Supply Chain Analytics: Enhancing Efficiency, Reducing Costs and Predicting Disruptions

Every product sold worldwide passes through multiple touchpoints before ending up in a buyer’s hands. For instance, a computer’s various parts may be developed by different companies and then shipped to a manufacturer for final production. Once assembled, the computer is shipped to a warehouse and sold directly to a customer or a retail outlet. The process of moving the computer and its parts from beginning to end is known as the supply chain.

As you can imagine, a lot of work goes into ensuring efficiency in modern supply chains. Manufacturers, producers, warehouses, shippers and retailers all play critical roles in supply chain operations, and the data they use to make informed decisions is known as supply chain analytics.¹ With proper data analysis, businesses can reduce costs, improve efficiency and customer satisfaction, and anticipate future risks and potential disruptions that adversely impact the supply chain.¹

Several different types of business analytics are used to evaluate supply chain processes. They include descriptive, predictive, cognitive and prescriptive analytics.¹

Descriptive analytics considers the entire supply chain from end to end, seeking specific insights from internal and external data.¹ Predictive analytics hypothesize probable scenarios based on data and how each outcome may impact businesses.¹

Prescriptive analytics emphasizes collaboration between business partners to solve common issues. It aims to eliminate supply disruptions and reduce fulfillment times. Cognitive analytics provide natural language answers to supply chain inquiries. Teams can submit questions to the cognitive analytics tool, and it will provide a range of options to solve problems.¹

Each type of supply chain analytics plays a different role in analyzing data and supporting streamlined operations and supply chain efficiency strategies.

One example of the successful use of supply chain analytics is Amazon’s AWS Supply Chain, which supports e-commerce companies and similar businesses by tracking inventory and demand forecasting based on consumer data.² The system helps reduce inventory costs while helping to avoid stockouts that harm the customer experience.²

There are multiple ways that companies can reduce expenses in the supply chain–and most involve the use of cost-reduction analytics. One option includes inventory management, such as repositioning products according to projected demand and optimal inventory levels, thus maximizing revenues and reducing inventory costs.³ Another alternative is the realignment of production teams to ensure parts and labor are available at the right time, reducing delays in the manufacturing process.³

Finally, companies can implement product quality and cost analytics in supply chains to track production and manufacturing issues, reducing product defects.³

A supply chain disruption can have serious or even extreme consequences. For instance, in 2022, consumers nationwide faced bare shelves and limited stock for everyday household staples, including potatoes, cream cheese and cars, among other items.⁴ The shortage was attributed to the pandemic and blocked shipping routes due to the Russia-Ukraine war.⁴

While predictive analytics can’t foresee global conflicts or diseases, they can assist in identifying potential blockers that delay goods or raw materials from reaching their final destination.⁴ For instance, a predictive analytics tool could use data to predict storage availability in a warehouse or a consumer demand increase.⁴ It could also estimate a manufacturer’s capacity, potentially reducing overloads that delay final production.⁴

To date, most supply chain analytics tools are available for single touchpoints, such as at a manufacturer’s warehouse or an individual retailer. The systems used don’t communicate with one another, meaning data doesn’t transmit throughout the entire supply chain loop.⁵ AI and machine learning have the potential to change that.

By integrating analytics through the entire supply chain loop, companies can benefit from real-time insights predicting supply chain disruptions or opportunities. AI, automation and machine learning can reduce organizational reliance on human communication between touchpoints, allowing for a more seamless process.⁵ Machine learning and AI provide advanced analytics that can assist in numerous areas, including demand planning, production scheduling, inventory management and supplier integrations.

There are several challenges in the current supply chain environment. For one, many organizations rely on other countries for parts or assembly, and it can be very difficult to coordinate efforts, especially when dealing with multiple suppliers.⁶ The solution is an integrated analytics system that connects suppliers, manufacturers, and other stakeholders that can mitigate disruptions and identify potential roadblocks to supply chain activities.⁶

Another issue is quality control and compliance. Product and part defects can lead to regulatory issues for brands. If a defective product is sold to consumers, it can result in injury and legal repercussions. Through supply chain analytics, companies can better identify the root cause of defects and reduce their occurrence.⁶

Shifting trends and demand also impact the fluidity of the supply chain. Numerous factors impact consumer demand, including macro and micro-economic elements. Predicting demand can be tricky for an individual, but supply chain analytics can support their efforts by considering multiple factors at once and making a reasonable forecast.⁶

As advanced technologies become more readily entrenched within the supply chain environment, it will become much easier for companies to identify and correct supply chain disruptions immediately rather than allowing them to fester into more significant issues. Integrating supply chain solutions on an open-loop basis, rather than autonomously, can help ease supply chain bottlenecks and better predict consumer demand.⁷

We’ll likely see future AI and machine learning advancements that continually improve supply chains and provide greater agility.⁷

Forward-looking companies in the logistics and manufacturing industries can benefit from experienced workers with a data analytics background. If you’re interested in participating in the supply chain revolution, consider working toward an online supply chain analytics master’s in business analytics (MSBA).

William & Mary’s Online MSBA program will instill the knowledge and skills you need to turn raw data into actionable business strategies for any organization. Our rigorous curriculum covers business acumen, applied math, computing technology and effective communication. During your program, you’ll learn from top experts, and you can tap into our extensive network of business leaders.

Review the MSBA application requirements and schedule a call with an admissions outreach advisor today to learn more.