Use Business Intelligence to Make Better Decisions

Collecting and analyzing information about your manufacturing business’s production and practices can help you make strategic decisions swiftly. With data analytics, you can diagnose what’s happening on the floor in real time and predict future scenarios more accurately.

Marie Monet, president of James Moore Data Analytics, specializes in using data to reveal which aspects of an operation are healthy and which are stagnating.

Mike Sibley, leader of the James Moore manufacturing team, talks with Marie about how business intelligence and data analytics can help you discern where your next dollar is best spent.

Understanding the basics of business intelligence

Business intelligence is the practice of gathering and crunching data about your manufacturing operations to help you make informed, targeted decisions.

“Business intelligence enables businesses to use the data that they already have to make better business decisions,” Marie said. “It empowers businesses to be more informed about what’s going on within their operations and strategize more effectively based on historical information.”

Data is one of your business’s biggest assets. You may not realize, however, the full extent of data that you already collect. Accounting and inventory systems, and programs such as Excel, run on databases. Becoming adept at knowing how to find data points in these databases and learning how to make databases “talk”  with one another to produce meaningful information can turbocharge your business’s success.

Marie described data analytics as a form of storytelling. When databases interact, they can reveal key plot points that may have been hidden previously. For example, you can use data analytics to help you predict machine failures, pinpoint a lag in a particular product’s production timeline and plan when to bring on additional labor.

“Data really does provide a window into what’s going on operationally to determine whether your organization is healthy,” Marie said.

Using data analytics and business intelligence to quantify your business instincts

Longtime manufacturers often get a gut feeling when something isn’t right. Data analytics can affirm or assuage that feeling by revealing the underlying components and causes of a problem.

Having data available on a daily basis also enables you to shift from being reactive to proactive in negative situations.

“You’re going to be able to see very quickly when something is starting to fail or when there’s an underlying issue that needs to be addressed. You’re not waiting till the next quarter to see what has happened,” Marie said.

This eliminates the lag time that manufacturing businesses often faced in the past. Companies had to wait until a month had closed out to get information about performance. Now, key data points such as the cost of goods sold, material costs, labor and other quality metrics are available in real time.

“To me, that’s the magic behind all this,” Mike said. “It gives you what you need to know when you need to know it.”

Business leaders can set key performance indicators and markers that should set off alarm bells when passed. Data analytics professionals can work backward from those points to ensure the relevant data is being collected. This allows leaders to take a high-level view of operations and zoom in as needed. If production costs exceed predictions, for example, data analytics can shine a spotlight the cause. Perhaps a new employee needs extra training, or someone has been out sick and needs help from team members.

For manufacturing businesses that take a lean approach, this is especially important. “Having the right data is one of the critical factors to know what your throughput is and how you’re measuring against your expectations,” Mike said.

During the supply-chain delays of the pandemic, manufacturers needed the ability to quantify the products coming in and out of their operation and manage their inventory to avoid storing surplus. Some manufacturers stocked supplies out of fear they wouldn’t be available later. When combined with workforce turnover (and the additional training that involves), businesses risked creating significant amounts of waste and using up raw materials.

Access to and use of of real-time data, however, helped some companies avoid these scenarios. “We need to be able to put our finger on what exactly is happening as fast as possible, so we can mitigate any of those problems,” Mike said.

While data should be available to management, some businesses also display real-time information on screens in their facilities. This can help catch a problem moments after it occurs and adds an extra layer of transparency.

Machine learning expands our data processing abilities

Humans can only make so many correlations on their own. Machine learning—computer models that can draw their own inferences from data—can sift large amounts of data and draw out potentially crucial aspects, such as a disparity in run time in a particular item or areas with unusually large amounts of incomplete products.

Marie joked that centralizing data and using machine learning makes her feel like a data “Terminator.” However, it’s a highly effective way of using business intelligence to illuminate the decision-making process.

“Our brains can only process so much,” Marie said. “Machine learning provides a glimmer of whether there is something I’m not recognizing or realizing or need to look into.”

How data and business intelligence can generate predictions and head off problems

True, data can help organizations spot production problems and conduct a postmortem on past scenarios to better understand their causes. The ultimate goal, however, is using data and business intelligence to predict what’s coming down the pike and plan accordingly.

One way of doing this is to use data to build predictive models. These models can incorporate data from your organization’s history, external databases and current trends such as customer sentiment.

For example, if a predictive inventory analysis uses five years’ worth of your transactional data in combination with information about customer behavior, it could help you spot a 10% increase in orders next spring. With that knowledge, you can ramp up your production cycle in anticipation of that future demand and reap the extra revenue.

Using data – rather than hunches – to predict increases in demand, machine hours, changes in raw material prices and shifts in labor and working capital needs can better inform investment decisions. Data analytics and business intelligence can also predict necessary machine maintenance, helping companies be proactive about inspections and repairs.

“Tying this in with financial projections to not only understand production, but also infer future financial needs, is an incredible boon for manufacturers,” Mike said.


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