The main job of the Plant Manager is to keep the site running smoothly,
Whilst producing as many goods as efficiently as possible and using the resources provided. In this role, there’s a number of challenges Plant Managers face on a regular basis:
- Machines stopping unexpectedly, messing up production planning and creating stress for employees working on the line,
- Goods being produced, but with defects: impacting yield,
- Repairs of critical production apparatus taking hours or days because the background isn’t known, or spare parts are hard to get to, and,
- High energy consumption, leading to high operating costs and a low sustainability score.
What you do see on blogs, in magazines and online advertisements is that AI is able to solve most of these problems. When occurring, these kinds of situations however create quite some pressure, thus stress. What isn’t going to help in reducing this stress is following a 1-year course on AI. There, one might learn some of the concepts behind this flavour of data crunching and see a number of examples. Learning how to make this technique applicable to one of the specific situations described above and which of all the branches within AI best to apply, will take another year. Getting all of this finally applied on-site and gaining valuable results out of it:
at least one more year.
Without even thinking about deployment on scale and internal resources (time as well as people) required for this, there are many years ahead during which you’ll still be facing the stress and, in the meantime, getting your head around a sometimes very complex mathematical concept.
For most people that doesn’t sound like fun, not at all like something you’d immediately want to start with. Reality is even worse: AI alone isn’t going to solve any of these problems. What’s more: AI might even not be the appropriate tool to solve them.
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 challenge 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 continuously producing insights can come from multiple sources: additional sensors, existing data sources such as historians, or from a mix of both. Communication can be taken up by a 4G network, LoRa, or your own internal Ethernet on a separate section of the network. Vertical modularity means that the product can take care of multiple aspects, while not all of them have to be used (thus paid for). 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, whenever relevant.
This new breed of products has a sole focus on delivering results to its users. It is built on proven experience, 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. They provide actionable insights, without hassle, and do it fast.
What’s there not to like?