Supply chain management with AI

By Jonathan Wilkins, Marketing Director, EU Automation
On average, businesses estimate they spend 55 hours per week doing manual processes and checks. Could this be avoided...

On average, businesses estimate they spend 55 hours per week doing manual processes and checks. Could this be avoided?

Here, Jonathan Wilkins, Marketing Director industrial parts supplier EU Automation, explains the potential of artificial intelligence (AI) in supply chain management. 

These trivial, but necessary, tasks equate to 6,500 work-hours in the working year, some of which could be saved by implementing AI and automation. However, these financial issues represent just one part of the complex supply chain. Could wider challenges, such as logistics and distribution also benefit from the AI treatment? 

With volumes of data growing at an unprecedented rate, computers are capable of parsing data in a contextual manner, providing useful insight to an operator — without them doing any of the legwork. Big data technologies are also capable of analysing market trends, integrating with enterprise systems and triggering automated actions based on the data it collects.

Build AI readiness

Businesses must have large data sets of deep granularity for effective AI to take place. Granularity is used to characterize the scale or level of detail in a set of data, of which AI is highly dependent on. The greater the granularity, the deeper the level of detail across the data. Whether AI implementation is in the forthcoming plans or not, it's a good idea to ensure data collection and storage are geared for high granularity. 

Boosting granularity may mean increasing the frequency of data readings, refining the precision of such recordings or even placing sensors in new places to measure new variables. For example, if a flow meter is currently measuring the flow rate of a liquid in litres per minute, changing this recording to millilitres per minute may provide more insightful data. Ultimately, even if a business is not AI-ready today, improving granularity will lay the foundation for when AI inevitably becomes a competitive differentiator. 

Target a specific problem

Have one business goal in mind at the beginning. Focusing efforts and resources on a single problem means a significant pain point can be tackled effectively, with relatively low risk compared to a complete overhaul of processes. By selecting a discrete project, initial successes can be built upon and lessons can be learned and then applied to other areas in the supply chain.

Equipment supply planning

Supply chain planning is a crucial activity, using intelligent work tools to build concrete plans for things that could go wrong. For example, using data from past equipment and current machine performance, AI can accurately predict when a part will need to be replaced in order to maintain the optimal running of a plant. This is critical, particularly for old legacy equipment that is now obsolete, as lead times to find the part from an obsolete parts supplier will vary. 

As the complex web of production and distribution are opened up to the benefits of AI, the supply chain will have a bigger economic impact than any other application of the technology. In fact, Mckinsey estimates firms will derive between USD$1.3bn and $2.1bn a year in economic value by implementing AI into the supply chains. 

But remember — for businesses just starting out with this technology, the focus should remain on building data granularity and choosing a specific issue to overcome with this technology. 

This article was contributed by Jonathan Wilkins, Marketing Director, EU Automation


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