Why I Joined Sight Machine: To Make CIOs Heroes

CIOs are seldom the heroes of the story in manufacturing. I want to change that by giving them superpowers to help operations teams improve productivity through better use of manufacturing data.

Over almost twenty years, I have been privileged to serve in Information Technology (IT) leadership positions in a wide variety of manufacturing industries. Across wire and cable, construction equipment, chemicals, and discrete manufacturing, I watched Industry 3.0 and then Industry 4.0 emerge and become buzz words (albeit with tremendous substance – see the great work the WEF is doing on this topic). As a CIO working closely with operations teams, I understood how Industry 4.0 could deploy sensors, connections, and data infrastructure to collect useful data and push it up to the cloud, or into analytics platforms.

Turning Mountains of Manufacturing Data Into Real Outcomes

But in my mind, a big question always remained. How could we use these mountains of data to improve productivity and become more competitive? How can we contextualize the vast volumes of data pouring out of all types of manufacturing equipment and data systems? It’s one thing to collect data from all the operational systems of a plant – PLC, QMS, MES, ERP. It’s entirely another thing to turn that data into real value and outcomes like better quality, productivity, sustainability, or a higher OEE. The sheer diversity of these data streams means that any project to transform this data into business impact and actionable insights requires deep industry experience and superior data science skills. This is why, for most companies, a low single-digit OEE improvement is considered a big deal.

I had learned the hard way why it’s difficult to get value from manufacturing data. In one of my previous roles, we built a limited manufacturing analytics solution as a pilot. We quickly realized it was too challenging for a single company to build, manage, maintain, and scale such a solution to produce tangible values with limited knowledge and experience in data engineering, architecture, and modeling. Further, we lacked expertise in the diverse ways of analyzing data in the manufacturing context. This problem equally applies to Information Technology and Manufacturing Technology.

In my last job as a VP – IT, I helped my company deploy Sight Machine. It was a remarkable 14 weeks from the start date to “go-live.” I was skeptical that Sight Machine would hit that deadline, but they did. When we first started seeing analytics deliverables through the Sight Machine analysis platform, I immediately recognized that this was a platform that finally answered the question of “How can we create value using manufacturing data?”

Building In-House Data Science Competency Is Challenging

Most mid-market IT organizations struggle to build manufacturing data expertise with data engineers, data architects or data scientists, and robust implementation processes. I have learned with my experience that it is equally important to connect the data dots and quickly build useful data models and analytics that operations and plant teams could see and easily use to improve their performance.

As a CIO, my goal has always been to put the right analyzed information in the right people’s hands at the right time. This endeavor requires substantial and sustained investment in both IT and Operations Technology. IT budgets in industrial manufacturing industries have historically been less than 2% of annual revenue. This budget level is below those in other industries like banking, pharmaceuticals, and consumer packaged goods.

Such a low budget does not provide enough funding for sustained changes and digital transformations that require advanced skills, knowledge, and proven technologies. The most effective way to justify investments for digital transformation to a CEO or CFO is if you “self-fund” the project through new value creation and cost savings by improving the effectiveness of the manufacturing operations like quality, availability, and productivity. The payback period should be as short as possible. This is happening right now in manufacturing and its where Sight Machine really performs.

Every Manufacturer Can Use Their Data to Boost Productivity

In my new role, I am looking forward to partnering with other CIOs (and their operational partners) to structure and enable their digital transformations. Every manufacturer should be able to answer the question, “How can we use manufacturing data?”. The answer to that question continues to expand. Production teams and also finance, procurement, quality, and workforce training teams can use manufacturing data – once it’s made simple to use, analyze, and visualize. That makes CIOs and manufacturing leadership heroes, which is the best part of my job.

Learn how Asian Paints achieved a 7% reduction in cycle time within 60 days of Sight Machine deployment.   Asian Paints Transforms Data into Business Impact with Sight Machine Read the Case Study

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