000			01439nam a22001337a 4500
999			_c532718 _d532718
008			260226b ||||| |||| 00| 0 eng d
100			_aDavenport, Thomas H and Bedman, Thomas C. _959639
245			_aHow to marry process management and AI: Make sure your people and your technology work well together
260			_aHarvard Business Review 103(1), Jan-Feb, 2025: p.39-44
300			_a103(1), Jan-Feb, 2025: p.39-44
520			_aWhen Mars Wrigley decided to digitize its supply chain, it invested in several AI and analytics capabilities. It built a digital twin of its production line (a virtual replica simulating its operations in real time) and fed data from it into a machine-learning model to predict the line’s output and reduce overfilling and waste. It worked with a “decision intelligence” vendor, Aera Technology, to create visualizations of the data, generate recommendations about preventive maintenance, and automate some operational decisions. It hired Kinaxis, a vendor whose AI software gave the staff suggestions on how to balance supply and demand, automate invoice processing, and increase truck utilization by 15%. As a result of all these improvements, the company was able to fill orders more quickly, and customer service ratings rose by a couple of percentage points.- Reproduced https://hbr.org/2025/01/how-to-marry-process-management-and-ai
773			_aHarvard Business Review
942			_cAR