risk a potential shortage. Failures in any step of the system –– production plants shutting down or freight halting –– can cause supply chain shocks. This is where the increasingly more relevant field of analytics can help.
Analytics in the Supply Chain
Supply chains generate tons of data, and analytics can be used to understand and optimize them. Common goals include identifying risk, reducing cost, and increasing planning accuracy. Adept supply chains are flexible and resilient, and thus, end-to-end visibility is critical. A recent Oxford Economics survey found that 49% of Supply Chain Leaders (the top 12 % of respondents) can capture real-time data insights and act on them immediately, while 51% use AI and predictive analytics to capture insights. The quick turnaround with data enables companies to have a more holistic view of the supply chain and to better understand customer demands. Despite the benefits of analytics, nearly 60% of businesses do not have adequate visibility across their supply chain. According to McKinsey & Company, there are two main challenges to implementing data analytics in supply chain management. First, supply chain managers often do not have the technical expertise to understand the scope and potential of data analytics. Second, most companies do not have a method to collect and evaluate the data. By establishing a structured approach akin to one outlined by McKinsey here, companies can begin to use analytics to address supply chain shocks.
A recent Gartner survey revealed that 76% of supply chain leaders report that their companies face more supply chain disruptions now than three years ago. Consequently, preventative applications of analytics are as important as corrective applications. Tracking, modeling, simulating, and predicting through data allows companies to mitigate disruptions. The three most applicable steps of the supply chain are procurement, inventory, and logistics. Analytics increases responsiveness to changes in supply, monitors imbalances in inventory, and determines the optimal transportation route. However, the most significant contribution is cross-functionality –– “the view of multiple supply chain functions into a single perspective to enable quick insight and decisions in a volatile market environment,” by providing the most comprehensive and timely information possible. In September 2021, International Business Machines (IBM) introduced its AI-powered Supply Chain Intelligence Suite which uses “a combination of business rules, advanced analytics, AI and automation.” Its emphasis on intelligence and automation is indicative of the industry trend towards digitization.
Intersection of Supply Chain, Analytics, and Sustainability
In addition to the financial incentives of an efficient supply chain, companies also have a responsibility for their customers who may demand to see a focus on environmental, social and governance (ESG) issues. Addressing and mitigating ESG risks –– for example, unfair labor practices in an overseas supplier –– can reduce reputational risk and outweigh any potential savings. One of the most relevant ESG steps in the supply chain is procurement, which impacts the footprint “directly through purchase decisions and indirectly by influencing product design.” Globalization and outsourcing makes it difficult to track individual company-level impact, especially in developing countries where regulation is sparse. However, smart devices have allowed for the continuous collection of information which powers big data analytics. Technology provides the opportunity to assess each party within the supply chain, to integrate and draw connections between them. Companies are using data analytics to re-evaluate the social and environmental impacts of their products and services, which can further increase efficiency. Indeed, McKinsey has reported that leading ESG companies were able to reduce costs by 5 to 10 percent and out-perform their peers in growth and valuations by a margin of 10 to 20 percent. Analytics have increased transparency and accountability of individual firms as well as their respective supply chain networks.
Supply chain analytics allows the wealth of information to be translated into decisions. Real-time data translates into resilience and flexibility because managers are able to simulate disruptions and make critical, timely decisions. Efficiency and sustainability are intrinsically linked, so ESG-minded supply chains are often optimized. Companies with efficient supply chains reach “5-15% lower supply chain costs, 20-50% less inventory holdings, and up to 3X cash-to-cash cycle speeds.” The breadth of data unmasks hidden players within the supply chain, connecting them to create a cohesive perspective. This thereby circumvents the problem that 76% of business leaders see: the lack of cohesive perspectives imposing challenges to meeting their respective objectives. Efficiency allows companies to drive costs down and to address their ESG impacts. Thus, using analytics to optimize the supply chain is necessary for every organization, regardless of size.