Three ways AI is reinventing Supply Chain Management

Workplace disruptions will continue to be endemic. Supply chain is an optimization game. With AI, enterprises now have better tools to become more focused on achieving the most optimal outcome. With AI reinventing Supply Chain Management, it’s time to train the workforce, writes Kevin Miller, Chief Technology Officer, IFS.

If there is one major takeaway from the last several years in business, it is that disruptions are, and will continue to be, commonplace – so companies need to be ready for it. They can only survive this level of uncertainty by using every tool at their disposal. Fortunately, smart technology has matured enough to be a viable tool in their arsenal.

With the proper AI configurations, business leaders can finally have full visibility into their organizations in real-time. As they try to use that information to streamline and optimize their operations, they will also need to address the talent gap currently affecting organizations across the economy.

There is no easy solution to it, but once the smart technology is firmly integrated in the workflow and employees are sufficiently trained to use the smart technology, businesses will be well prepared to weather the next major shock.

1. Pre-empting Weather Disruptions

Leaving behind legacy technologies in the era of digital transformation requires breaking down silos within an organization’s tech stacks and embedding the new technologies into the operation of the company. If AI is built into supply chain performance through integrating weather prediction into the process, it’s able to help pre-empt possible disruptions due to the weather.

Weather events are becoming increasingly more common and disruptive to the supply chain process. Nowadays, running on standard lead times doesn’t work anymore – it’s too tight, meaning that timelines are completely disrupted when a notable weather event happens.

With AI that can use historical weather trends along with meteorological readings, businesses can measure the probability of lead times being disrupted by a weather event. If there’s a high probability, businesses can then change their plans to prepare for the forecast, which can even happen automatically given an AI with the capability to make changes directly ordering and shipping instructions.

The moral? You can’t stop a hurricane from happening, but the better you can anticipate and plan around it, the better your performance will be when it happens.

2. Leveraging Predictive Asset Management

Predictive maintenance is becoming increasingly popular as companies adopt new AI and ERP tools. Predictive Asset Management (PAM) is a form of Asset Performance Management (APM) that uses IoT data to improve asset reliability, lower maintenance costs, and better understand asset performance. APM ensures that assets are able to perform at optimal levels, increasing their reliability and availability and driving the use of IoT-sourced data.

PAM lowers costs and reduces the necessary time associated with maintenance by streamlining the work order process. Once it ingests an alarm signal or fault code from a piece of equipment that has gone down, the AI analyzes previous work for that type of equipment and that particular signal code.

Based on the history of repair trips for the code and machine, AI then determines the correct spare parts and tools necessary to complete the repair and can note it down on the work order, thereby eliminating the need for a preliminary diagnostic trip to the equipment and the time needed to order the parts.

Coupled with IoT, where the equipment is able to feed the AI this information directly, predictive asset monitoring is a game changer for anybody who works with the equipment such as field service technicians.

3. Optimizing Data

In order to achieve all of the promises of AI and predictive maintenance, collecting the right data is critical. The primary method for companies that design, build, deploy and service assets that might make use of AI in their supply chains or asset maintenance, sources this data from sensors on equipment in the field or data coming from the production floor.

With the ability to incorporate quality filters into the process, businesses can cut down on costs and prevent someone from having to go out in person by using data directly from the source.

This data is the key insight to what’s really happening with those assets. If businesses are constantly monitoring the surrounding conditions, they may even be able to predict that maintenance should happen before or after it is regularly scheduled.

For instance, if you see that the equipment’s temperature is rising before it’s scheduled for maintenance, you can address it before the temperature gets too high and the machine goes offline, which would result in a much bigger interruption. Information directly from the asset makes this predictive aspect of the data usage, and the resulting outcome, all that much better.

Smart machines take smart people to work

At IFS, we are seeing a ramp up in investment among manufacturers and field service providers in data science, creating new job roles. A recent survey commissioned by IFS revealed that nearly a third of businesses cite technological superiority as the most significant differentiator, a figure that has tripled since 2018. It clearly shows the unrelenting desire from businesses to take advantage of all the benefits that smart technology provides.

While the appetite for deploying advanced technology has only increased, the supply of skilled workers needed to navigate such deployment has failed to keep up with demand. In fact, according to the same IFS survey, nearly 50% of businesses reported that they struggle to meet service level agreements – 37% attributed it to inadequate tech support.

Moreover, for manufacturers, the issue of skills shortage has never been clearer, with 44% citing the shortage of skilled labour and turnover a top concern for them, while a further 40% said user adoption of new technology and 29% pointed toward the increased complexity of assets as their top concerns.

Reskilling and upskilling

Having the right people is as important as having the right equipment to do the job. Reskilling and upskilling existing employees can be a good place to start – especially as attracting the right talent becomes more challenging with a growing labour shortage across the broader economy.

By retaining and retraining existing employees for new roles, companies keep the institutional knowledge key to a well-oiled machine and save the costs associated with layoffs. It also builds a perception of a healthy company, which is key to attracting further customers and investments, let alone boosting employee morale.

Starting an apprenticeship program could also help. Learning hands-on while on the job allows companies to train people specifically to their standards. Workers who graduated from apprenticeship programs are also more likely to stay, helping companies retain trained skilled talent. It is also an affordable path for people to “earn and learn”, gaining the skills required for the new, tech-enabled roles.

Labour problem

It takes more than one solution to addressing the labour problem, people and technology are two sides of the same coin. With AI and the innovations it brings, enterprises now have better tools to become more focused on achieving the most optimal outcome. Whereas smart technology has matured enough for real-world deployment, labor remains the last piece of the puzzle.

Businesses will need to start tackling the labour problem so as to have the right workforce to realize the promises of technological advancements. For any organization to be successful, it requires both advanced technology and well-trained people to make use of such technology.

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