The AI market in the supply chain sector is projected to exceed $20B by 2028, with a compound annual growth rate of 20.5%. A Gartner report specifies that 70% of supply chain leaders plan to implement AI by 2025. Furthermore, Proxima’s Supply Chain Barometer reports back from 3,000 CEOs that while they may be planning to, or already are implementing the use of AI tools, only 22% expect their strategy to be greatly affected by it in the next year.

The Barometer highlights the differences in approaches available to CEOs and their team, from self-build, to partner solutions or even hybrid models. Whatever the strategy, advances in AI will redefine supply chain operations making organizations more agile and resilient. We are possibly on the cusp of a revolution following years of continuous evolution. 

AI can manage large data sets, streamline processes, and empower companies to adapt to dynamic market demands. As AI-powered solutions become more available, the potential for innovative solutions within the supply chain and logistics sectors promise enhanced productivity and competitiveness, vital in a turbulent global marketplace. 

Supply chain challenges continue to plague organizations: Rising operational costs, labor shortages, raw material shortages, inconsistent demand, customer demands, geopolitical challenges, import/export regulatory instability, environmental/sustainability pressures, and the list goes on…

With the ever-growing challenges, AI is poised to help alleviate some of these pain points.

1. Optimization through automation

Supply chain optimization necessitates tracking physical goods at every transfer point. AI can automate documentation by entering, extracting, and classifying data from text files, ensuring the integrity of multiparty transactions. Additionally, AI-powered tools can analyze historical demand and vast amounts of supply data to determine optimal levels of inventory to avoid overproduction.  AI tools will also be able to automate warehouse operations such as picking, sorting, and packing which will reduce labor costs and increase efficiencies and productivity as robotic systems can operate 24 hours a day, seven days a week.

Powerhouses, like Amazon are leading the charge by investing heavily into transport automation technologies such as autonomous trucks.  As part of their quality control, BMW uses computer vision to scan car models as they move through the assembly line.

2. forecasting

AI can analyze both internal data (such as sales pipelines and marketing leads) and external markers (such as market trends and economic outlook). With AI, supply chain planners can estimate demand and assess scenarios such as economic downturn, impactful weather events, etc. AI tools can help alleviate or eliminate the bullwhip effect where small fluctuations can amplify upstream and downstream challenges.

Walmart uses AI forecasting tools to track product demand. Utilizing machine learning tools, Walmart analyzes historical sales data resulting in optimizing inventory. Using natural language processing, they review customer feedback, social media engagement, etc. to identify trends, thereby predicting demand trajectory. 

3. transparency

AI-powered supply chain management (SCM) tools outperform traditional methods with their ability to track in real-time. This enhanced visibility helps organizations identify operational problem areas and help with corrective measures. AI-powered risk management systems can monitor data from multiple sources including social media and news feeds to identify potential risks and supply chain disruptions, cyber-attacks, etc. which will help give visibility in real-time and mitigate risks.

4. efficiency

AI tools can optimize warehouse racking and design layouts. AI models can evaluate floor layouts, and improve inventory access, documentation, and travel time – rack to packing to shipping. AI can also improve route scheduling, enhancing fulfillment rates.   Powerhouses such as Amazon use AI to optimize inventory levels.

For example, Amazon utilizes computer vision devices installed onto their cleaning robots to scan inventory levels at warehouses – these robots, clean and scan in parallel.

5. operating costs

AI modules can identify bottlenecks and inefficiencies within the supply chain and provide solutions that reduce operational costs.  And, as mentioned above, AI can reduce labor costs by automation. 

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There are barriers, but they are worth overcoming.

As AI tools become more accessible, sourcing the right services becomes paramount to meet an organization’s needs for success.  While AI is growing and is the future of supply chain, there are still inherent challenges that need to be addressed and mitigated.

• Data integrity – while Gen AI in particular is getting to grips with unstructured data, greater accuracy comes from better data. Using AI for forecasting and decisioning in particular, starts with good data.
• Security –  AI systems can be vulnerable to cyberattacks, which can compromise sensitive supply chain data, leading to threats that can disrupt corporate systems and inflict significant damage. These emerging threats underscore the necessity for continuous monitoring, regular vulnerability assessments, and robust security measures within the supply chain.
• Technical expertise – Digitally literate resources, particularly those with data or prompting skills are coming in at a vast premium (in comparison to conventional pay grades), There is a war for talent, even more so diverse talent, meaning that candidates can pick, choose and out earn their peers. Not many companies are a destination of choice, which will fuel “As-a-Service” models.
• Change management – Generally AI offers fundamentally different ways of working and potentially a change in skills requirements for certain roles. The greater the level of democratization (self-serve), the greater the need to wrap change management around initiatives. In some cases, the gains may be more fundamental than marginal, without sufficient change effort, initiatives will fail, workers will feel stranded, and efficiencies lost.
• And everyone’s favorite, costs! – AI projects are vastly underestimated, which can significantly impact project outcomes. Several factors contribute to the total cost of an AI project, including the decision to build from scratch or buy AI models. Each choice carries its own cost implications and can easily surpass a forecasted budget if not carefully kept in check.

Artificial intelligence is transforming our world, and its influence is only expected to increase. AI enhances supply chain strategy, playing a crucial role in fostering organization-wide fluency and adoption of AI technologies. This advancement is instrumental in securing funding for the right AI use cases—those that promise clear returns on investment and drive further innovation.

For more information on how our procurement consultants can help your business address the challenges of changing technologies and advance your utilization of AI tools, get in touch now.