Data strategy and effective planning are critical to your organization
Welcome back, Troubleshooters! Thanks for tuning in again to TST. As you know, recently we’ve been posting a series of blogs on Cutting through the Noise, about data strategy and managing the ever-increasing amount of manufacturing data to hear the relevant signals.
In Part 1(Managing big data in manufacturing), we made the case that there is so much data coming in to manufacturers now that it can be overwhelming to try to sift through it all looking for the information that is actually meaningful. This glut of data is like noise that drowns out the truly valuable signals—relevant data streams that will lead to increased efficiencies and profits. In Part 2, we laid out the broad steps for finding and leveraging that relevant data. Last week, in Data analysis in manufacturing, we looked at specific steps for developing a data strategy in your business.
Today in Part 4, we’re going to take a deeper dive into those steps. If you’re a Director, VP of Operations, or plant manager who wants to inject some clarity into the potentially confusing world of developing a data strategy, this post is for you.
Developing your data strategy—goals and objectives
If you recall from last week, the first step in developing your data strategy is to sit down and think about your business’s goals and objectives. Goals are general; for example, increase revenue, increase efficiency, provide better customer service, improve employee training, reduce carbon emissions, improve ISO rating, etc.
From there, you will need measurable objectives to help you reach your goal. If the goal is to increase revenue, then one objective might be to increase production by 5%. If you want to increase efficiency, the objective may be to increase operations reliability by 10%. If you want to improve your ISO rating, your objective may be to improve workplace safety by reducing the number of accidents and injuries by 20%.
Most businesses already have their goals and objectives in place. Manufacturing executives can use them to help clarify the main purpose of their data, that is, what they hope to get from it over the next 5, 10, and 15 years. At least one goal of most businesses is to increase profits; where are the best opportunities in your organization for doing that—the areas with the most potential for improvement? Those are the areas where leveraging data may prove most valuable.
Once you know your objectives, you want to find data streams that can contribute to the specific goal or objective. You need to think about the data streams available to you now, and how they can advance the goal. But you also need to think about streams that will be available in the future.
Consider the contribution of new manufacturing technologies
Technological advances may contribute new data streams to your enterprise in the coming years. Do any of these align with your data goals and objectives? How can you be preparing now to realize your future goals?
Thinking about the data that will be available to you in future involves being aware of trends in your sector and manufacturing in general. Obviously, no one has a crystal ball. There are technologies down the road that we haven’t even begun to imagine. But, there are a lot that we have. Think about the future role in your business of transformative technologies such as robots, cobots (collaborative robots that act cooperatively with humans on the production line, acting as assistants for tasks that can’t yet be automated), sensors, and the IIoT—the interconnectedness of all aspects of the smart factory. Take a look at state-of-the-art brilliant factories and see how organizations are already taking advantage of complete digitization and making the vision of whole connected enterprises a reality. Which of these innovations are you likely to adopt in 5, 10, or 15 years? They will be rich sources of manufacturing data—start planning now for how these data streams can be harnessed to improve operations and other processes. What will you need to have in place to transform raw data into useable information?
Exploit opportunities in unexpected departments
As you’re developing your strategy, consider each area/department within your industrial unit and how data may be leveraged there to help attain the company’s overall goals. Production line processes and supply chain logistics are some of the obvious areas, for good reason. You may already have begun utilizing data to find efficiencies and reduce waste there. But don’t forget about the others that aren’t so obvious and may also prove fertile soil for helping you obtain your goals, such as your training and human resources departments. They may be a source of data that is currently being under-utilized, or they may have the potential to provide useful data with a few updates.
Look at maintenance training, for example. Production-line downtime is massively expensive and a thorn in the side to manufacturers. Maintenance professionals are under extreme pressure to repair failed equipment as quickly as possible. They need to be trained to find and fix electrical faults quickly, efficiently, and safely. A product such as Simutech Multimedia’s electrical troubleshooting training software provides a digitally simulated learning environment that simultaneously trains employees and sends data about their progress to program admins, who can then use that information to decide which trainees are best able to reduce downtime. So, making the right data-driven training decisions can make a huge difference to the overall bottom line.
When applied in a slightly different way, data from the same software can also reduce waste in the HR department. Customized tests conducted during the hiring process can assess the aptitude of potential maintenance employees, thereby avoiding the significant costs of staff churn, and training staff only to lose them a few months later when it becomes clear they were not a good fit for the job. Making the upgrade from training by traditional methods to a digital simulation solution will provide a new data stream that can form part of your data strategy for the immediate future.
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