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Six Ways That Business Intelligence Can Expand Manufacturing Margins

Business analytics are at the center of most of these successful firms, providing focused and actionable insights, which help them fine tune operations, and create opportunities to increase prices.

Mnet 193819 Business Accounting
Amir OradAmir Orad

Profit margins in manufacturing sometimes feel like they are frozen in place or, worse, facing a continuous squeeze that can minimize and even erase profits. According to Sageworks, a financial information company, many manufacturers were working for free last year, with flat or negative margins.

There are many reasons for this margin squeeze: on the fiscal side, identifying new efficiencies in this complex operation has been a time-consuming and difficult process. Efficiencies often required months of research to identify, followed by the testing and re-testing of innovative ideas before they can be implemented. In many cases, because of all the effort involved, companies miss the opportunity to implement changes ahead of their competition.

Moving to the revenue side of the equation, when it comes to differentiating service and increasing prices, manufacturers tend to get into a vicious cycle: the perception is that new services and support, which could provide opportunities to increase pricing, involve a significant financial investment. That financial outlay scares off many managers, which, in turn, keep margins thin across the industry.

Although there are significant pressures on margins, there are numerous winners in the manufacturing space—companies that manage to innovate on cost reductions and premium service enhancements to make great returns. Business analytics are at the center of most of these successful firms, providing focused and actionable insights, which help them fine tune operations, and create opportunities to increase prices.

Six common ways that analytics expand margins are:

No. 1 - Ensuring Top Performance from Suppliers

Experienced managers understand that purchasing can be an important part of their company’s supply chain. Still, this function is often overlooked when managers are too busy trying to improve other aspects of their business. It’s an unfortunate reality that weak suppliers – those who are too expensive, too slow, or unreliable – can cost a firm a few fractions of a cent per unit, which can add up to millions of dollars in additional costs in aggregate. Just as serious, it can create unhappy customers downstream as well.

To be sure, these shortfalls aren’t always easy to find. Insights can be lost amidst the huge volumes of data which are too complex to parse with the human eye.

Still, manufacturing data analytics can help illuminate the cost and efficiency of every component in the production life cycle, from the time the order is placed to the moment it rolls off the truck. Advanced analytics help managers make better decisions by providing a visualization of how each component impacts the final. This, in turn, helps identify the hidden, margin-killing costs of certain components, tracking them back to a weak supplier that might otherwise have escaped notice.

No. 2 - Leveraging Analytics to Predict and Avoid Shutdowns

Manufacturing systems operate under heavy loads, so any stoppage can translate to spiraling losses. Unfortunately, the best solution that many companies have for dealing with failures is preparing for a fast response once a crisis has already happened.

By incorporating big data analytics, companies can develop manufacturing systems that can consistently gauge their own need for repairs. It’s a brave new world in manufacturing with analytics that can predict the lifespan of hardware, proactively generate alerts for managers, and even order replacement parts before they run the risk of failure.

No. 3 - Creating Better Demand Forecasts

Every budding MBA who has taken a class in operations knows the so-called “Bullwhip Effect,” where minute fluctuations in demand at the retail level can translate into major supply changes at the manufacturing level—the painful lash of the metaphorical “bullwhip” for people in manufacturing.

By combining existing demand data with predictive analytics, manufacturers can build a more precise projection of downstream purchasing. This move towards better demand forecasts can reduce snaps of the whip on the production side—that is, the major fluctuations between peak production and downtime—and can increase chances of having sustained, orderly production and sales demand.

No. 4 - Managing Inventory More Effectively

Another sometimes-overlooked aspect of the manufacturing process is inventory. Once products are ready to be shipped, they are often placed in warehouses before they leave for their final destination. At this point, seconds and minutes become important, especially in a world that is increasingly embracing “just-in-time” and zero-inventory models. Beyond ensuring on-schedule delivery for clients (where speed and timing translate to being able to charge more for goods), manufacturers have an incentive to reduce storage costs.

Managing warehouses is about more than just finding space for products to wait. Establishing efficient arrangement structures, better product flow management, and the most effective replenishment procedures can improve operations, reduce overhead, and widen margins. Advanced analytics make it easier to understand how to improve your inventory and better manage your warehouses.

No. 5 - Joining Multiple Data Sources

A solid analytics platform can make looking at a single, simple data-set easier and more intuitive. To be sure, to driving innovation in the manufacturing space sometimes requires new ways of combining, or “mashing up” multiple, disparate, complex data sources. The former helps provide better control over the data that you already know is important. The latter helps managers test new theories, vet “best practices,” and make new discoveries that can provide new efficiencies and sustainable differentiation in their manufacturing operations.

No. 6 - Using Transparency as A Differentiator to Deliver Better Service (and Margins)

Manufacturing can often be a low-margin business where contracts are won or lost on pennies per unit – not the type of place where companies would normally seek to differentiate with high-touch service for their customers.

Embedded analytics can be used to provide a level of transparency to a manufacturer’s customers, offering differentiation and value. In essence this transparency can be used to differentiate on service—giving buyers a deeper level of trust, and helping them save time and money—without a significant increase in headcount or overhead.

This puts manufacturers in a position where they can be an efficient low-cost producer, while also a service leader in their space. Over time, as customer use the analytics to get answers to pressing questions in real time—things like long-term product availability, failure rates of subcomponents, and energy usage of finished products—manufacturers can leverage this transparency to expand on their relationship.

The continual pressure to modernize and upgrade in the manufacturing world is nothing new. What is new is the need for improved ways to derive insights from data. This new emphasis on analytics can pay rich dividends for manufacturers that can master their own data, helping them eliminate inefficiencies and charge more for their products. Over time, these masters of data will be able to do more than just improve their own operations; they will become a valuable and an irreplaceable partner for their customers.

Amir Orad is CEO of Sisense.

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