Adoption of Supply Chain Management Software Using AI

Adoption of Supply Chain Management Software Using AI

Adoption of Supply Chain Management Software Using AI

Supply chain management governs our modern way of life so much that we take the process for granted; but like every other technology that surrounds our everyday lives, we only notice what it does for us when it goes wrong. When food starts disappearing from supermarket shelves, only then do we question the ways in which it normally arrives there.

As the world has shrunk considerably through globalization and improved logistics, so we rely on ever-longer supply chains, consisting of more and more links to deliver goods to the consumer from their source. The concept of the supply chain is a simple one, but the mechanics and processes involved can be extremely complex.

Obviously, like any other contemporary business practice, supply chains are managed by computer software. If that software fails, or more likely, human error causes it to fail, that’s when empty shelves appear, and angry shoppers start ranting on social media because they can’t get their daily fat and sugar intake! Fortunately, one way of avoiding human error in supply chain management software is found in operators being helped by a digital adoption platform or DAP.

A DAP is a secondary layer of software that runs alongside the primary platform to which the DAP is assigned. In effect, it’s a ‘hand-holding walk-through’ platform, which can prove invaluable for human operators when adopting to brand-new software packages or potentially disrupted workflows caused by system updates.

A paltry amount of poultry…

The consequences of supply-chain software not functioning correctly because people aren’t able to understand it or adapt to new User Interfaces (UI) and User Experiences (UX) caused one of the more bizarre news stories of 2018; people were turning up at Kentucky Fried Chicken food outlets across the United Kingdom and calling the police because the retailer had, quite simply, run out of chicken!

Unsurprisingly, the cops didn’t regard KFC’s poultry paucity as their problem and issued several finger-lickin’ edicts on social media channels – urging people not to call 999 when a Bargain Bucket wasn’t available at their local drive-through.

But coming back to the serious side of this issue, the financial and reputational damage to the logistics provider involved and KFC themselves was immense. KFC outlets had a severe national chicken shortage for a significant period. Storage facilities were having to discard tons of products as they had been languishing for too long on refrigerated shelves.

Trucks were apparently ready and waiting to pick up and deliver products, but the computers simply didn’t create their itineraries. How did it all go so wrong? Apparently, the problem was caused by software compatibility problems between the two organizations, after a new logistics company was awarded the KFC deliveries contract.

Had these two companies used a DAP, the situation might have been very different. The adoption of any new systems can be made much simpler by artificial intelligence (AI) in a DAP helping human operators along. The AI hyper-personalizes its teaching output and acts like a friendly knowledgeable colleague, interjecting only when asked, or proactively, when it predicts that the operator is about to make a mistake.

Also read: 6 Best Supply Chain Management Tips

Getting that first date just right

Let’s imagine that Sarah arrives at work on a Monday morning, faced with an unfamiliar computer screen’s UI and UX. She knows she must input the date of required deliveries from a given warehouse to a series of fast-food outlets. She knows that five outlets need their raw materials on, say, May 15th. Sarah inputs the ‘delivery date’ field as 15/05/2022, as she normally would. But an error shows up.

The error message on the primary software simply states: ‘Date Invalid’. ‘What hokum’ thinks Sarah. How can the 15th of Maybe ‘invalid’? She calls IT support on the phone, but the line is busy. Probably because about 50 other people are encountering the same problem.

But if Sarah was using a DAP, the instant that she typed the figures ‘15’ into the first part of the date field, the DAP could show a helpful tooltip, personally addressed to her:

“Sarah, this date field requires American format dates e.g.: mm/dd/yyyy.”

Immediately, Sarah shakes her head at the stupidity of the change but inputs the required date as 05/15/2022. The system accepts the date requested. Now let’s imagine that the following day, Sarah comes in again and logs on to her workstation. The AI in the DAP has ‘remembered’ that Sarah struggled with date formats yesterday, so it predictively offers advice the instant that Sarah hovers her mouse over that field: “Sarah, please remember that this date field requires American format dates e.g.: mm/dd/yyyy”.

Sarah is a bit hungover, and she would have made the same mistake. “Thanks, DAP” she mutters under her breath.

The beauty of the hyper-personalization element is that the DAP would then stop prompting Sarah after she had used the correct date format on the next few occasions. However, if Sarah’s colleague Winston logged onto the same workstation, the AI would already have a record of Winston’s learning profile and will only offer help on request or where it ‘suspects’ Winston will make a mistake.

In summary, the DAP system helps employees adapt to new workflows, across any industry, from supply chains to stockbroking. There’s no doubt that when humans interact correctly with updated or new software, everyone’s lives are made easier.

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