Preparing the Supply Chain Workforce for an AI Revolution

A version of this article originally ran in the August 2024 edition of the Manufacturing Leadership Journal.

Artificial intelligence (AI) has the potential to transform every aspect of a manufacturers’ business — but some of its greatest impact will be on the supply chain.

Supply chain professionals will be able to enhance their work with the insights that AI provides, allowing them to bring together data from around the business in real time to make data-driven decisions and uncover opportunities to mitigate risk and improve resilience. AI can also be used by supply chain professionals to increase the efficiency of administrative tasks, predict demand patterns, improve inventory planning, and much more. Many supply chain professionals will soon interact with AI for the first time in their careers as manufacturers seek to increase their AI and machine learning (ML) investments. In fact, BDO’s 2024 Manufacturing CFO Outlook Survey found that 47% of CFOs are increasing investment in AI and ML this year. 

While AI is promising, manufacturers need to build the foundation for adoption, otherwise their investments will not realize expected ROI. So, how can manufacturers prepare their supply chain management teams for AI adoption? In this article, we explore steps manufacturing leaders should take to enable successful AI adoption in the supply chain function, including:

• Building a strong data foundation
• Enabling cross-functional collaboration 
• Fostering AI-related skills in the supply chain workforce
• Creating a culture of curiosity around AI usage

Building a Strong Foundation

The first step in any organization’s AI journey is to build a strong data foundation, which involves consolidating disparate data sources, ensuring all data is stored in an accessible location with appropriate reference fields enabling analysis across datasets, and implementing strong data governance standards and processes. Digitally mature manufacturers may already have this data infrastructure in place — however, for many manufacturers, their existing data management practices are insufficient to support many of the most promising AI use cases.

To build this foundation, manufacturers need AI-savvy data scientists to help them interrogate and analyze data to extract useful insights. Manufacturers can either hire data scientists directly or work closely with a third-party provider with experience helping companies set up their data infrastructure.  

Once an organization has data scientists onboard, they should collaborate with operations, supply chain, quality, and other leaders to identify the business problems they are trying to solve and the relevant internal and external data that will power their AI tools. Once these are identified, they can begin designing the company’s AI-enabled strategy. 

Enhancing Cross-Functional Collaboration

To deliver the most value, AI requires access to data from across the organization. Enabling this kind of data sharing requires the integration of many disparate systems — including warehouse management systems (WMS), customer relationship management systems (CRM), supplier relationship management systems (SRM), and enterprise resource planning systems (ERP).

The supply function is an ideal area to roll out new data sharing processes, as supply teams already naturally interact with groups across organizations. Supply leaders can be critical partners to data scientists and other individuals leading AI adoption and standing up new tools and processes. Strong data governance is also critical to ensure that AI tools provide accurate outputs based on high-quality, reliable data sets.  

Effective data sharing across systems can empower supply chain professionals with real-time, organization-wide visibility. Powered by AI, supply professionals can have quick access to aggregated insights that enhance their decision-making and free them up from having to perform manual analysis. 

For example, a procurement professional at an appliance manufacturer may receive an automated alert from an AI-powered tool that there has been an influx of negative customer feedback pouring into their CRM system due to unreliable electric motors in some washing machines. Since the CRM system shares data with the company’s SRM system, the procurement professional can identify the relevant supplier and reach out to discuss how to alleviate the issues. The company could also use this information to inform benchmarks for supplier performance. 

Fostering New Skills

While many supply chain organizations will need to hire professionals with knowledge of strong data governance principles and an understanding of how large language models and other AI solutions work, the larger challenge will be upskilling their existing teams to succeed in a data-driven environment and optimally leverage the company’s new AI-enabled capabilities. 

In a world where machines can automate tasks, perform rapid calculations, and analyze vast data sets to uncover deep connections and patterns, leading supply chain organizations are prioritizing analytical thinking and digital dexterity — or the ability of employees to adopt and adapt to using emerging technologies to deliver improved business results — as part of their core curriculum to upskill supply chain teams.  Additionally, training supply chain professionals to use generative AI tools is essential. For example, if a manufacturer is deploying generative AI tools, training on prompt engineering — or how to design effective queries to extract the necessary data from AI tools in a useful format — will be vital.  

Application-based instruction that leverages a controlled AI environment that is disconnected from the company’s production systems will be critical to building these skills. The test environment can also teach employees about their company’s acceptable use policies for AI and provide a safe place for professionals to learn from mistakes.

Many companies are also investing in third-party developed prompt libraries, or guides for sample queries to run in specific scenarios to support increased user adoption. For example, an advanced inventory planning tool might recommend questions like “identify which suppliers have had lead times that were more than five days past system projections over the past three months.”

Creating a Curious Culture

Successful AI implementation requires empowering individuals to use tools in their everyday work. To encourage adoption, manufacturers need to foster a culture of curiosity — training and inspiring their teams to explore the possibilities that AI tools can provide. This culture can also help manufacturers overcome common roadblocks to adoption.

For example, some supply chain professionals may worry that AI will replace them or will complicate their jobs. Explaining how AI can augment (versus replace) human expertise and judgement is essential to overcoming these hurdles. Beyond mitigating replacement concerns, manufacturers can demonstrate the value that achieving mastery over AI tools and skills can have for their employees’ professional development. 

Starting with a small pilot project focused on achieving tangible, near-term ROI can help get team members on board and establish internal AI champions. For example, a manufacturer that has access to an internal generative AI tool could work closely with procurement teams to show how it could support researching new vendors or generating information that may be helpful in a negotiation. 

The Future Won’t Wait

AI is no longer on the horizon — it’s here, and leading manufacturers are moving quickly to explore how AI-driven solutions can enhance productivity, quality, and safety while making companies more resilient and cost-effective.   

Manufacturers who want to remain competitive can’t adopt a wait-and-see approach. Instead, they need to start preparing their organizations for AI by establishing their data infrastructure, equipping their teams with the necessary skills, while also exploring where AI adoption will provide the most tangible and immediate benefit. Those initial wins can then be scaled into broader solutions that create a long-term competitive advantage.