Artificial intelligence (AI) is playing a growing role in the supply chain, yet, in order for it to be utilized to its fullest, there are several factors worth considering.
AI has been talked about as a game changer in the realm of supply chain for a long time now. In recent years, its potential has finally started to be harnessed by companies. However, the implementation of AI is not as straightforward as it might seem. Due to the robust and varied nature of the technology, there are a number of elements within the supply chain that need to be looked at for it to properly work. Whether it’s machine learning, forecasting, analytics, or something else, they require certain conditions to be met in order to be employed at full capacity.
This article by Morai Logistics breaks down what companies should be evaluating as they implement artificial intelligence into their supply chains.
Culture
Ultimately, no matter how great any kind of technology is, it can’t be effective without the workforce to utilize it correctly. This is where company culture comes in. It’s understandable if employees are a bit tentative when AI is first introduced into operations. After all, even as it marks a monumental leap forward, that also means it can feel disruptive to employees. Particularly if they’re not sufficiently prepared.
With all that said, it behooves the management of any company implementing AI to get their workforce comfortable with it. This can be done a couple ways. One, is through training and education. Another, is being transparent with them in regards to any temporary inconveniences they might face initially.
Data
Quality data is central to getting the most out of artificial intelligence. In fact, data is what AI runs on. With that being the case, the more data being collected and the higher quality of that data, the better. As such, prior to implementing AI, a company should be confident in its data collection.
A Supply Chain Brain post emphasizes this point,
All computational processes need good data, and artificial intelligence is no exception. Machine learning (ML) in particular requires huge volumes of accurate data in order to train algorithms and develop predictive models. However, most companies have neither the quality nor quantity of data to accomplish this.
Data Silos
Regardless of how much data a company gathers and the quality of that data, it’s not that useful if it becomes fragmented. Data that’s incapable of being analyzed with or interacting with other data is data that’s siloed. Thus, data silos are great liabilities, as they’re a breakdown in the larger data network that needs to flow smoothly in an intelligent supply chain. It, then, is of the utmost importance that, when data is collected, it goes on a data platform that can integrate and consolidate it, regardless of what source it comes from.
Supplementing Artificial Intelligence
Despite AI being incredibly varied in its uses, it can’t stand alone. It requires help in getting the sorts of results supply chains need. As such, it’s critical that other technologies that can work in tandem with AI are also introduced into supply chains. These technologies include automation, Iot, and others.
An article from Supply Chain & Demand Executive further explains,
Even with sufficient and complete AI data, you may face some technological constraints. Many applications can be significantly sensitive to latencies; for instance, predictive maintenance applications will only work when auto alarm mechanisms and rapid response are built into the overall process of handling predictive maintenance issues … this is where ultra-fast computing, together with the proper response process, can make a difference.
