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Why Accelerating Digital Transformation and AI Can Future-Proof Your Supply Chain Infrastructure

Continuous learning through AI is critical to successfully digitizing and, ultimately, improving Supply Chain Infrastructure.

Supply chain shortages have arrived this summer as the nation reopens causing varying delays for everyday goods like appliances, chemicals, auto parts, and other imported products with hundreds of companies and their customers stuck in limbo. The impact on the supply chain industry encompasses everything from untold logistical and financial ramifications to the erosion of consumer confidence.

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Even before this latest wave of shortages, some companies have already had their supply issues, with inventories that were not well controlled or in check, and the problem is compounding. Having an improved, smarter digital infrastructure in place to analyze supply chain signals and change data efficiently – or to ensure that data is harmonious in the first place can help alleviate inventory bottlenecks that will come with the reopening and beyond. The status of a ‘new normal’ post-pandemic serves to underscore the urgency for manufacturers to accelerate their digital transformation.

Continuous learning through AI is critical to successfully digitizing your infrastructure and, ultimately, as many companies have found, improving the efficiency of the supply chain and material management via the ability to accurately predict outcomes, promote greater sustainability and preserve customer loyalty.

How can you leverage AI to future-proof your organization? The following best practices can set up your organization for successful digital transformation, step-by-step.

Start Small, But Think Big

Start small and win big by first defining a less risky part of your business with a quick outcome to see how AI can work for you. Pick a problem that is well defined, such as an area with indirect materials management where there is a lot of repetition involved. As AI and ML are trained for specific tasks, the data will eventually be able to provide outcomes that result in better inventory management. At the same time, you want to be sure you have a 360-degree view of the overarching problems you are trying to solve. Even though you will want to take small, chewable bites at the start of your digital transformation process, you’ll want to think big so you can build in the logical steps later.

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It’s about taking the plethora of data that is out there – that would be impossible for a human to absorb and analyze – and putting it to work. To do this, you need to start by having the data you can trust, that is understood, and can be analyzed quickly.

Build a Foundation to Achieve Outcomes

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To drive the desired outcomes in specific areas—rather than enriching everything—you need to build a foundation for AI to work with. Build the models and then continue to add new signals as the AI is trained for the specific task.

The first step is to bring in the experts. Not just the technology experts but subject matter experts, people who are familiar with a pertinent and specific task and what needs to happen to streamline that task. You want someone familiar with the supply chain and demand planning who knows what data is vital, and what information would remove mundane tasks. Supervised verification teaches the technology what to apply the next time a similar situation comes up and what analysis is necessary for the required outcome.

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Focused Data, Not Perfect Data Is the Goal

Because AI and ML can handle tasks quicker and more efficiently than doing things manually, the tendency might be to aim for perfect data. But perfect data, if it exists, doesn’t offer focus for the task at hand. And it is only as valuable as the outcomes it can drive. Why not instead create a wish list of what you want to accomplish with the data like inventory optimization, and put together a data set around that goal? If you are starting small, look at a specific business unit or geography (i.e., North America) That can work as a training set for the rest of your global network. Then, be sure to harmonize the data to make sure it is as uniform and cohesive as possible and that you are not working with disparate data sets.

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Most importantly, be wary of relying solely on data analyses based on history. If there is a change—say a ship stuck in a canal—the historical data alone is worthless. Instead, focus on agility. You need to be adding decision-based knowledge and external signals able to keep up with current demand, and your data needs to be able to react in real-time. By being able to quickly identify and investigate the outliers, what you haven’t seen in the past, and up-to-the-minute trends that you don’t want to see come to fruition and how they affect different data sets, you will be able to prepare for the unexpected.

Get Executive Buy-in Across Relevant Business Units

While implementing AI and best practices for digital transformation may start at the CIO level, it should involve all relevant executive stakeholders – across divisions, business units, and functions. Case in point, for example, a manufacturing company that ships products across the globe will want to get buy-in from the chief procurement officer and the chief supply chain officer.

They can not only help make for a smoother AI implementation process, but they can also help to champion your initiatives across the organization for less push-back and an easier move to digitization.

AI is not a futuristic technology that is going to swoop in and instantly do all the jobs that make the supply chain optimized or solve other business challenges. Leveraging AI to understand what data you have to work with and how to either use that data more efficiently or shift directions on the type of data collected. The reason to turn to AI in your digital transformation process is to bring speed to value. What once took years for data cleansing now takes weeks. But AI can’t help until you get executive buy-in.

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