Automating Demand Forecasting For Major Logistics Company
Elder Research implemented an automated framework for time-series forecasting at a major logistics company. Logistics is a mature, technologically-advanced, and analytically-sophisticated industry. However, major efficiencies can still be realized by applying advanced analytics and data engineering. All business processes in logistics rely on accurate demand forecasting in the short, medium, and long-term to inform resourcing, planning, and staffing to support future needs. Our client was three months into a highly-visible, strategic analytics project and with an urgent need to integrate forecast results in their production system. Given the strategic importance of this project, they needed to quickly scale a prototype forecast model into their automated production system that interfaces with a new platform for their planners.
Elder Research provided a bridge between technical experts and the application development team responsible for time-series forecast implementation. We worked collaboratively with the prototype model authors, software developers and architects, database administrators, and business stakeholders to ensure that our production solution would meet their requirements, interface with existing systems, and provide the flexibility required for future development. We also provided a valued perspective on statistical and optimization techniques for the operations research team that created the prototype model.
In three weeks we delivered a functioning production time-series forecasting framework and within six months had scaled the framework to produce 35 million forecasts on over 2000 locations in under one hour. This framework features automated execution and algorithm selection at short, medium, and long-term horizons. At a two-week interval, our forecasts had a median accuracy of 88%, despite high variance in the characteristics of the entities being forecast. We also developed a flexible forecasting model API to enable easy inclusion/exclusion of time-series algorithms as better techniques are identified or existing algorithms are replaced.