When I graduated with a degree in chemical engineering, I was excited to join the workforce and make a positive contribution. Being part of different engineering teams at various companies made me believe that mining the many years’ worth of big data stored in the process manufacturing historians could be a gamechanger. There were many opportunities to contribute to sustainability, reliability, and profit maximization goals that would result in happier customers and a more environmentally responsible industry.
Having been involved in multiple data-driven initiatives at a process manufacturer made me realize that technology must be married with process understanding to effectively address key challenges as part of digital transformation (DT). So, after developing my fundamental engineering skills, I decided to go on the other side of the aisle and help develop technologies to address this challenge.
An article by Harvard Business Review reveals that 70% of all DT initiatives do not reach their goals. Of the $1.3 trillion that was spent on DT last year, it was estimated that $900 billion went to waste. Why do some DT efforts succeed, while others fail?
To answer this question, I’ll share what I wish I had known about technology development when working at a process manufacturer, providing insights to industry leaders and engineers who are making DT a reality for their companies. I will elaborate on three main categories: Decision Making Mindset, Business Operation, and Company Culture and Innovation.
Although I work for Seeq, all opinions expressed are solely my own and do not express the views of any entity.
Decision Making Mindset
Working for a process manufacturer made me realize that the management team leading DT initiatives were constantly faced with hard decisions, such as build vs. buy, and how to invest and when.
When I was part of a project to identify the worst bad actors in our plant to prioritize maintenance, our team faced the dilemma of build vs. buy. When I joined the tech startup, I realized they faced similar challenges and were working towards the same ultimate goal: getting and staying ahead of competitors.
For example, a tech company developing an application can rely on cloud vendors to provide the required infrastructure so they can focus on innovation in application development. Using existing infrastructure not only gives them a boost in the competitive race, but also saves them costs in terms of IT cybersecurity, support, maintenance, and continuous infrastructure improvement.
These learnings not only help with build vs. buy decisions, but also aided in identifying business requirements for technology adoption in OT space.
The decision to invest in the right technology at the right time is similarly challenging, and it requires the correct management mindset. Many technologies promise to help companies reach DT goals, and leadership teams often have a hard time selecting the right technology for their company.
As an aid to making these types of decision, I learned there are two main mindsets in the world of technology: entrepreneur and venture capitalist (VC). VCs tend to invest in multiple solutions and ideas, while entrepreneurs rally around a particular solution with a focus on revenue growth.
If OT and IT business buyers in the process manufacturing industry adopt a VC mindset as part of their DT journey, they can make the best decision for their company when it comes to technology investment. That means allowing for different competing technologies to be used at pilot scale, and then evaluating the ROI of each before making a long-term investment.
Business Operation: Process Manufacturing vs. Technology Industry
For process manufacturers, maximizing output from new and existing infrastructure is a key to maximize revenue. A main area of focus for OT is acquiring best-in-class assets, and then investing in projects to justify capital expenditures. This trend is evolving as part of DT as companies use technology and data to improve their operational efficiency which allows them to cut costs and create a competitive advantage.
In the technology world, I learned that tech companies use a technology stack to improve their operational efficiency. Based on my observations, tech companies are not necessarily looking for the newest hot technology or buzzword. They instead look at technology adoption as a solution enabler and not a business objective, with a focus on implementing the right technology to help the people in their organization. As a result, their failure rate in adopting new technologies is minimal. Tech industry focus and investment is on human assets, in comparison to the traditional approach of investment in physical assets by manufacturers. Leaders and management teams in tech instead rely on their people to drive business growth.
Process manufacturing is evolving, and this is a paradigm shift in the way these industries operate. In this new reality, unless companies invest in their people and enable them with the right technology, it will be hard for them to reach the end goal of DT initiatives. Technologies like Seeq, with its main focus on workforce enablement, are therefore critical for driving value by empowering engineers, operators, and decision makers.
Company Culture and Tech Innovation
Before I entered the tech industry, I had many questions. Was something like the Google search engine developed overnight? How did companies like Google become smarter and develop state of the art algorithms as a part of their technology?
After working with people who were part of technology development, I realized that it all starts when you see a workflow or process that needs improvement. Technology developers watch manual efforts and remove the steps required to complete the task one after another. It starts from there and becomes the advanced technologies we know today. The tech mindset is therefore to crawl before you run in order to innovate and build successful products.
Something that hit me hard was the fact that back in the process manufacturing industry, the culture I experienced was to jump to the last step before understanding the true problem. As an example, in manufacturing, unexpected downtime is one of the main contributors to revenue loss. Instead of trying to understand what was causing the downtime, often times the problem statement was how we could predict and prevent equipment failure.
Many leaders responsible for finding an innovative solution actively look in the market to find a technology vendor promising a product to predict failure. They should instead take a close look into their processes and observe how their people are working today. Then, they can try and empower teams to innovate and improve the processes by deploying the right tools and technologies.
As an example, moving data to the cloud is not required as the first step to perform advanced analytics. Instead, advanced analytics can be performed on data wherever it's stored if it is accessible. This empowers employees to monitor a process/equipment for known poor performance, and to learn from the insights gained from monitoring and known failures to eventually predict failures. Technologies like cloud storage and cloud computing might eventually be needed, but that is not a mandatory first step.
Companies like Seeq understand these types of process industry journeys and provide the right tools for full lifecycle analytics, from experimentation to execution at scale.
Another important cultural aspect for encouraging innovation is celebrating failure. As a manufacturing engineer in the chemical industry, failing in my engineering assessment could lead to disaster, the loss of lives in the worst case.
The consequence in tech is much less, therefore people have a higher risk tolerance. The culture is to fail fast to speed learning because the benefits from learning are more than the waste in time and resources from failure. Therefore, people are encouraged to test their idea in the least expensive and quickest way possible, and to succeed or fail fast. The right technology enables engineers to fail fast with minimum cost.
In conclusion, I’m positive I could have made better decisions and contributed more effectively to DT initiatives when I worked at the chemical manufacturing company if I knew then what I know now. Living in both worlds has given me a great perspective on how technology needs to be looked at in the OT domain, and how IT and OT can partner to achieve success.
To learn more about Seeq's advanced analytics, schedule a demo today!