First 90 days reflection: Challenges and opportunities in the manufacturing industry

As I reflect on my first 90 (+) days as Chief Commercial Officer at Intelecy, I can't help but feel overly excited about the opportunities that lie ahead. During this time, I've had the chance to visit several of our customers in the process industry, and witness firsthand the positive mindset and the results they have achieved by leveraging technology in a smarter way. 

I'm looking forward to exploring how we can enable organizations even further to optimize processes and drive efficiency. It's an exciting time to be part of this industry, and I'm thrilled to be part of the journey!

While attending trade shows, and talking to many industrial organizations, I’ve made some reflections about the challenges and opportunities that lie ahead.

  • Going beyond data visualization
  • Sustainability drive
  • Enabling the workforce
  • Let me dive a bit deeper into these!

Going beyond data visualization
The process industry is facing several challenges that are impacting its growth and profitability. These challenges include rising energy costs, increasing competition, and strict regulatory compliance requirements. Many organizations have started their digitization journey with the aim to solve these challenges, with increased instrumentation, time series data collection, and analysis. Quite a few of the companies I’ve talked to have expressed a concern that they know they have the data, just not how to leverage it in a good way. They have enhanced operators' visualizations, but have not been able to keep up with the latest technology and advances in decision-support solutions.

Sustainability drive
It excites me that many industrial companies are not focusing on sustainability just for “green washing” reasons; rather, there is a genuine desire to drive change. According to World Economic Forum the global production sectors are responsible for 20% of carbon emissions, and consuming 54% of the world’s energy sources. These figures highlight the critical need for organizations to find new ways to optimize their processes and improve efficiency to address these challenges.

Enabling the workforce
A third topic I’ve heard a lot about, often referred to as the organization’s most important asset; their people. The engineers and operators on the factory floor are crucial to running the plants, however still rely heavily on experience and intuition to manually monitor alarms to troubleshoot issues. This puts a strain on their human capacity, leading to shortcuts and prioritization of urgent tasks that don't necessarily add value. The people closest to the production lines, those who know the processes, need user-friendly tools that enable them solve challenges and make data-driven decisions.

The heavy reliance on experience also makes it difficult to replace highly skilled operators at retirement. At one of my customer visits I was told a story about how an operator, who was failing to identify why a piece of equipment was malfunctioning, was asked by a senior operator if he had tried “talking” to it. By knocking on the equipment the senior engineer was able to hear where the issue was. This kind of knowledge is obviously not easily transferrable, and to be able to attract new talent from a younger more tech-savvy workforce, there is a need for a step-change when it comes to more technical advanced tool.

How to fast-track the changes needed?
There is a real need and urgency to change the pace, as continuing at the current speed will not cut it. Here I truly believe that technologies like Artificial Intelligence (AI) and industrial Machine Learning are important keys. With the attention AI has received the last months, mainly through the buzz around ChatGPT, the opportunities in leveraging AI has been an eye-opener to many. According to McKinsey, AI and industrial Machine Learning is also starting to be seen as game changer for manufacturers.

There are several areas where Machine Learning could be beneficial for the process industry. Some examples:

  • Optimize the production process, improving yield, reducing waste, and increasing efficiency
  • Analyze energy usage patterns, and identify opportunities for reducing costs and improving sustainability
  • Detect quality issues allowing for immediate corrections
  • Predict equipment failures and maintenance needs, helping to reduce downtime and prevent costly breakdowns

You can read more about more concrete use cases from the process industry here.

How to get started?
One question I’ve received several times during these dialogues is how to get started, especially if you do not have the internal knowledge or possibility to hire external data scientists to develop the machine learning models.

In my next post I will dig deeper into how the manufacturing industry can draw on the benefits of AI and industrial Machine Learning, delivering predictable insights while at the same time attract talent and enable the existing workforce. All without having to write a single line of code. 

I might be a tiny bit biased here, but I truly agree with Bill Gates: The age of AI has begun!

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