What Building Central Asian Supply Chains Taught Me About AI
I took over spare-parts supply at BPK Auto in Ust-Kamenogorsk in the mid-2000s, at the age of 25, moving from selling cars. The company was the general dealer for VAZ in Kazakhstan, assembled Niva models in the city, and was taking on Skoda, Chevrolet, and Kia while building service centers across the country to meet the expectations of those brands for a dealer. My job was to keep parts flowing to all of it. Several thousand vehicles came through in a month, and a single car contains something like 50,000 separate part numbers. The service teams wanted as many of those as possible sitting on the shelves. From where they stood, that made sense, because they were judged on whether a mechanic could start a repair the same day, whether a customer complained, and an ISO audit was running over the service operation at the same time. From the procurement side, the arithmetic looked different. Some parts cost two or three thousand dollars and were fitted once or twice a year, and holding them across four model lines tied up a large share of the company's working capital. I built the forecast in spreadsheets and checked it against actual sales once the month closed in 1C. To run it, I needed the service side's own records: how many vehicles of each type they had seen, and what had been repaired. I asked for them for months. They would not release them. Some of that was habit; a warehouse full of everything being what a well-run operation had looked like for the previous 40 years. More of it sat in how the two departments were counted. My result was recorded the moment the parts moved into the service center. Theirs was recorded much later, when the car left the workshop. Nobody was measured by the number that would have made the forecast work. The forecasting was the straightforward part of that job. The difficult part was building an operation in which people shared the information behind the forecast and were expected to act on its findings. Why a Better Model Would Not Have Changed the Outcome Put one of today's AI demand-forecasting systems into that same warehouse and very little changes. The forecast would be far better than anything I produced by hand. It would still depend on service records that nobody was willing to release, and where records did exist, it would be reading entries written up at the end of a long shift. It could not grant the purchasing manager authority to reorder against it, nor could it alter the measures by which the service department was judged. Better intelligence does not repair the path between operational data and operational action. Every party you add to that path lengthens it. A distribution network across Kazakhstan and Kyrgyzstan can run from a manufacturer to over 45,000 small retail outlets, served through more than 18 distribution centers, with 1,391 field staff taking and checking orders on the ground. An order begins...
