Avoiding Pilot Purgatory: Lessons Learned for Industrial Transformation Success

Avoiding Pilot Purgatory: Lessons Learned for Industrial Transformation Success

A recent report from LNS Research offers insight from industrial transformation (IX) leaders on how companies can achieve success with their IX efforts. The bottom line for transforming operations and improving ROI? Organizations need to assemble the right data, put the right people and processes in place, and select the right goals.

Avoiding Pilot Purgatory: How to Choose the Right Use Cases to Accelerate Industrial Transformation combines valuable data culled from years of IX research and a recent, global survey of 275 companies of varying sizes and a mix of industries (discrete, batch, and process manufacturing). The report is meant to help executives and decision-makers at industrial companies plan and manage strategies for their IX efforts.

Put the Right People and Processes in Place

While the recent survey results identified that just 15% of companies were stuck in early or pilot phases, the industry perception of “pilot purgatory” leaves a much larger footprint. LNS suggests four failure modes for industrial transformation initiatives, two of which concern people and processes. Failure mode 1 is the failure to converge IT-OT (information technology and organizational technology) organizations, culture, and technology architectures, and mode 2 is a siloed approach to data management and decision making.

Put simply, IX initiatives rely on data—the more the better—and limiting available data by siloing portions of the organization means limited solutions. Previous LNS research has conclusively shown that leaders in IX are also leaders in IT-OT convergence, and the report recommends that teams leading IX efforts be cross-functional, including IT, OT, and other affected departments.

A caution on personnel and processes has to do with in-house expertise. LNS’s research indicates that smaller organizations and discrete manufacturers find themselves stuck in “pilot purgatory” more often than larger companies and more often than batch or process manufacturers. This is likely due to a lack of in-house expertise in managing large-scale data and, therefore, a lack of ability to estimate the budget and resources a project might entail. The report suggests companies without this experience in-house might find it beneficial to bring on data scientists with industrial experience and/or manufacturing engineers with training in advanced data techniques for the IX effort.

Start with the Right Data

If you’re hearing the word data a lot, it’s because data is the backbone of industrial transformation. While data can’t make changes itself, nothing can be done without the data to point the way and verify success. As identified above, data must start with a convergence of information from IT and OT—as well as data from IIoT (industrial internet of things) systems—but the IX dataset must also go beyond internal sources. It should include data from people, processes, machines, online marketplaces, and third parties—whatever might be relevant to the organization’s product lifecycle. As LNS points out, “The idea is to use this data to empower new business insights that were previously unreachable. … IX Leaders use data to gain new insights into performance, restructure work to reduce organizational silos, and redefine their business models.”

LNS Research offers a reference—or template—architecture for industrial transformation to provide a framework for technologies, processes, and functions that are required for industrial companies looking to implement transformation initiatives. Unsurprisingly, the architecture’s two foundational layers involve data: Connectivity, Transport, and Security and Data Conditioning and Contextualization. Industrial companies must accurately assess their data and technological readiness with regard to potential use cases if they want to set themselves up for IX success.

Pick the Right Goals (a.k.a. Use Cases)

LNS Research suggests that once you have the right people and processes in place—those that can handle large amounts of data and work cross-functionally across your entire organization—and once you have honestly and accurately evaluated your architecture and technology capabilities, the third key to successful industrial transformation is selecting the right use cases—and building your goals around them.

Ultimately, what picking use cases comes down to is ensuring you’re getting the most bang for your buck. To help companies determine how to do that, LNS has defined 35 individual use cases sorted into six major categories. Survey respondents were asked to rate each of the 35 use cases (on a 1-10 scale) on two criteria: total resources required to implement and potential initiative impact. LNS then evaluated use cases with the lowest resource number and the highest impact number—or potentially the largest gap between those numbers.

Out of the six categories—customer experience, connected supply chain, connected operations, connected worker, connected product, and connected assets—LNS identified four use cases that consistently deliver high impact with low resources.

  • Asset performance monitoring: Gathering sensor data for all systems, including assembly components, robots, closed loop systems, and more.
  • Predictive maintenance for high-cost assets: Using sensor data, rather than usage data or schedule tracking, to improve uptime and reduce maintenance costs.
  • Mobile apps or augmented reality to scale rare or expert skills: Taking advantage of technology to address the scarcity of expert skills and more easily share institutional knowledge.
  • Predictive quality: Leveraging sensor and machine data to better predict product quality.

However, we were most interested in the Connected Product Use Cases, as we see these issues in many clients we work with.  These cases include:

  • Improving product quality
  • Reducing downtime and risk
  • Increasing customer satisfaction through improved service and quality

This category was ranked as having the highest impact on the business but also required the greatest effort, which is no surprise.  Data is usually at the root of this issue, due to multiple CAD systems, engineering databases, and file management that goes along with managing product data and product management processes.  Related initiatives mentioned in the report around Product Use Cases include Industry 4.0, Quality 4.0, EMS 4.0, among others.  

The LNS report concludes with a caution against focusing too narrowly within a company. Industrial transformation tends to start with industrial operations—in the factory, typically—but it shouldn’t be confined there. Similarly, a company shouldn’t focus on customer experience or sales or financial operations alone, but should focus on industrial operations in conjunction with the other business functions. That holistic view of operations will more appropriately address your company’s needs—and help you correctly define your goals so you can achieve transformational results.

To learn more about how to address some of the Connected Product use cases described in the report, contact our team at Adaptive. We understand how to navigate the data challenges inherent in managing product data effectively and efficiently.

For more details on IX use cases, LNS’s reference architecture, and the survey on the state of industrial transformation programs—as well as an extensive list of reference materials and resources, visit LNS Research.