In my role as a sales leader for Sight Machine, I often work with customers to help them define a strategy for digital manufacturing. One challenge I’ve seen arise again and again is the misalignment and sometimes conflicting agendas between corporate-level executives who are part of innovation or digital transformation teams and plant operational leaders who manage one or more facilities.
The corporate digital transformation teams are often looking at ways to evolve or transform the company’s business model, focusing on initiatives such as Industry 4.0, Internet of Things, and cognitive computing. Conversely, the plant executives are tasked with improving operational performance in the short- to medium-term, and are focused on investing in solutions that address operational metrics or specific issues, such as causes of scrap or enabling predictive maintenance. These different perspectives can create a disconnect between team strategies and priorities that ultimately impacts individual digital manufacturing initiatives.
These different views can not only slow down the progress of digital manufacturing projects, but worse, can cause one of the groups to inadvertently sabotage a project due to misunderstandings about the objectives and scope of the initiatives.
Let me give you some examples.
One of our projects at a large multi-national manufacturer was initiated by the digital manufacturing team who was championing data-driven manufacturing capability and engaged us to build a data and analytics platform for a number of plants. This team was knowledgeable of the digital manufacturing software landscape, had a vision for the future of the company, and was excited about concepts of advanced analytics, increased visibility, and cross-plant comparisons.
The innovation team selected a plant that suffered from stubborn quality and productivity challenges as the starting point for our efforts.
Unfortunately, the project soon stalled, as the plant team was unable to provide the local IT, engineering, and operational support for the project. Digital manufacturing initiatives need local resources who understand the complex machine to process to sensor data relationships that serve as the foundation for advanced analytics. Without local resources fully committed and allocated, the effort soon lost momentum and was shelved.
Our post-project discussions with the customer revealed that the local team had not bought into the concept of increased visibility with the corporate team. There were concerns that local data analysis resources could be lost or that the corporate team would begin driving more local control over operational projects.
In retrospect, our customer learned the importance of communicating the long-term vision for company transformation to the local plant teams and collaborating with plant resources on how to align digital manufacturing projects to that vision. To get local buy-in, it’s critical that the plant teams understand how their plant’s digital initiatives are supporting the development of long-term corporate capability and how this vision will benefit local operations and resources.
On the other end of the spectrum, we were engaged on a project initiated by the local plant team that focused on their short-term needs of solving complex quality issues. Their local data scientists had been unable to determine what was causing their consistently high scrap rate, even with the assistance of a myriad of consultants. In this case, the local team was concerned about getting other corporate groups involved because it would slow down the project. And indeed, because of the local buy-in, this project progressed quickly – with needed resources committed and aligned. Eventually, our data-first approach – integrating data from a broad set of machines, parts, and quality systems into an analytics platform, was able to pinpoint the root cause of the quality issue.
Unfortunately, because the corporate teams did not have a sense of ownership for the project, its impact was limited to this one facility. Without advocacy from the central digital manufacturing group, no resources or budget were allocated to scale this capability across the enterprise. With this approach, a plant invested time and resources to address a challenge, but the next problem at the next plant will require a similar level of effort and investment. We’ve learned that even successful projects like this one, often do not scale to other plants or help transform the organization.
Today, there is too much at stake to allow these internal organizational issues to derail transformation. Global manufacturers must align corporate teams defining the long-term vision for the enterprise and local plant teams tasked with improving specific KPIs and metrics. It’s the only way a manufacturer can start to change its DNA to accelerate their digital manufacturing journey.
To support manufacturers with their digital initiatives, Sight Machine has developed a tool that captures the learnings from our engagements with some of the leading global manufacturers. Our Digital Readiness Index (DRI) offers a methodology that lets companies not only measure the readiness individual plants for digital manufacturing, but also ensure the organization is asking the right questions regarding the cross-enterprise alignment needed for projects to truly be successful.
Visit http://sightmachine.com/digital-readiness/ for more information