Cloud-based Manufacturing Analytics: Case and Barriers
September 01, 2022
September 01, 2022
High tech manufacturers today face a wide range of significant, persistent challenges—from infusing more agility to deal with current disruptions, to boosting efficiency and throughput, to ensuring consistently high quality. Yet most manufacturers are hamstrung in their attempts to address these challenges because they lack the visibility into their shop floor operations—visibility that could enable them to identify and correct issues that are undermining performance.
Consider the situation one multinational technology company faced. The company had no formal way to track its performance at the machine or line level, and typically only discovered it was behind schedule in production through anecdotal insights from operators. By then, the company was weeks or months behind in what it promised its customers, leading to inflated costs, quality issues, and poor on-time delivery—which, in turn, compromised customer loyalty and put revenue and margin at risk.
How can high tech manufacturers gain greater visibility—at scale—to avoid such negative outcomes? One impactful way is to embrace cloud-based analytics tools, which is the subject of this two-part blog series. In our first installment, we explore how manufacturers can benefit from such tools and some of the major barriers that, so far, have prevented more widespread adoption and use of them.
The fact is, because they lack visibility, manufacturers are leaving a lot of money on the table. And that’s why analytics tools have become increasingly important. With analytics both at the edge and in the cloud, manufacturers can gain greater, more timely insights that can help them improve operational performance and, consequently, boost revenue and margin.
Because they lack visibility, High Tech manufacturers are leaving a lot of money on the table.
Service is a good example. With more intelligence on products installed at customers’ sites, a high tech manufacturer can create additional, relevant service offerings for each customer—such as maintenance contracts through which the manufacturer monitors customers’ equipment and ensures it’s always running at peak performance. That means new revenue streams for the manufacturer. At the same time, by using this equipment data to proactively identify and correct potential issues before they become problems, the manufacturer eliminates the need—and associated expense—for equipment under warranty to be shipped to a facility for repair. That reduces the number of warranty claims hitting the bottom line.
The technology company just mentioned has embarked on a two-pronged journey toward greater use of analytics to address its own operational challenges. We helped the company initially implement a new analytics application at the edge on the shop floor that enables operators to see, in real time, exactly where production is compared with plan and to know when products will be completed so the company can alert customers when they’ll receive them. This has helped the manufacturer identify and quickly resolve inefficiency and quality issues specific to each site. But that’s not the end of the story. We’re now working with the manufacturer to take this shop floor data to a cloud-based analytics solution, which will enable the company to combine site-specific data with data from its ERP, PLM, and other systems to identify macro-trends that can inform predictive, prescriptive, and preventive measures.
If there’s so much value at stake, why aren’t more manufacturers embracing cloud and analytics? In our experience several barriers stand out.
Ironically, one of the prime barriers to using analytics in manufacturing is also the reason it’s an opportunity: proof of value. We see that companies tend to lead with proofs of concept and surgical applications of analytics—i.e., where the opportunity presents itself most intuitively.
The challenge here, though, is that where intuition tells us these results would be incredibly measurable, the reality is not necessarily as clean. Proof of value must strike the right balance between speed to insight; alternatives ways that manufacturing personnel could have analyzed the data themselves; and the increased coverage that a holistic analytics solution can provide. The challenge, then, lies in managing the pendulum between these surgical, proof-of-value use cases and investing in manufacturing analytics at scale, regardless of the target use cases.
Another barrier involves security concerns. For industries such as high tech, in which intellectual property (IP) related to manufacturing capability and process design are critical to success, protecting those capabilities and controlling access to the underlying data associated with them is critical. Any successful implementation for analytics that uses cloud capabilities, then, must consider an organization’s appetite for risk and comfort level with the cloud.
There also are barriers related to disconnected systems and limited to no flow of structured data within systems. In many cases, no data is available due to old systems or machines that still are functional on the manufacturing floor. Lack of data makes calculating the return on the analytics investment difficult.
Finally, the most common risk or barrier involves people: the skills they have to use these new tools—which, in many cases, are absent or need strengthening—and their willingness to change to new technology.
How can manufacturers overcome these barriers and begin reaping the benefits of analytics and cloud? Read Part 2 of this Essay