The Right Data from the Right Source Creates the Right Models

Connected factories are now becoming commonplace, as manufacturers recognise and embrace the power that their operational data can unlock. Indeed, the COVID-19 pandemic forced many companies to pivot and adapt to the ‘new normal’, with the primary protagonist in these efforts being the foundation of data capabilities – digitalisation.

By lifting the lid on their technology, people and processes – and extracting this operational data – many have discovered myriad dormant capabilities and connections that give them even greater insights into the operation, capacity and efficiency of their production lines.

But with all this new data on tap, the big question is: Is this data used to its maximum advantage? Are companies wringing out every last byte of useable information, and are they then deploying it where and when it can make a difference – making the right decisions at the right time?

In most cases, decisions should be made as close to the source as possible – the edge – to maintain data context and speed. Contextualisation and modelling at the edge enables richer analytic insights, and edge-to-cloud-based solutions can leverage the best of both worlds to deliver increased agility and productivity.

When you gather and analyse data closer to the source, you get real-time feedback. Without this immediacy, you might build a model or simulation with limited capabilities and scope – because you don’t know what you don’t know! You need information that allows you to understand the real capability, not just the typical observed capability. But an edge solution is just part of the picture.

Not enough slices

Take for example an artisan pizza truck and what operational data will tell us about it. It operates for X hours, Y days a week, serving Z pizzas, with a time T between order and delivery. What we can’t ‘observe’ are the truck’s capabilities: How many hours a day could it operate? How many hours do staff need to work? Are they trained, and to what level? What is the maximum number of servings? What is its range (and speed) between pitches? Is it properly maintained? What equipment (capabilities) is in the kitchen? Can other food be prepared?

The pizza truck example illustrates how many companies face a mixture of data and models, where some of the details are not readily available. This is particularly true when trying to examine data remotely. Understanding the limits of observable data and considering where additional data could be collected and included, will ultimately give a more robust picture of any situation.

Compounding this are the multiple challenges around data availability, include sampling rate, the way data is expressed, and limitations caused by network communication resources, resulting in huge gaps in data.

Learn more about how you can use your data to its full potential on the Management Perspectives hub. There you’ll find a wealth of resources for executive industrial decision-makers, providing the information you need to thrive in the evolving digital landscape.