If you work in a (production) process environment you probably look to improve that process. Any improvement should a. work and b. be profitable. In this article I will describe how we execute digital manufacturing projects for fact based, business case driven improvements.
I love watching the tv show ‘How it’s made’. Luckily, I work in Digital Manufacturing. My job gets me in many places where things are made. In all these places, people are working hard every day to improve: Improve their work, improve their products and improve the way they make things: How it’s made better!
(Production) process improvement has been a subject of study for a long time. Lean manufacturing has become a popular way to continuously improve (production) processes using insights(data) from the people closest to the production line. Now Digital Manufacturing is taking process optimization to the next level by also using data to pinpoint bottlenecks and prioritize improvement priorities. So what is the difference? It is that continuous data ensures continuous feedback and enables facts-based decision making.
Big question then becomes: How do we know where we need to improve and how do we get the right data?
In this article, I want to demonstrate how we execute a Digital Manufacturing project by describing an example case of a batch production process.
Benefits of the case (or what are we trying to optimize)
- Increase Overall Equipment Effectiveness (OEE), as dictated by Quality, Performance & Availability
- Increase Material Efficiency by reducing scrapping and improving process yields
- Reduce the Fixed Manufacturing Costs, such as energy, utility and maintenance costs
We want to leverage sensor technology and available production process data to achieve above benefits. The result will be a solution from production process to business value.
Approach of the case
- Map the process and available data sources
- Identify pain points and brainstorm a solution longlist
- Determine a solution shortlist based on the expected business value
- Detail the shortlisted solutions
- Determine the business case for each solution
Map process and data sources
In order to understand the process, we first map it. Part of this mapping is gaining a first understanding of the available data sources.
Pain point identification and solution longlist
The business will have the domain knowledge. We always do workshops with the business to explore what issues can be identified as the painpoint by the people from the field. For each painpoint we will brainstorm solutions. From available data sources and expert knowledge we can derive a solution long list. This solution long list will allow us to score on business value. In a joint session the most promising solutions will be selected.
Detailing the solutions
Next up we detail the chosen solutions. Visuals are always a great way to demonstrate your ideas. We often use visuals to show the detailed solutions as shown in below picture but also to explain the corresponding architecture of the -to be- state.
The business case
To finalize, for each solution we calculate a business case. This means that we calculate cost and benefits. We try to give full insight in the breakdown of the different contributors of both cost and benefit. Based on the business case we can decide which solutions are the most interesting to pilot.
And this is how we run our digital manufacturing projects for fact based, business case driven process optimization.
I hope that I have been able to give you an idea of how you can start improving your processes. If you would like more discuss this topic further, please feel free to reach out to me for a cup of coffee.
About Jorus Everaerd
Jorus worked in international banking before setting up a systems engineering company in (high end) smart homes. During his 10 year entrepreneurship Jorus discovered a passion for new and accelerating technologies and the possibilities that they offer to businesses. Following a study at Singularity University in Sillicon Valley he started giving talks and consulting on new technologies, smart buildings and digital transformation. His most extensive consultancy assignment was in Internet of Things (IoT). Jorus is driven by discovery, learning, ideation and uncommon challenges. In his current assignment at Philips he is developing a framework and model to measure impact on (customer- and user experience). This work involves quantification, IoT and machine learning. Jorus loves to talk and write about his work, technology and ideas so feel free to contact him.
More on Jorus Everaerd.