Jaime Macias Aguayo
Postdoctorate Associate MIT Center for Transportation & Logistics
- Cambridge MA
Jaime Macias Aguayo's research focuses on the economic value of digitalization in supply chains.
MIT Center for Transportation & Logistics
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Biography
Before joining MIT, Jaime was a Senior Lecturer in Industrial Engineering at ESPOL (Ecuador), where he taught courses in Sustainable Supply Chain Management, Stochastic Modelling, Forecasting, and Inventory Control. Prior to academia, he served as Head of Planning and Supply at IPAC-Duferco, a multinational in the steel sector, and worked as an independent manufacturing and logistics consultant.
His research focuses on the economic value of digitalization in supply chains, with particular attention to how Small and Medium-Sized Enterprises (SMEs) can adopt digital and automation solutions effectively. He develops analytical and empirical methods to quantify the value of digital technologies prior to adoption, enabling companies to make better-informed investment decisions.
Research Focus
Intelligent & Autonomous Supply Chains
AI-driven, data-centric, and increasingly autonomous decision systems for supply chain planning and execution.
Critical Systems & Supply Network Resilience
Ensuring continuity, preparedness, and mission assurance under disruption and geopolitical volatility.
Areas of Expertise
Links
Languages
- English
Media Appearances
How to Automate Operations Without Breaking the Bank
MIT Sloan Review online
2025-08-11
How to Automate Operations Without Breaking the Bank
Research Papers
When Do Forecasts Create More Value? A Bayesian Newsvendor Perspective
MIT Center for Transportation & Logistics Research PaperJaime Macias-Aguayo, María Jesus Sáenz, Ilya Jackson
2026-05-07
Problem definition: We study how forecast information creates economic value in Bayesian newsvendor decisions. We focus on the information structure induced by the forecast signal rather than treating it only as a point prediction. Academic/practical relevance: In practice, forecasts can induce a wide variety of information structures. Yet, studies that link these structures to inventory decisions have focused on a limited set of benchmark structures, thereby limiting firms' capacity to evaluate and select forecast information effectively. Methodology: We develop a Bayesian framework that models the full information structure induced by a forecast signal and estimates the resulting economic value. The framework allows for structures with non-zero conditional mean errors, heavy-tailed noise, heteroscedasticity, and standard benchmarks.
A Data-Driven and Context-Aware Approach for Demand Forecasting in the Beverage Industry
MIT Center for Transportation & Logistics Research PaperBenedict Jun Ma, Maggie Huang, Sebastian Villegas, Jaime Macias-Aguayo
2025-10-10
Accurate demand forecasting is essential for logistics and supply chain management as it enables efficient inventory planning, reduces operational costs, and ensures high service levels across the network. However, in practice, diverse demand patterns of items make this task challenging, and a one-size-fits-all forecasting approach is inadequate. This paper proposes a data-driven and context-aware forecasting framework and tests it by using both endogenous data from a large private-label beverage manufacturer and exogenous features (such as holidays and temperature). Our method begins by classifying SKUs based on demand volume, volatility, and intermittency, and then refining the derived clusters by taking volume distribution into account. Totally, we obtain four distinct clusters, which are (i) stable and high volume, (ii) stable with low volume, (iii) erratic and intermittent, and (iv) lumpy.
Valuing Real-Time Digital Monitoring for On-Time Operational Performance
MIT Center for Transportation & Logistics Research PaperJaime Macias-Aguayo, Duncan McFarlane, Maharshi Dhada
2025-09-04
Meeting delivery dates remains a major challenge for companies, as end-to-end lead times often fluctuate significantly. While many solutions in the literature seek to reduce external lead-time variability (e.g., supplier or transport delays), companies also face internal variability challenges (e.g., processing or loading delays) that drives tardiness. Real-time digital monitoring promises greater visibility into internal job status, enabling timely intervention; however, the economic value of investing in these solutions remains largely unquantified, and uncertainty about benefits often deters adoption. This article quantifies the economic value of real-time digital monitoring that flags lagging jobs to trigger expediting actions.
