Overcoming Artificial Intelligence Obstacles in the Supply Chain

https://morailogistics.com/overcoming-artificial-intelligence-obstacles-supply-chain/

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.

Supply Chain: The Digital Transformation Imperative

https://morailogistics.com/supply-chain-digital-transformation-imperative/

If supply chain companies are going to evolve to meet the demands of the market, digital transformation has to be central to that evolution. 

Now, more than ever, supply chains are being pushed to grow as a result of the needs of customers. And, no matter how well run a supply chain is, by itself it simply can’t meet those needs. Not without the aid of technology. In turn, there’s no greater way to technologically integrate and streamline an operation than digital transformation.

The numbers bear this out. A McKinsey study showed,

That, on average, companies that aggressively digitize their supply chains can expect to boost annual growth of earnings before interest and taxes by 3.2 percent—the largest increase from digitizing any business area—and annual revenue growth by 2.3 percent.

If they digitally integrate properly, supply chains should see improvements in the following areas, just to name a few:

This week’s article by Morai Logistics underscores the importance of digital transformation for supply chains. Pointing to some of the most relevant areas of improvement digital tools will bring and how.

Speed

There are a multitude of reasons why digitization should improve the speed of a supply chain. Automation by itself should greatly enhance supply chain speed by conducting repetitive tasks like data collection without human error. Additionally, machine learning can greatly help with predictions that are central to supply chains running smoothly. 

These predictions can involve data within a company, such as the health of machinery so that it can be fixed or replaced before it disrupts operations. The predictions can also involve external data such as market demands, so inventory can be stocked accordingly or weather patterns, so the supply chain can adapt to them.    

Efficiency

Efficiency often goes and hand-in-hand with speed, with the added bonus of leading to more profitability due to less waste. Thus, for many of the same reasons speed is improved, efficiency is too—automation and machine learning. However, in addition to those reasons, digital integration drives efficiency also because it can bring with it artificial intelligence (AI) and robotics. 

AI, much in the vein of automation, can handle tasks that would otherwise be mundane, freeing up the workforce for more important matters. Robotics is useful in several domains, particularly warehouse management, as they can deal with the handling of the inventory. 

Decision-making

In order for a supply chain to perform optimally, the decisions that underpin it have to be precise yet flexible, accounting for customer demands and adaptable to any circumstance. The collection of data, the generation of analytics, and the subsequent insights they give can be integral to understanding a supply chain. 

Moreover, the earlier mentioned machine learning can go a long way in making decisions more informed. As they give suggestions to help with inventory management, scheduling, market fluctuations, and so on.

Communication

As a result of the incredible size of modern day supply chains—often stretching from one side of the globe to the other—it’s critical that communication along them is excellent. Any gap can lead to a breakdown in the entire chain. One digital option to overcome this issue is blockchain technology.

Blockchain provides a database with an immutable and transparent digital record of the movement of products along supply chains. Where, in turn, each new piece of data has to be validated by every player in the supply chain. Consequently, there is a continual mutually agreed upon data trail of what is happening each step of the way.