AI + ERP: Stronger Together
Artificial intelligence (AI) is rapidly becoming a powerful tool in the supply chain leader’s toolkit. Although there is no shortage of hype surrounding AI, the most successful organizations are finding practical ways to leverage its capabilities to improve decision-making, accelerate analysis, and uncover opportunities hidden within their data. It is important to note that AI is not a replacement for ERP, MRP, MPS, APS, or business intelligence systems. These systems remain the foundation for transactions, planning, execution, and data integrity. Instead, AI enhances their value by helping organizations analyze information faster, evaluate alternatives, and make more informed decisions. When combined effectively, the result is far greater than the sum of the parts. Think of it as an equation where 1+1 equals 22. AI provides the speed, flexibility, and analytical horsepower while ERP and related systems provide the structure, data, and operational backbone required to achieve sustainable results.
Case Study: Utilizing AI for What If Scenario Analysis
Several clients have been utilizing Chat GPT and AI to perform what if scenario analyses. Although AI is not at the level to replace an MPS, MRP or APS (advanced planning) system capabilities, it can perform a quick analysis or what if scenario with a bit of setup and effort. For example, a food and beverage manufacturer struggled mightily to get meaningful data from reports from their MPS/ MRP system. In essence, they received a data dump that lacked clarity, was missing data, and didn’t provide directionally correct information for decision-making. Thus, Operations didn’t know what to produce, let alone know how to optimize sequencing to increase throughput and better serve customers. Supply chain planners didn’t know what to replenish to their distribution centers to meet customer needs without overstocking one facility and creating shortages in several others. And Purchasing had no idea what to purchase from co-manufacturers, co-packers, and material suppliers. An unacceptable situation.
Although they were going to roll out MPS/ MRP and upgrade the reporting with Power BI, it would take longer than acceptable to ensure customers were served. Thus, they downloaded the information from their ERP system into Claude and created a scenario to review requirements due through the end of the year. Although it took setup and practice runs, they were able to get the answer within a day. Thus, the planners and buyers could jump into action to ensure inventory availability levels increased and customer service levels were attained.
Case Study: Utilizing AI for ROI Analysis
A consumer products distributor was growing and running out of space even though they optimized their space and labor planning processes, improving results by at least 20%. In working with the client, we developed an AI-enabled SIOP (Sales Inventory Operations Planning)/ supply chain planning model, which was fed information from ERP and related systems. This model built forecasts with meaningful variables related to space and labor requirements, calculated space requirements for A, B and C items based on the pareto principle, recommend how to optimize space and minimize steps by positioning A items closest to the dock door and determined gaps and recommended action items. This model would fuel their Power BI tool set for enhanced decision-making.
One of the recommended actions was to evaluate purchasing an automated vertical storage system for installation prior to running out of space, thereby negatively impacting service levels, efficiencies and costs. As volume and margins are essential to distributors, getting ahead of this situation was of paramount importance. After a review of the benefits and costs, it seemed worthwhile to pursue; however, analyzing freight details and operational efficiencies is a data-intensive task. Thus, the team turned to Claude. Within a short period of time and ten thousand calculations and iterations later, AI provided an ROI type analysis so they could pursue the appropriate approvals.
The Future of Supply Chain Decision-Making
These examples illustrate where AI is delivering meaningful value today. In neither situation did AI replace ERP, MRP, APS, Power BI, or other enterprise systems. Instead, it leveraged data from those systems to accelerate analysis, evaluate alternatives, and support better decision-making. In one case, AI enabled planners and buyers to quickly identify supply requirements and improve customer service while waiting for system enhancements. In another, AI helped analyze thousands of variables and scenarios to support an investment decision with significant operational and financial implications.
As manufacturers, distributors, and supply chain organizations continue to invest in ERP, planning systems, analytics, and digital transformation initiatives, they should also evaluate where AI can complement those capabilities. The greatest opportunities often emerge when AI is combined with strong business processes, quality data, and enterprise systems. When used effectively, AI can help organizations respond faster, evaluate more alternatives, improve decision-making, and achieve better business outcomes. Rather than viewing AI as a replacement for existing technologies, leading organizations are finding that combining AI with ERP, planning systems, and analytics platforms creates a powerful multiplier effect where 1 + 1 equals 22.
If you are interested in reading more on this topic:
Supply Chain Visibility Fueling Faster, Smarter & Proactive Decision-Making