Launched in fall 2020, the Extensiv Supply Chain Scholarship aims to foster the talent that will drive the future of our industry. Now in its fourth year, the Extensiv scholarship program hopes to inspire fresh ideas and voices in logistics and supply chains.
Now, we present Alicia Rodriguez, the Spring 2023 Extensiv Supply Chain Scholarship winner, and her essay describing how artificial intelligence (AI) and machine learning will revolutionize logistics and demand forecasting.
On a Thursday before a long weekend, I volunteered to help the warehouse associates at a subscription meal-kit company finish their holiday shipment. Wearing several layers of clothing against the refrigeration air, I carefully inserted peaches into protective sleeves for eight hours. Due to unanticipated forecast demand and labor shortages, corporate employees like myself volunteered time to help associates meet customer demands.
My interest in supply chain began with building a pricing-demand forecasting model to answer questions that plagued leadership: how many boxes ordered; would production need automation or manual preparation; would ingredient swaps occur? It has led to analyses on labor costs, presentations to the CFO on recurring and preventable supplier violations and managing increasing inventory issues. While forecasting demand will always remain a crucial component for many manufacturing and logistics companies, it will be AI and machine learning that will revolutionize the future of forecast demand and more importantly how companies will anticipate and cover their customers’ needs. “Close to three-quarters of supply-chain functions rely on the simplest method: spreadsheets” (1). Anyone who has ever managed thousands of data rows understands this method can be cumbersome, slow, and inaccurate in determining inventory.
Transforming manual tools to those empowered by AI would accomplish three main objectives:
- More all-inclusive and alternative sources of data.
- Quicker detection of unusual patterns.
- Faster and smarter decision-making.
Most companies instinctively include internal data points such as enterprise systems, point of sale (POS) systems and the inventory levels from their manufacturing and retail locations. “There are very few companies that actually go outside of this box and get data from sources that are completely unstructured, " says Meher Dinesh Naroju, director of AI services at Centific (2). These data sources are external factors such as social media from targeted demographics, weather patterns, and retail traffic that reflect the complexity of purchasing materials. AI tools can survey through large amounts of this unstructured data to generate condensed predictions for warehouse operations and logistics management and more importantly allow companies to make decisions that incorporate the full picture.
AI technology encourages better decision-making because it automates the laborious review of supply-chain processes to “prioritizing actions that will reduce excess inventory while maximizing production readiness” (3). Production readiness is based on several indicators, including lead time, demand, order quantity, procurement and safety policy. AI can confidence score all these indicators in real time to let business leaders quickly know which factors are important and should be prioritized. Silos between different teams can be broken, and the company can focus on shared objectives that lead to cost savings and accurate management of their assets.
Had the meal-kit company I worked in maximized their data structure to better predict demand, we may not have had to utilize expensive corporate labor to cover customer orders. These experiences have inspired me to continue to pursue my career in supply chain optimization. Thus, I am excited to pursue my graduate degree at The Wharton School of the University of Pennsylvania by majoring in Operations, Information, and Decisions. I want my career to focus on being a leader who is data driven and grounded in science-based management to make decisions such as AI powered forecast planning to build more sustainable and effective supply chain systems.
Sources:
(1) “Demand Forecasting in Supply Chain Technology | McKinsey.” n.d. www.mckinsey.com. https://www.mckinsey.com/capabilities/operations/our-insights/to-improve-your-supply-chain-modernize-your-supply-chain-it
(2) “AI Opens New Frontier in Supply Chain Planning.” n.d. Supply Chain Dive. Accessed June 30, 2023. https://www.supplychaindive.com/news/inventory-demand-forecasting-ai-machine-learning/650781/
(3) “How AI Technology Trends Are Impacting Supply Chain Operations | SupplyChainBrain.” n.d. Www.supplychainbrain.com. https://www.supplychainbrain.com/blogs/1-think-tank/post/37185-how-ai-technology-trends-are-impacting-supply-chain-operations
