Line Managers

Aren't Longing For IoT

Here's what drives them or just not

The main job of the line manager is to keep the production line in prime shape and maximize its availability. This includes day-to-day operations as well as long-term investment plans for upkeep and capacity increase. In this role, there’s a number of challenges and questions faced by line managers on a regular basis:

• What machine is the bottleneck in my line?
• How long will I still be able to use that component without reliability issues?
• By how much can we still increase the throughput while avoiding hitting the limits of our installations?
• What is happening when I'm not around?
• What is causing deficiencies and other quality issues in my line, and how can I avoid these?

What you do see on events, in magazines and online advertisements is that IoT will solve all these problems, putting a smile on your face (again). For almost each specific problem you can come up with, a multitude of dedicated IoT solutions seems to exist: health of individual bearings, electricity consumption on pumps, valve positions, motor condition, groundwater level, indoor climate and temperature, counting of operational hours…

What we know as well is that options cause headaches, and difficulties in selection. 

The end result is ‘no decision’, ‘a very slow decision’ or ‘the wrong decision’ leading to frustration and often not even providing a relevant solution to the actual problem. The journey often starts with doing research on all the IoT solutions available, their strengths and weaknesses, the communication options that are used (is it wireless?), their compatibility and long-term relevance...

When nearing the point of ranking the candidates or options, a next challenge arises. The IoT solution chosen actually only solves one of my 99 problems (Thanks Jay-Z), will I have to go through this entire process all over again, multiple times? Should we not first select and set up a platform where we can collect all possible IoT data? Should we not first make sure this platform is up and running and, after that, see what we can do with the data and how we will do that?

In reality this often turns out to be a lengthy exercise, with many pivots and new questions popping up along the road. It follows the classical approach of setting up ‘requirements’ and subsequently scouting the market for platforms that meet those requirements. 

However: in reality the ‘requirements’ set forward are just plain wrong! So: one ends up losing a lot of time (often up to 2 years), ending up in selecting a platform that turns out to be capable of solving only 10 out of the 99 problems. As an extra: all else is frozen from that moment on, with no option to change or shift, as ‘the investment has been made’.

For many people in the production industry this however is present-day reality: these very processes are taking place in many plants and companies. 

Reality is: IoT or platforms alone aren’t going to solve the challenges listed above. What’s more: IoT might even not be the appropriate tool at all to adequately solve them, as the richness of using data can only be obtained by having a ‘system’ view on entire installations and an awareness of the fact that the problems of today won’t necessarily be the challenges of tomorrow.

Not all is lost however, as today a new breed of solutions is arising.

 You can even rest assured, because these solutions are not made up out of services offered by consultants. What is available today are vertically integrated solutions with an open structure and a modular architecture, offered as a full product:

‘Vertically integrated’ means that the solution is provided as a product that is pre-made for your specific asset, line or problem and containing all the individual components required to get it running, smoothly: from sensors and/or connection to existing data sources, including integrated, automatic analysis packages up to the interfacing through tailored warnings, dashboards or integrations with existing site management platforms such as SAP or Maximo.

The ‘open’ structure is reflected in the fact that the data remains the property of the client, isn’t shared with anyone else, nor is it stored in proprietary file types. Next to that the products are designed such that they are intrinsically made for interfacing with existing software packages or databases: (re)using as much as possible the relevant existing applications and don’t disturb ongoing operations and processes.

The ‘modular’ aspect refers to the structure of the products being, well, modular… This modularity is found in a horizontal as well as in a vertical dimension. Horizontal modularity means that the data the product uses as a ‘fuel’ for producing insights can come from multiple sources: additional sensors, existing databases or lakes such as historians, or from a mix of both. Communication can be taken up by a 4G network, LoRa, or your own internal Ethernet using a separate section of network. Vertical modularity means that the product can take care of multiple aspects at once and has a ‘system’ awareness. It actively uses the knowledge that a motor is connected to a gearbox and this gearbox to a wheel; that the impact of the oven heat is related to the conveyor speed, also affecting the bearings … But only those modules you actually use have to be paid for, others can be activated on demand in a later stage. One can choose what asset type to be followed, or what specific problem to focus on: unexpected stops, energy efficiency, product quality. All can be selected and activated, when relevant.

This new breed of products has a sole focus on delivering results to its users. Each product is built on proven experience in monitoring the specific asset type, it takes away the burden of finding the most appropriate platform to deploy a solution on, it avoids losing time on selecting, trying and testing models and it helps to avoid the frustration building up in the company when the solution turns out not to deliver what was promised. Each of them provides actionable insights, without hassle, and does it fast. 

What’s there not to like?

^{Image: Bradley Pisney @unsplash.com}