ForecastFatima
Inventory Planner · FMCG sector
Our AI forecasting tool is great in normal conditions — MAPE around 6-8% which is honestly better than our old statistical models at 14%. But the moment something unusual happens (port strike, sudden viral product, weather event) it falls apart and our planners have to take over manually anyway. Is this just a known limitation or are there tools that handle disruptions better? Feels like we're paying for something that fails exactly when we need it most.
RajBhatia_SCM
Supply Chain Manager · 11 yrs FMCG
This is a known limitation and honestly its a training data problem not an AI problem. Models trained on 2018–2022 data have seen covid, suez, etc but the signal for a "new" disruption type isn't in the data yet. Best tools right now pair traditional demand signals with external data feeds — news sentiment, shipping index, social trends. One of our vendors added a "disruption flag" input where planners can manually set a multiplier. Hacky but it works.
EcoColdChain
Logistics Manager · Cold Chain
We had a product go viral on social media last October (totally unexpected, a cooking video). Our AI forecast was off by 340% for that week. I'm not even exaggerating. Stock was depleted in 48 hours. Some newer forecasting tools are actually connecting to Tiktok and Instagram trend APIs now — that's not a joke, its real. Haven't tried one yet but the concept makes sense. Traditional demand signals are just too lagged for this kind of thing.
DataSanjeev
Data Scientist · 3PL tech team
@ForecastFatima the 6-8% MAPE in normal conditions is genuinely very good. Most enterprises are at 10-15%. Your problem isn't the tool, its the expectation. No AI system today can predict black swans reliably — that's not a 2024 problem, its a fundamental forecasting problem. What good AI does is reduce your baseline error so your team has MORE capacity to handle the edge cases manually. Think of it as shifting effort, not eliminating judgment.
NinaG_Procurement
Procurement Lead · Retail
Worth noting — a lot of companies are now building "disruption playbooks" alongside their AI tools. Basically pre-agreed override rules: if news API detects port closure in X region, automatically apply Y buffer multiplier to affected SKUs. It's rule-based on top of ML, which sounds old school but the combo genuinely works better than either alone. Amazon has talked about this hybrid approach in a few public posts if you want to look it up. Our 2024 Q1 disruption was handled almost automatically because of it.
