Many Industrial Internet of Things (IIoT) projects fail, so why is implementing the technology so challenging? Matt Isherwood, Managing Director at Pathfindr looks at the problems.
The International Data Corporation (IDC) has predicted that discrete manufacturing and transportation will become the two largest sectors for Industrial Internet of Things (IIoT) spending by 2022.It is clear the IIoT market is maturing significantly, moving from concept to commercial deployments and helping to solve complex and long-standing challenges within manufacturing businesses worldwide.
A key contributor to this IIoT adoption trend within manufacturing is asset intelligence – placing sensors on every tool, part and asset within a supply chain, creating huge operational visibility including the ability to locate assets. This provides vital intelligence that can either speed up production - by locating or tracking assets or, in some cases, enable organisations to completely rethink how they make things.
Why do many projects fail?
That’s the vision but unfortunately, it is not always the reality. Many IIoT projects fail - usually because of integration issues or because senior stakeholders have not been convinced of their wider value beyond an initial trial or siloed implementation. Sometimes managers view IIoT as a one-size-fits-all solution and do not appreciate the effort required to get IIoT working in a way that benefits the bottom line. The technology must be viewed as a ‘ground up’ bespoke capability and one that will need substantial upfront planning and preparation to implement effectively.
Another major stumbling block is around software integration. Many manufacturers have multiple software platforms, often with overlapping functionality. It can be hard to know how to effectively integrate them and the thought of discontinuing particular platforms understandably meets with resistance. This can lead to fatal flaws either because performance and data accuracy are affected, or because data analytics can’t be performed centrally though one user-friendly platform.
A further pitfall can be the failure to demonstrate value quickly. In the vast majority of cases, new technology adoption begins with small trials, or as more established solutions with defined business units. For a technology solution to gain broader internal buy-in, it must demonstrate value, and quickly. If a solution doesn’t produce actionable data, or if tools aren’t used to their full capacity, a robust ROI case cannot be made.
Finally, we sometimes come across manufacturers “doing IIoT” for the sake of it, without really considering the rationale and desired outcome. Putting sensors on every asset is a huge investment and there needs to be a solid justification for tracking something.
The way forward
- Having said this, when implemented thoughtfully and successfully, IIoT can provide huge operational and financial benefits to manufacturers. To help with this, we have devised the Asset Intelligence Ladder, a simple organising principle that supports a focus on the opportunity (to reduce wasted time and improve process) rather than technology. It consists of six stages:
- Agree a project team
- Agree ontology (the key concepts and terms under discussion, including asset, attributes, intelligence and location.)
- Classify and value assets including raw materials, components, accessories, equipment and more.
- Explore operational scenarios and their business implications in more detail - no situation is too trivial to be considered. It is important that as many process frustrations and bottlenecks as possible are identified and shared.
- Prioritise use cases and consider what a solution would need to achieve
- Create a business case by comparing the cost of waiting for assets, or the cost of process inefficiencies, versus the total-cost-of-ownership of the solution (including installation, maintenance, training etc.) Assuming that you move ahead to a pilot project, it must be designed in such a way as to deliver a clear ROI to inform decisions on the scalability of the approach.
By highlighting where major inefficiencies lie a business can discern how best to utilise asset and process intelligence and have a focus for the IIoT project. The intelligence gathered across each of these stages will be key in helping manufacturers of all sizes and levels of digital sophistication to improve manufacturing processes and deliver efficiency gains through reducing asset wait time.
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