40 Questions Supply Chain Managers Should Ask Before Trusting AI Forecasts and Decisions
Your AI tools make recommendations at machine speed, but your organisation still relies on your judgement when those tools fail. The questions you ask before acting on an AI output determine whether you catch the edge cases your training data missed or whether you're flying blind when the next disruption hits.
These are suggestions. Use the ones that fit your situation.
Demand Forecasting: Questions to Ask Before Acting on AI Predictions
1What specific events or conditions in the past 18 months were outside the normal range that trained this model, and did the forecast accuracy drop during those months?
2If a major supplier went offline tomorrow, would this forecast still assume current lead times, and how would I know?
3What is the oldest piece of data in the training set, and does it include the last major recession or supply shock relevant to your category?
4Blue Yonder or Oracle are showing a 15 percent increase in demand next quarter. Can you manually trace back the three strongest signals driving that change?
5How many weeks into the future does the model confidence genuinely hold, versus how many weeks you are actually planning for?
6If your largest customer's behaviour changed by 20 percent, how long would it take the AI model to detect that shift, and what damage happens in the meantime?
7Are you seeing similar forecast patterns across your industry peers who use the same SAP or Blue Yonder system, and if so, does that concentration risk worry you?
8What seasonal patterns or promotional cycles did the model learn, and do you still plan promotions the same way the training data assumed?
9Does the forecast account for your own production constraints, or does it assume you can manufacture or source anything at any time?
10When was the last time you overrode this model's forecast, what did you know that the model did not, and was your judgement correct?
Supplier Management: Questions Before Letting AI Score and Rank Suppliers
11Your Oracle or SAP supplier scorecard is ranking suppliers on on-time delivery and cost. What is not being measured that matters in a crisis?
12If a key supplier's score drops 10 points overnight, can you name the specific transaction or metric that caused it, or is it buried in a weighted algorithm?
13Which of your suppliers has warned you about upcoming problems that their transaction history would not yet show the AI?
14Has the AI flagged a supplier as at risk who you know is actually stable, or missed a warning sign on a supplier you now know was fragile?
15When you relied on the AI ranking to deprioritise a supplier, did you lose relationship depth with that supplier that you could not recover when you needed them again?
16Your AI tool is recommending you consolidate spending to fewer suppliers for efficiency. What relationship capital and backup capacity are you giving up?
17Does the AI scoring system know which of your suppliers have geographical or regulatory exposure that could disrupt you, or does it only see their past performance?
18How often do you actually speak to your top ten suppliers without reading their AI score first, and has that frequency changed?
19If ChatGPT or another LLM is helping you draft supplier communications, are you losing the tone and context that maintained those relationships through previous disruptions?
20Which suppliers do you still assess manually because you do not trust the AI, and what are you learning from them that the algorithm does not capture?
Inventory and Optimisation: Questions Before Trusting AI Speed on Stocking Decisions
21Llamasoft or SAP is recommending you hold 20 percent less safety stock for a critical component. What buffer are you removing if the model's assumptions go wrong?
22Your AI optimisation system adjusts inventory across multiple locations automatically. How often are you reviewing those changes before they lock in?
23If a component goes obsolete faster than the AI model predicted, how much excess inventory will you have written off before you can intervene?
24Does your AI system know about planned maintenance windows, factory shutdowns, or seasonal capacity limits, or is it recommending production and stock based on normal-case assumptions?
25When the AI recommends you hold zero safety stock on a fast-moving SKU, what single supplier issue would cause a stockout, and how many customers would that affect?
26Your inventory optimiser is making dozens of small reorder decisions each day. Are you tracking which decisions create actual supply chain problems that you only discover days later?
27If you trust the AI to manage inventory at the speed it recommends, who is watching for the edge case where the model's logic breaks down?
28Does the AI account for the real lead time you experience from each supplier, or the theoretical lead time that supplier stated?
29Your Blue Yonder or Oracle system flagged a component as overstock. Before you reduce orders, did you check whether another part of your organisation just submitted an urgent request for that component?
30When was the last time you caught an inventory mistake before the AI system's recommendation became an actual purchase order?
Systemic Risk and Resilience: Questions About Shared AI and Industry Fragility
31How many of your direct competitors use the same SAP, Blue Yonder, or Oracle forecasting logic, and what happens if you all act on the same signal at the same time?
32If a major AI tool supplier (SAP, Oracle, Blue Yonder) announces a model update that changes forecasts across your entire industry, how would you know whether the change was improvement or a systematic blind spot?
33Your industry peers who use the same AI tools are also optimising inventory downward. Is your supply chain more or less resilient than it was three years ago?
34If your region experiences a disruption outside the training data of the major AI platforms, will your competitors face the same forecasting failure at the same moment?
35When you stopped making manual judgement calls on inventory and demand, what knowledge did your team lose that you cannot quickly rebuild?
36Your suppliers are also using AI to make their own inventory and production decisions. Are you creating a system where everyone is reactive to the same signals instead of holding buffers?
37If an AI model fails during a crisis and you need to make fast decisions manually, how many of your team have done demand planning or supplier negotiation without the AI?
38Are you building supply chain resilience (multiple sources, strategic buffers, relationship depth) or just optimising for cost at the speed an algorithm can move?
39Your ChatGPT prompts or other LLM use is helping you move faster. What are you not stopping to question because you are moving too fast?
40If the AI tool you rely on becomes unavailable for a week, what supply chain decisions stop until it comes back online?
How to use these questions
Keep a log of forecasts the AI model made three months ago and what actually happened. Review it monthly to know when the model struggles. Use that pattern to spot the next blind spot.
Block time each week to talk directly to two suppliers without looking at their AI score first. Ask them what risks they see coming that their recent transaction history would not show an algorithm.
When an AI tool recommends a decision at speed, pause and ask yourself what you would do if you had to manually own that decision for the next three years. If the answer changes, you are trusting the AI too far.
Document every time you overrode an AI recommendation and why. Share those cases with your team so they build the same instinct for when the model is likely to fail.
Run a quarterly audit: which critical supply chain decisions are now made purely by AI without your review, and which of those would create serious damage if the algorithm was wrong?