Analytics Factory of the Future
“Data is the future of digital transformation and we have a wealth of data and insights within our factories, but very little of it is elevated above factory. We do very little on a regional basis or indeed get factories talking across factories.”
“I’m Head of Data Strategy at my company, we sit as part of the central IT team with a broad scope to support HR analytics across all functions, including manufacturing and supply chain. We have got legacy and new facility challenges such as making use of the plethora of data that’s already there. And then also, how ambitious should we be in setting up new things? There’s lots of options out there – should we go after all of them or are some of them a bit of a fad?”
“Before you even consider the importance of the data and leveraging it, you need to consider what data to store, why you need it and what you’re going to do with it, and therefore, figuring out the best way of storing it. It almost seems to be the most important element before you can work out how to turn it into useful information.”
“How can we get the people to trust the data, especially when deploying it on traditional areas of work. The people on the shop floor have tried and trusted experience that says they know how to solve problems. And a lot of issues arise around getting them to agree that it’s the right way. That trust and understanding is key and goes beyond being just about analytics.”
“In my role, I have a strategic initiative underway, which is data analytics. However, I’m trying to get the business, particularly supply chain, to create a roadmap for what the factory of the future looks like. The danger is that they all come up with great proof-of-concepts, but the problem is figuring out how it all fits together from an end-to-end perspective.”
“One of my responsibilities is to the lead the data strategy across the business, working with all departments. We could run the factory on an Excel spreadsheet (and many do actually try) but of course, there’s a lot of challenges with that, because everybody that has their own copy of the data, has their own interpretation of the data and will display it how they choose to tell their story. It’s not their fault but that’s when the trust issue starts to creep in. We’re relatively early on that journey – moving on from very manual gathering of data.”
Data is of course different between different sectors, and if you look at the connectivity across a cell, line, or a whole facility, it does vary and, in some places, it can be quite immature. However, if you’re speaking about value, the other secondary and tertiary datasets that you start to involve, you start seeing other patterns. For example, you might begin to see that you had dips in quality on certain lines when introducing freshly qualified technicians that are just coming off their apprenticeships, or you can start to see patterns with environmental data.
Discrete automated manufacturing organisations who have increased or solved quality issues have started to augment some of that secondary and tertiary information. While there is value in looking at the cell and line level and having it across a specific facility, there is also value when you start to bring in that higher level and bring in some of those other data points. You can start seeing patterns as well. It is not to say there aren’t differences because there are, but once you put a level of abstraction on it, you can start to see these patterns. So I think there is value there.
Manufacturer insight: “A key point to focus on is what the data is telling you, rather than what the data itself may be.” “Currently, our company is looking into how we could integrate our supply chain, which are currently very complex with lots of companies that feed through to us, so it’ll be a huge job.
