Track These 5 Things to Boost Manufacturing Productivity and ROI

Data can unleash major ROI and productivity in your manufacturing plants--if you know how to use it. Here are 5 things you need to track with sophisticated data strategies.


By Micah Brandenburg

Why are analytics vital to success in the manufacturing industry? If you can measure something, you can improve it. And if you can improve it, you can stay ahead of the competition.

Manufacturers constantly look for ways to optimize operations and coax every drop of productivity out of each employee, machine, and process. Most manufacturers are already doing as much as they can to stay ahead by making the most of every asset.

But there’s one asset you might not be using to its full potential: your data.

Every day, thousands or millions or even billions of data points flow through your company. They give you information about your customers, products, equipment, employees, and processes. By tracking them and analyzing them, you can boost both productivity and profit.

But many manufacturers haven’t yet switched over from manual analysis to software solutions. Why not? One reason is that they don’t have the right tools or the support to implement them.

Any analysis initiative has to start with clean data, integrated storage, and clear objectives. With that said, let’s take a look at some of the ways you can improve your operations by tracking key analytics.

1.  Productivity

Productivity metrics help you identify bottlenecks in your production lines or identify and solve problems to create more efficient processes.

Example 1: Assess speed, quality, and downtime of individual machines to determine your overall equipment effectiveness score (OEE). By utilizing equipment more efficiently, you can increase production and reduce costs.

Example 2: Profit-per-hour analytics dig even deeper by tracking machine operations over time. This data can be used to monitor specific processes in real time and make adjustments that increase efficiency.

2. Predictive Maintenance

One of the most impactful innovations of AI for the manufacturing industry is the ability to predict when a machine needs maintenance before it goes down. By examining historical data on the machine, the software can prevent lost productivity due to downtime and keep your production lines operating at optimal capacity.

Example 1: Track performance across machines, lines, or units over time to see where problems have occurred and identify which lines aren’t producing as they should. You can use this information to pinpoint problems and increase your overall yield.

Example 2: Track productivity metrics to determine how well an individual machine is functioning. Use the data to identify maintenance needs and to strike the right balance between output and energy consumption.

3. Supply Chain Dynamics

There are literally hundreds of metrics that flow from your supply chain data, including inventory turnover, freight costs, order cycle times, shipping rates and times, and delivery accuracy. Supply chain analysis and modeling tools help you create an optimal supply chain strategy while also generating the greatest profit.

Example 1: Use delivery rates and shipping data to predict shipping delays that may happen as a result of weather or natural disasters. Use this information to create a backup strategy (such as alternate suppliers) so your production doesn’t drop.

Example 2: Reduce problems related to overproduction, lack of inventory, and transport delays by tracking asset utilization rate, wait times, and current stock levels. You can use these numbers to predict supply chain needs early before they undermine profits.

4. Yield/Energy/Throughput (YET)

YET analytics track operational efficiency, making sure that your production lines function at full capacity while reducing energy requirements. Even small improvements can radically affect the profitability of your plant, with fractional gains delivering compounded benefits.

Example 1: When low performance of individual machines causes hang-ups in your production capacity, you can use data from the machine to analyze the variables that contribute most to productivity. Using the data, operators can increase output by making small adjustments to those key variables.

Example 2: YET models can be used to pinpoint fluctuations in individual components of the manufacturing process and to understand performance variations at different plants. By stabilizing those elements and creating a consistent, uniform procedure, you can boost both productivity and profit.  

5. Quality Assurance

Data is generated and stored during every part of the manufacturing process, and that information is gold to your quality assurance engineers. By tracking key performance indicators and analyzing them for inconsistencies or deviations from standard, your plant can predict the potential for quality problems before they happen.

Example 1: Testing is an essential part of quality assurance, but analytics can streamline your testing procedure by correcting errors and identifying anomalies earlier, during the manufacturing process. Companies that implement this kind of data analysis successfully can expect to see significant cost savings over time as well as a reduction in liabilities.

Example 2: By analyzing performance data from multiple locations, you can identify the specific factors causing variation in quality among plants. This is the first step in defining best practices that can be used by all locations to generate the best possible performance.

Taking the Next Step in Your Manufacturing Plant

Of course, none of these metrics are new for the manufacturing industry. The key difference is that the right software helps you gather more data, makes it more accessible, and helps you analyze it more effectively. It would take human workers exponentially more time to comb through all the data and identify trends as compared with the analytic capabilities of well-designed software.

Big data isn’t just about what information you collect. It’s more importantly about using that data to gain insight into your processes, which in turn will drive better business decisions and improve performance.

So what’s the next step? 

Define your business objectives

The first step is to take a systematic, strategic look at your company, from the technology you use to the processes on the floor. Where are the opportunities for improvement? Data can make your company more efficient, improve sustainability, and reduce costs—but only if you know how to use the information you have. So look for places where performance, productivity, quality, and speed can be improved by capitalizing on data opportunities.

Assess your data collection and storage strategy

Data collection must be systematic and accurate if it’s going to be effective. Ask questions like:
* What data will bring us the most benefit?
* Where and how will we collect it?
* How frequently do we need to take data measurements?
* Where will we store it and for how long?
* How will we share it?
* How will we ensure security?

If you currently store your data in disconnected siloes, you’ll need to integrate that data into one central place before you launch an analytics strategy.

Develop an effective software solution

Software projects often fail because companies don’t plan sufficiently on the front end. At Worthwhile, we work closely with you to select the right technology for your needs, understand your requirements and scope, create a project timetable, and develop a finished product that not only gets the job done but also supports your long-term business objectives.

The key to effective analytics is viewing your data as an opportunity. You have the potential to understand your processes, solve problems and predict needs based on the data you already have available to you. And that information can in turn help you improve quality and efficiency, promote sustainability, manage costs, and boost revenue.

Micah Brandenburg
Micah leads our sales and consulting teams. His passion is enabling business strategy with technology solutions.
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