Digital technologies are transforming manufacturing. While you may not know exactly where you want to end up or what digitization means for your organization, it’s important to identify the kinds of business objectives that digitization can support.
Digitization is all about transforming manufacturing operations using the latest technology—and it often starts with connecting factory floor equipment. Over the past few years, the cost of components that support connectivity has fallen dramatically. Consumables like RFID tags now cost very little to include in products. Sensors are becoming more affordable, increasing the amount of new equipment sold with sensor capabilities built in, while older equipment can be retrofitted, or IoT-enabled, at lower price points. At the same time, it is now possible to collect and analyze massive quantities of sensor and device-generated data, thanks to technologies like the cloud and advanced analytics.
Digital transformation means harnessing capabilities like these to gain insights that you can use to make your manufacturing operation faster, more efficient, and more flexible. Benefits of digitally-enabled manufacturing can include:
In addition to understanding the possibilities, it is important to determine target business objectives. This helps provide the foundation for a business case and serves as a benchmark for proving value. It is also important to start small and identify a specific place to start.
Experimenting with a solution that allows for simulation gives you a no-risk way to see what digitization can accomplish. Simulations don’t require connecting any of your actual equipment and won’t impact your operations.
Even if it’s clear that digital transformation holds great promise in theory, it can be challenging to pursue in practice. That’s because the path forward is often unclear. It’s common to think something like this: “I see the overall value of digitization and Industrie 4.0, and I know I need to make changes, but I’m not sure where to get started.”
The good news is that getting started doesn’t require an all-encompassing project scope, or a precisely optimal starting point. The key is simply to get started somewhere with a focused initial project, and to experiment and iterate.
Connected equipment simulations can help you explore your options in a low-risk environment. A simulation gives you the freedom to explore how digital changes could offer value, such as better visibility, without affecting operations.
By exploring simulated interfaces and dashboards, you can get a sense of how equipment all over the world can be viewed holistically, including both an overall view of performance and detailed insight into the status of individual machines. Even if connecting operations around the globe is a much later step, seeing the results of a simulation firsthand can help you refine your objectives and get a better sense of what’s possible.
When deciding on a solution to support this effort, it is important to select technology that enables fast and easy set-up of both simulations and real-world assets. For example, the Microsoft Azure IoT Suite connected factory solution allows you to quickly generate a simulated environment, and can be deployed in minutes.
Armed with a better understanding of what’s possible in theory, the next step is to experiment by connecting equipment in the real world.
Digitization doesn’t have to be accomplished all at once. Connecting a specific set of equipment enables you to experiment on a small scale and at your own pace—all without disrupting operations. This creates a foundation you can build on and scale out across your organization when you’re ready.
It’s one thing to see simulated data, and another thing to hook up your own equipment and see real-time data and insights from your own operations. Fortunately, with the right technology, this transition to connecting your own equipment can be a straightforward one.
The same technology solution used for simulation should also offer a path for connecting your own equipment and processing the data that the equipment generates. Connecting equipment can be relatively simple – at a basic level, it involves:
From there, you’re ready to start exploring real-time data from your equipment.
Keep in mind that a flexible, secure technology solution is critical to making this process painless and straightforward, minimizing potential risk and disruption. For example, the Microsoft Azure IoT Suite connected factory solution uses the existing software on a machine to connect – there’s nothing new installed on the machine itself.
The right kind of solution also enables you to connect equipment without taking it offline, and to connect individual pieces of equipment at your own pace, with no need to connect everything at once. Want to experiment by connecting one machine now, and the entire assembly line starting next week? You should have the flexibility to do that. Want to connect everything quickly and start getting insights as soon as possible? Your technology shouldn’t slow you down.
With connected equipment comes greater visibility into operational status, anomalies, trends, and other performance insights. This visibility is the foundation for making a wide array of operational improvements.
The value of connected equipment is the data it generates. Once machines are connected, you start to benefit from real-time visibility into key performance indicators. There are a number of insights that can quickly be gained, such as:
These simple but critical data points can make a big difference, enabling a better understanding of operations, better decisions, and greater responsiveness. But it’s not enough to have the data. Insights must be easy to glean via intuitive, visual dashboards, and must be readily available to the people that can act on them, such as shop floor technicians and plant supervisors.
As in previous phases, it’s helpful to experiment. With a robust technology solution, you can quickly identify the data points to collect for KPI calculations and root cause analysis. In some cases, you may find you are collecting data that isn’t needed, or isn’t providing insight, and adjust accordingly. Cloud-based solutions make it easy to fine-tune the information you collect. For example, with the Azure IoT Suite connected factory solution, you can control the data that gets collected without having to physically send someone to a machine.
Data from connected equipment is also the foundation for uncovering trends and patterns. For example, collecting and analyzing historical data enables you to establish your own performance benchmarks across similar equipment and across plants. By comparing real-time data against benchmarks, you can constantly monitor whether a piece of equipment is operating within normal ranges, and identify subtle anomalies that emerge over time.
A digital approach to operational visibility offers tremendous potential value. A recent Automation World survey found that nearly three-quarters of respondents use plant-floor data at the corporate level, but a spreadsheet was cited as the most common reporting tool.1 With a connected factory solution, operational intelligence is available automatically and immediately, enabling better, faster decisions at the plant and at the corporate level.
Connected equipment is ultimately useful when it drives changes. Anomalies can be quickly detected and fixed. Maintenance schedules can be optimized to minimize disruption. There’s no limit to the kinds of data-driven improvements that become possible.
The visibility you gain by connecting equipment adds value when those insights drive operational changes. Better visibility and insight makes it possible to identify issues and respond faster, make better decisions, and enact other operational changes.
For example, detecting anomalies through real-time insights gives you the ability to intervene more quickly. Take the example of a machine that is displaying signs of an impending failure, such as increased power consumption. With the ability to monitor performance via a live dashboard, you know immediately when those conditions occur, and can quickly dispatch a repair technician to address the issue.
Similarly, monitoring against benchmarks enables an even more proactive approach. Consider a scenario where bearing temperature in a piece of equipment is increasing. If you have established a benchmark provided by the equipment supplier, or by analyzing data from similar machines used for similar purposes, you can pinpoint when the temperature exceeds the normal range and schedule maintenance before failure occurs. Apply this benchmarking and monitoring approach across lines and sites, and your ability to preempt failures and reduce costs increases dramatically.
Another benefit of visibility is the ability to identify under-performers and out-performers, and make corresponding improvements. For example, one site may have particularly high utilization of a certain machine, while utilization at another is particularly low. With the ability to see performance side-by-side, you can more easily identify these outliers and investigate what’s behind them. You may uncover issues like machines not being run or maintained optimally, or you may find utilization differences related to different operators. You may also discover best practices that you want to roll out more broadly. When you can compare performance across equipment and over time, these kinds of variations become more apparent, and easier to act on.
These are just a few examples of potential operational changes that connected factory visibility supports. Other types of changes may include optimizing production processes to reduce waste and bottlenecks, adding or replacing equipment, and adjusting staffing or training procedures. Once you start collecting data automatically and gaining visibility, finding operational improvement opportunities and making changes is a natural next step.
Scale from a single assembly line to an aggregated view across your operations. Add new equipment and capabilities at your own pace. Use your solution as a starting point for expanding to scenarios like predictive maintenance.
By this point, you’ve built an understanding of what it takes to connect equipment and the insights you can gather. The next step is to expand your project—for instance, by moving from a connected assembly line to connecting the entire plant, and then to connecting multiple plants around the world. With a single consolidated view of your operations, you stand to gain better, faster insight into performance, and the ability to compare performance across the organization. You can also work with third-party contract manufacturers to connect their equipment and get insight not just into your own operations, but those of your partners as well
Beyond scaling to more equipment and factories, you can also choose to expand the capabilities and scope of your digitization efforts. Visibility into current performance and historical data are a powerful foundation for other digital changes, such as predictive maintenance programs and an optimized energy management approach.
For example, a natural next step is to consider predictive maintenance. By applying predictive analytics to performance data, it becomes possible to not only identify when maintenance is imminently needed, but to accurately predict maintenance needs well ahead of time. This is an example of how layering on new capabilities can deliver additional value – in this case, using predictive analytics capabilities like machine learning to detect subtle patterns and changes in a set of historical performance data.
Similarly, you may choose to integrate performance data into other business applications, such as a field service system. In this case, if an anomaly is detected, a service alert could automatically be triggered and a technician automatically scheduled to look at the equipment with the potential problem.
Digital transformation can take many forms and mean many things for your operation. As you enable new scenarios and scale, the key is to continue experimentation using a phased approach, and to continue fine-tuning as your needs and environment evolve.