The pursuit of optimized production is not as farfetched as it used to be. Today’s manufacturers have access to more useful data, and they possess advanced tools that supply more information than ever before. In spite of the possibilities and the technological advances, however, many manufacturers continue to struggle.
Technology has enabled manufacturers to take huge steps forward in terms of operational improvements. Even so, technological change can be a double-edged sword. Whereas advancements in technology can facilitate improvements to production processes, they often create challenges for a facility’s staff by introducing complexity. A survey performed by GatePoint Research highlighted that process manufacturing executives are increasingly worried about the complexity of their manufacturing operations. Almost 90% of executives scored the need for less complexity in their processes at a three or above on a five-point scale.
When it comes to the deployment of and expectations for analytical technologies like control loop performance monitoring (CLPM) solutions, Control Station has identified three challenges that manufacturers commonly encounter. Those challenges are Time, Data, and Knowledge.
Progress Takes Time
When it comes to analytics and process optimization, manufacturers benefit when they take a long-term approach. Optimization is not an end destination; it’s a journey. Indeed, as soon as a production facility’s performance goals are achieved, it’s time to establish new ones.
For such a long-term approach to succeed, it’s essential to articulate a clear vision and to establish buy-in at all levels of an organization. This rarely happens overnight as change is hard. It should be expected that some employees will be skeptical of what might seem like just another new initiative. Accordingly, leadership needs to properly convey the benefits of data analytics and optimization. The success of safety programs across the process industries proves the value of buy-in. With their long-term commitment to safer work environments, manufacturers put in motion changes that resulted in meaningful improvements involving and benefiting everyone.
Beyond buy-in there are other time-related aspects that must be taken into consideration. There’s the need for effective training, the creation of clear goals and methods for tracking performance, and the public recognition of milestones achieved. Training isn’t a one-and-done requirement as staffing will change over time. What’s more processes and technology will evolve and so should training. Similarly, tomorrow’s goals will be different from today’s. Planning for the each subsequent challenge will take time and it will require an eye towards continuous improvement. Regarding recognition, regular celebrations of accomplishment and the people who are key to each success reinforces buy-in. That takes time, but it often produces disproportionate value.
Not All Data Is Equal
Data is the basis for decision making and it is especially important in the context of process optimization. It’s hardly a surprise that some types or sources of data are not as valuable as others. Knowing the difference is key.
In the absence of high quality, high resolution data, manufacturers will struggle to either identify underperforming systems or optimize their processes. It’s understood that lower cost sensors as well as effective communication and storage capabilities allow manufacturers to track nearly every aspect of production. Still, more data is often just that – more. In order to uncover new insights, the data being used must be suited for the task. Often that means higher quality, higher resolution data.
Another consideration for data relates to its availability. The approach of isolating different sources of data and maintaining distinct silos has been shown to be counterproductive in most cases. It’s not unusual for such a siloed approach to lead to blind spots in a manufacturing process. No one wins when data that could’ve contributed to the analysis effort was ultimately unavailable. An integrated data architecture allows manufacturers to tap into that resource and makes it possible to uncover new and potentially impactful insights.
Knowledge is Power
As the old adage goes: You don’t know what you don’t know. To that end it’s essential to be honest about the limitations of the team assigned to a production facility’s analytics initiative.
There are countless factors that influence production and that contribute to sub-optimal performance. Consider the different functional areas within a plant: Engineering, Operations, Maintenance, and Management. It’s rare that any one individual possesses the domain expertise from all of those areas. Individuals that do are so rare that they’re often referred to as “unicorns”. Realizing the need for a diverse team and access to different functional insights is another key to success in analytics.
For an effective process analytics initiative, experience suggests that a Process Engineer, a Data Scientist, and a Technology Specialist are all needed. Such a team would combine the domain knowledge needed for understanding the nuances of a facility’s production processes, corralling KPIs that effectively characterize performance, and applying appropriate analytics tools for crunching the data. For some manufacturers, those individuals either aren’t on payroll or they’re committed to other critical projects. In those instances, it’s wholly reasonable to supplement with a trusted third-party resource.
Understanding and acknowledging these three truths can help a manufacturing team to devise and execute a proper process analytics and optimization strategy. To learn more about our experience in analytics and/or our process analytics platform, contact us at firstname.lastname@example.org.
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