Other Voices: How supply chain analytics can help drive data-driven decisions

Editor’s Note: The following column by Jason Barrett, director of research and development for Voxware, is part of Modern’s Other Voices column. The series features ideas, opinions and insights from end-users, analysts, systems integrators and OEMs. Click here to learn about submitting a column for consideration.

———

Whether selling to businesses or consumers, producers and distributors are under relentless pressure to keep costs down, deliver goods quickly, efficiently, and accurately, and to anticipate trends and disruptions. Supply chain managers need hard data, the tools and expertise to sort through the information, and solutions that are purpose-built to use these facts to solve their specific business problems.

If you’re in a position of responsibility for your organization’s supply chain, you know that having sophisticated computer systems and ‘smart’ machines and vehicles that track their own locations, productivity, and downtime is important, but the data generated from the ‘Internet of Things’ is only as valuable as how well it can be used to help an organization keep pace with increasing competition and expectations.

After all, goods today make their way to customers via many routes. With traditional brick and mortar retail remaining prominent and e-commerce currently on pace to exceed $334 billion by the end of this year, peer-to-peer sales from sites like Amazon, eBay, and Etsy as well as the social media-driven sharing economy add further complexity—and competition—to the retail marketplace. When consumers have so many options, companies can’t afford to rest on their laurels.

Staying ahead of the curve means starting with a descriptive baseline for past performance. Unfortunately, for far too long, backward-looking descriptive reporting is where analytics solutions stopped. Today, that’s changed; not only do analytics tools give supply chain leaders the information they need to conduct performance reviews and audit processes, but they now make critical decisions in real time and powerful predictions about the future.

High stakes: Customers are kept or lost in minutes
In today’s warehouse, fulfillment decisions are made not in days but in minutes or even seconds. With the rise of next-day delivery and the demand for same-day delivery, supply chains are more sensitive than ever to last-minute changes such as extreme weather or natural disasters. Lags in the system due to poorly performing personnel need to be identified and corrected to keep shipments running on time. Like mission control technicians, supply chain front-line supervisors must constantly evaluate incoming telemetry and make real-time mission-critical decisions. Throughout every shift, fulfillment supervisors need to ask themselves: Do product-carrying trucks need to be rearranged in the departure queue? Will extra selector capacity be necessary for the next shift? If we’re having mechanical problems at one manufacturing center, can production be increased at another?

In the past, answering these questions required time-consuming manual analysis and massive coordination. Today, these questions can be answered and addressed in real time thanks to predictive analytics. Real-time dash boarding capabilities provide current actionable information to the front line supply chain manager. For example, a warehouse supervisor can view up-to-the-minute delivery and picking assignment statuses with the ability to move floor workers from one assignment to another to keep up with current priorities.

Never guess again: Predict the future and prepare for it
Big Data offers companies the opportunity to analyze historical data, which can be valuable for setting benchmarks and analyzing past performance—but perhaps one of the most enticing use cases for Big Data in the supply chain sphere involves looking ahead. Business intelligence tools use mathematical and statistical models to create forward-looking analytics, which may be predictive, by assessing future conditions and needs, and/or prescriptive, by offering recommendations for future actions and planning, such as longer-term factory and warehouse staffing.

From production quantities at micro and macro levels to historical and projected sales figures, from elapsed times for manufacturing, distribution, and delivery to travel distances, all of these numbers can serve as inputs to sophisticated planning models. As computing power grows and scales, it’s now possible to run increasingly intricate and powerful simulations that model alternate scenarios. Today’s systems can evaluate multiple paths and courses of action in real time. Questions related to how many people should be hired to meet peak demand or how much extra transportation capacity should be allotted can be made based on all available data instead of gut feelings and best guesses.

Leverage analytics for competitive advantage
To make decisions with confidence, supply chain leaders need hard data easily accessible in a solution that is purpose-built for the industry’s unique challenges. Fortunately, predictive analytics tools tailored to use cases warehouse leaders encounter every day are making it possible to make data-driven decisions that consider all factors, from labor management to manufacturing planning to delivery optimization.

To make the best use of supply chain data, it’s important to pull it all together to paint a complete picture that gives each stakeholder all the information necessary to make smart decisions. Predictive analytics tools that tie multiple sources together are a significant competitive advantage for organizations, and supply chain executives must choose partners who will work with them to advance decision-making ability at every level of the supply chain. As in so many other industries, the winners will be those who use the right data intelligently to make objectively optimal choices.