Mass customization and AI-powered supply chains: Process acceleration and Uber models for resource allocation, Part II

Part 2: Automation and Organizational Issues

by Kishore Jethanandani

Introduction

Forecasting error is not eliminated entirely by predictive analytics. Vendors are responding by digitally automating their processes for demand sensing, faster deliveries, and multiple methods of reaching customers. The automation also generates vast volumes of data that artificial intelligence processes to align inventory flows with demand.

Automation and shorter lead times

Shorter lead times between the emergence of demand signals and deliveries mitigates the risk of a mismatch between supply and demand. “Automation of business processes up and down the supply chain help to respond rapidly to unexpected demand signals,” Colin Kessinger noted. “The cushion that the shorter lead times provide leave room to configure products closer to market and respond quickly to product variations,” EJ Tavella, Partner of End-to-End Analytics, told us. “Artificial Intelligence plays a major role in ensuring the expected times of arrival are achieved by monitoring anomalies such as delays caused by weather, events, breakdowns, and evaluate course…