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In the last few years, global supply chain organizations have faced a relentless cycle of disruption. From the unprecedented challenges posed by COVID-19 to climate disasters and the recent disruption in the Middle East, we find ourselves navigating a new reality of change and uncertainty. However, these disruptions also serve as catalysts for innovation and growth.
Artificial intelligence (AI) is not only helping organizations weather the storm but also fundamentally transforming how they operate. As the global supply chain has become increasingly complex, the need for swift, informed and efficient decision-making has only become greater.
AI, with its ability to analyze vast amounts of data, generate predictive insights and automate complex processes, is stepping up to meet this demand.
Jabil’s 2024 Supply Chain Resilience Survey showed that nearly 200 supply chain and procurement decision-makers from some of the world’s leading product brands are planning for and already using AI in their day-to-day processes. From the insights participants shared, we can all learn a few best practices for AI implementation in our own supply chain organizations.
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Be strategic in your use cases for initial AI deployments
According to our survey, two-thirds (66%) of companies are now harnessing predictive analytics and AI/machine learning (ML) models in their supply chain activities. Organizations are focusing on specific use cases when implementing AI instead of attempting to apply solutions across the entire supply chain all at once.
Demand planning and inventory management are the most common ways companies are using automation, with 80% of respondents leveraging AI/ML to improve forecasting. Nearly two-thirds (64%) of survey respondents believe AI will increase the accuracy of their demand-planning processes. Organizations across the industry continue to face an imbalance between supply and demand. Some components are in excess, while others are in short supply. By analyzing large volumes of data, AI can help identify impacted parts, predict future trends and optimize resource allocation and distribution. More accurate demand forecasts can help teams effectively optimize their inventory levels—to not only meet customer requirements but also improve free cash flow.
By identifying areas where AI tools can provide an immediate impact, such as forecasting, supply chain organizations can align their AI implementations with big goals like cost reduction (a top organizational goal for 69% of respondents), customer satisfaction (61%), and enhanced visibility across the value chain (46%). Organizations expect that AI will have the greatest impact on the third goal; 76% of respondents said they anticipate AI providing increased visibility into the entire supply chain. However, gaining that visibility requires more than just onboarding the right AI tool.
Think “data first”
AI will have a massive impact on the ability of supply chain professionals to sift through the vast amounts of data collected by their organizations and use it to make agile, effective decisions. However, reaping the benefits of AI requires a data-centric approach, with investment in a robust data management framework and comprehensive governance before an AI tool is deployed.
In order for AI to generate the meaningful insights organizations seek, data assets must be not just governed and secure but also trusted and positioned within a unified data model. Without these foundational elements in place, a tool may not reach its full potential.
Collaboration is also vital for successful AI implementation. Many of the ways companies are using AI in their supply chain activities—including sourcing (59%), contract creation (29%) and quoting (21%)—require the use of data from suppliers, customers and other external data sources. As part of your supplier and customer relationship-management strategy, building data-sharing agreements and AI guardrails will be crucial steps in automating supply chain processes.
Bring your workforce along in the AI-deployment journey
The importance of collaboration in AI deployment extends to the workforce.
Seven in 10 respondents in our supply chain survey said they expect AI will reduce labor costs through increased automation and efficiency. But that may raise concerns among employees fearful of displacement by automation.
Preparing the workforce for AI integration by alleviating their fears and misconceptions is a vital step of deployment. People in leadership roles must emphasize that automation is designed to complement human skillsets, not replace them. Providing hands-on experience with AI pilot programs, conducting skills assessments and creating tailored learning pathways can help equip employees for a more automated future.
The integration of AI into supply chain operations offers significant opportunities for companies to optimize processes and enhance their resiliency in the face of disruption. By aligning AI implementation with strategic goals, investing in data and analytics — starting with specific use cases — collaborating with partners, and educating the workforce on the realities of automation, supply chain organizations can navigate the complex landscape of AI integration effectively and efficiently.
