Tarkan Tan

Fleet electrification is essential to decarbonizing transportation. However, commercial adoption, particularly in heavy-duty segments, remains limited and falls short of zero-emission targets. As the primary decision makers, fleet operators face complex trade-offs that place this transition squarely within the scope of operations management (OM). This review adopts an OM perspective to synthesize and classify the expanding literature on fleet electrification, drawing also from operations research (OR), transportation, and energy domains. The contributions of this review are threefold: (i) Literature synthesis: We identify key decision factors at the strategic, tactical, and operational levels, connecting them to relevant academic work and supporting them with recent data and practical examples. (ii) Practice-oriented insights: We extract lessons learned to inform decision making by fleet operators and related stakeholders. (iii) Research agenda: We propose open research questions across decision levels to guide future studies in the OM and OR communities.

Demand planning is informed by demand forecasts, service level requirements, replenishment constraints, and revenue projections. “Demand forecasts” differ from “demand plans” in that forecasts only represent the distribution (or the most likely value) of product demand. Motivated by common forecasting practices in industry, our research examines whether forecasters recognize this difference between demand forecasts and demand plans. Based on a lab experiment informed by data from two large FMCG companies, we found that forecasters factor service levels into their demand forecasts, even when they are clearly instructed to predict the most likely demand and incentivized to minimize the forecast error. We establish that this result holds for students and practitioners alike, and show that this behavior is driven by the service level information, and not some other anchor. We use data from a recent industry survey to support the external validity of our key findings.

This article addresses the challenges for companies that seek to invest in carbon abatement projects. Traditionally, such companies would address their own operations, but it might be more efficient to invest in improving the operations of a supplier or customer. By adopting a supply chain perspective on carbon abatement projects, this study proposes a way to account for activities beyond the company’s boundaries. First, the proposed decision-support framework enables companies to define the appropriate boundaries for carbon emission measurement and abatement and identify the relevant optimisation problem. Second, this framework describes options for optimising the level of abatement effort linked to each selected action. Third, an application of the framework to the Eastman Chemical Company reveals several real-world implications, including recommendations for where to exert carbon abatement efforts in a supply chain. As this illustrative case shows, external abatement efforts can be worthwhile in practice.

We consider an integrated usage and maintenance optimization problem for a k‐out‐of‐n system pertaining to a moving asset. The k‐out‐of‐n systems are commonly utilized in practice to increase availability, where n denotes the total number of parallel and identical units and k the number of units required to be active for a functional system. Moving assets such as aircraft, ships, and submarines are subject to different operating modes. Operating modes can dictate not only the number of system units that are needed to be active, but also where the moving asset physically is, and under which environmental conditions it operates. We use the intrinsic age concept to model the degradation process. The intrinsic age is analogous to an intrinsic clock which ticks on a different pace in different operating modes. In our problem setting, the number of active units, degradation rates of active and standby units, maintenance costs, and type of economic dependencies are functions of operating modes. In each operating mode, the decision maker should decide on the set of units to activate (usage decision) and the set of units to maintain (maintenance decision). Since the degradation rate differs for active and standby units, the units to be maintained depend on the units that have been activated, and vice versa. In order to minimize maintenance costs, usage and maintenance decisions should be jointly optimized. We formulate this problem as a Markov decision process and provide some structural properties of the optimal policy. Moreover, we assess the performance of usage policies that are commonly implemented for maritime systems. We show that the cost increase resulting from these policies is up to 27% for realistic settings. Our numerical experiments demonstrate the cases in which joint usage and maintenance optimization is more valuable. © 2017 Wiley Periodicals, Inc. Naval Research Logistics 64: 418–434, 2017