Understand the requirement before developing the solution
Artificial intelligence is changing the economics of software development. Tools that once required significant development resource can increasingly be prototyped, tested and refined more quickly, creating opportunities for businesses to develop applications around requirements that may previously have been difficult to justify commercially.
For supply chain teams, the possibilities are considerable. Forecasting, inventory management, replenishment, supply planning, production planning, capacity management, scenario modelling and exception management all involve decisions that can potentially be supported by more bespoke tools.
The opportunity, however, creates a risk. As developing the technology becomes easier, it can become tempting to start with what can be built rather than with what the supply chain actually needs.
AI can assist with building a system, but it does not remove the need to define the operational problem, select an appropriate methodology, establish the business rules or determine whether the resulting recommendations make sense in practice. Those remain supply chain questions.
The starting point for an AI-enabled supply chain system should therefore be no different from any other system: understand the requirement before developing the solution.
The difficult part is not necessarily building the tool
Consider something apparently straightforward such as an inventory planning tool. At a high level, the requirement might be described as calculating how much inventory a business needs to hold and when it should replenish it.
Building an application capable of performing calculations is only part of that requirement. Before development begins, decisions need to be made about how demand should be represented, how variability should be measured, what lead time means within the calculation, how service requirements should influence inventory and how supplier or operational constraints should be incorporated.
The methodology also needs to accommodate the characteristics found in real supply chains. Regularly demanded products may require different treatment from intermittent items. New products may have very little history. Promotions and exceptional demand can distort historical patterns, while minimum order quantities, production constraints, shelf life, purchasing agreements and capacity limitations can affect whether a mathematically attractive recommendation is operationally useful.
None of these questions is primarily about AI. They are questions about forecasting, inventory, planning and operations. Technology can execute the resulting logic, but the quality of the tool will still depend on the quality of the decisions made when that logic is defined.
Begin with the decision that needs to improve
One consequence of faster development is that businesses can move from an idea to a prototype without necessarily spending enough time defining the requirement in between.
Prototyping is valuable, particularly when the purpose is to test an idea against real data and expose weaknesses quickly. It becomes less useful when development itself is being used to discover what the tool is supposed to achieve.
A better starting point is to define the decision that needs to improve. That may be determining an appropriate replenishment quantity, identifying inventory at risk, allocating constrained capacity or highlighting the exceptions that genuinely require planner intervention.
Once that decision is understood, the supporting requirements can be established. The business can determine which data is available, what methodology should be applied, what constraints need to be recognised, what information the user needs and what circumstances should cause the system to behave differently.
That creates a much stronger development brief because it separates the underlying supply chain requirement from the way the eventual application happens to implement it.
Plausible outputs still need to be challenged
One of the difficulties with analytical and AI-enabled tools is that their outputs can appear authoritative. A precisely calculated inventory recommendation, a detailed forecast or a well-presented planning dashboard can create considerable confidence in the result.
Presentation, however, does not validate the reasoning behind it.
An inventory recommendation is only useful if the assumptions behind the calculation are appropriate. A forecast needs to be assessed against the characteristics of the demand it is attempting to predict. A capacity model needs to reflect the constraints that genuinely determine operational capacity rather than simply those that were easiest to represent in the model.
This makes validation an important part of development. The question is not simply whether the application performs the intended calculation, but whether that calculation produces sensible decisions under the range of circumstances the business is likely to encounter.
Historical data can be particularly useful here. Where appropriate, a proposed methodology can be tested against previous demand, supply and operational conditions to examine how the tool would have behaved. Different approaches can be compared and unexpected results investigated before recommendations begin influencing live decisions.
The objective should not be to demonstrate that a new system works. It should be to understand where it does not.
Faster development should allow more testing, not less
AI-assisted development creates an interesting opportunity because reducing the effort required to create and modify a prototype should make it easier to test alternative approaches.
Rather than treating requirements, development and validation as entirely separate stages, businesses can potentially work through shorter iterations. A requirement can be defined, sufficient functionality developed to test the underlying concept, the resulting decisions examined and the methodology refined where weaknesses become apparent.
For supply chain applications, this can be particularly valuable because different products, suppliers and operating conditions often expose behaviours that are difficult to anticipate from methodology alone.
A forecasting approach may perform well overall but consistently struggle with one type of demand. An inventory model may behave appropriately for most products but generate excessive holdings where minimum order quantities dominate the calculation. An exception-management tool may correctly identify unusual conditions but create so many alerts that planners cannot distinguish what genuinely requires attention.
Finding these weaknesses is not evidence that development has failed. It is part of establishing whether the solution is robust enough to support real operational decisions.
The ability to change a tool quickly should therefore encourage more challenge, not reduce the discipline applied to its design.
Business rules deserve the same attention as the calculations
Not every important element of a supply chain system is a sophisticated algorithm. Much of the value can sit within comparatively straightforward business rules.
A replenishment recommendation may need to respect a supplier’s minimum order quantity. A capacity-planning tool may have to recognise when one resource can substitute for another. An inventory application may need different treatment for products approaching the end of their life. An exception-management tool needs rules determining which conditions warrant attention and which can safely remain automated.
These rules translate operational knowledge into system behaviour.
If they are incomplete, poorly understood or contradictory, implementing them more quickly does not solve the underlying problem. It simply allows the business to automate an unclear process more efficiently.
Requirements definition for an AI-enabled system should therefore include both the analytical methodology and the practical rules surrounding it. The objective is to capture enough of the operational context for the resulting tool to support the way decisions actually need to be made.
Users still need to understand the decision
There is also a practical question of explainability.
Users do not necessarily need to understand every technical element behind a system, but the people responsible for supply chain decisions need sufficient understanding to recognise what an output represents, why it may have changed and under what circumstances it should be challenged.
This becomes particularly important where a system is making recommendations rather than simply presenting information.
If a planner is asked to approve a substantial change in inventory, capacity or supply without understanding the principal factors driving the recommendation, the apparent sophistication of the system can become a weakness. Either the user accepts the answer without sufficient challenge or distrust develops and recommendations begin to be routinely overridden.
Good system design should therefore consider not only how to calculate an answer, but how that answer will support human decision-making. The appropriate balance between automation and intervention will vary by application, but the principle remains the same: technology should improve the quality and efficiency of the decision rather than distance the user from it.
AI changes the development opportunity, not the underlying requirement
There is genuine potential in using AI to support the development of supply chain systems. More accessible development can make bespoke solutions viable where a commercial platform would be disproportionate to the requirement, allow businesses to address specific gaps between existing systems and make experimentation with alternative approaches considerably easier.
What has not changed is the need to understand the problem before designing the solution.
A forecasting tool still needs an appropriate forecasting methodology. An inventory application still needs sensible policies and parameters. A planning tool still needs to represent relevant constraints and business rules. All of them need suitable data, testing and operational validation.
The technology used to build the application may be new, but many of the questions that determine whether the resulting system is useful are not.
AI should therefore be seen as an increasingly capable way of developing and refining supply chain tools, rather than as a substitute for determining what those tools should do. Used in that way, it can shorten the distance between a well-defined requirement and a practical solution while allowing ideas to be tested and improved more readily.
The important sequence remains the same: understand the decision, establish the methodology and business rules, define the requirement, build the tool, test it against operational reality and refine it where necessary.
AI can help build the system. Supply chain expertise makes sure it builds the right one.