Introducing SDI AIM, an in-house platform built around the realities of industrial demand, critical spares, and maintenance-driven supply chains.
Industrial organizations rarely have an inventory problem in isolation. Instead, they face a compounding set of challenges:
- An uptime problem: Stock too little, and a missing part can delay maintenance or extend costly downtime.
- A working-capital problem: Stock too much, and capital becomes tied up in materials that may sit untouched for years.
- A decision-quality problem: Balancing the two requires speed, consistency, and confidence.
The goal is not simply to carry less inventory or to maximize availability at any cost. It is to understand where each inventory dollar earns its place. That requires a forecasting approach designed specifically for MRO (Maintenance, Repair, and Operations) from the beginning.
MRO Demand Does Not Behave Like Retail Demand
Many forecasting platforms were built around consumer demand, assuming frequent transactions, visible seasonality, short replenishment cycles, and products that can easily be substituted. Those assumptions simply do not hold up inside an industrial facility.
In MRO environments, demand patterns look vastly different:
- Critical but Infrequent: A repair part may have little or no recent usage yet remain essential to keeping a production asset running.
- Event-Driven: Demand is often tied to unpredictable equipment failures or planned maintenance rather than predictable consumer behavior.
- Complex Logistics: Lead times may be long or highly inconsistent.
- High-Stakes Consequences: The operational cost of not having the right part can far exceed its purchase price.
When a general-purpose model encounters these patterns, it can mistake infrequency for insignificance or treat a sudden need as statistical noise. For MRO teams, that creates a familiar and expensive tension: excess inventory in some categories and unacceptable risk in others.
Turning Usage History Into Better Inventory Decisions
This is the challenge behind SDI AIM (Adaptive Inventory Management). While “adaptive inventory management” is a recognized supply chain concept, AIM is a proprietary term and platform coined specifically by SDI.
As SDI’s new in-house, AI-driven forecasting system, AIM combines modern forecasting methods with decades of hands-on inventory and warehouse management expertise. It is built to interpret usage history through the realities of industrial and MRO supply chains.
AIM generates forecasts and suggested stocking levels with three practical objectives:
- Improve forecast accuracy.
- Better align inventory investment with actual operational need.
- Give customers deeper insight into how materials are purchased, used, and replenished.
That distinction matters. AI offers little value when it simply adds complexity to an existing process. Its true value comes from helping teams make better decisions—with recommendations that reflect the actual environment in which those decisions will be used.
Why Building AIM In-House Changes the Equation
Owning the forecasting platform end-to-end gives SDI greater control over how it develops. Instead of depending on a third-party system built for broader use cases, SDI can:
- Improve AIM faster.
- Validate it directly against real customer operations.
- Tailor future capabilities to the way industrial teams actually manage MRO inventory.
Created and developed by SDI’s Predrag Krstic and Benjamin Heinzerling, AIM translates the company’s operating knowledge into a platform SDI can continuously refine. Their work established more than a new forecasting engine; it created an internal foundation for ongoing innovation in inventory management.
AIM is also designed as a connected layer of SDI’s broader digital supply chain ecosystem, working seamlessly alongside ZEUS and the processes that support customer operations. That integration matters because forecasting is not a standalone exercise—its value is realized when better insights can actively inform replenishment, planning, and broader inventory strategy.
AI Should Strengthen Operational Expertise
Industrial inventory decisions still require context. While usage history can show what happened, maintenance, reliability, and warehouse teams understand why it happened, what is changing, and where operational risk is concentrated. The strongest approach connects model-generated recommendations with that human expertise.
This is especially important in MRO, where a low-use item can still be critical, and historical demand does not always reveal the full consequence of a stockout. AI can make analysis more scalable and consistent, but its greatest value comes from helping experienced teams evaluate trade-offs and act with greater confidence.
That reflects a broader principle for digital transformation in MRO: the best technology does not replace operational knowledge. It makes that knowledge easier to apply across sites, categories, and decisions.
A Foundation for What Comes Next
AIM launches with enhanced AI-driven forecasting, but the platform is designed to expand. Additional capabilities and enhancements are planned for future phases as SDI continues building a more connected, intelligent digital supply chain ecosystem.
For SDI, AIM represents more than replacing a third-party forecasting system. It is a shift toward owning the technology that supports a core area of customer value—and using that ownership to bring data, domain expertise, and operational execution closer together.
For customers, the promise is equally direct: smarter inventory decisions, stronger visibility, and a forecasting approach built for the realities of the plant floor.
Want to see what SDI AIM could reveal about your inventory? Connect with SDI: https://www.sdi.com/contact/
