Operations Management Research Research Articles

Big data driven order-up-to level model: Application of machine learning

Data driven optimisation has become one of the research frontiers in operations management and operations research. Likewise, the recent academic interest in big data has created a desire for big data driven operations research. A new data driven methodology, which employs the empirical risk minimisation (ERM) principle, has recently been introduced in the inventory management literature. It has been used to formulate data driven inventory models which can take multiple features into account and do not need classical distributional assumptions. However, the research on big data driven inventory models is currently confined to the newsvendor model. In this paper, we aim to generalise the previous results on the big data driven newsvendor model and to expand the research by solving a big data driven dynamic order-up-to level inventory model. We show how the ERM methodology is employed to formulate a big data driven order-up-to level inventory model and design a machine learning algorithm to solve the model. The performance of our big data driven inventory model and solution algorithm is demonstrated by an experimental study based on real business data. The numerical results show that our integrated big data driven model generates up to 60% cost savings compared to the best performing univariate benchmark model, and up to 6.37% cost savings compared to the best performing big data driven benchmark model.

Enhanced Visualization of Production Systems Concepts and Simulation Data for the Smart/Digital Factory

Aircraft maintenance, repair, and overhaul (MRO) ensures the continuing airworthiness of aircraft or aircraft parts. Aircraft MRO is extremely costly: the U.S. Air Force spends most of its lifecycle costs to sustain, versus acquire, its aircraft. A key component of Smart Manufacturing for MRO is the use of new software tools for analyzing and exploiting manufacturing knowledge and data more effectively. This abstract describes two new analytic tools developed for the U.S. Air Force, enhanced with data visualizations, to enable more effective MRO production systems design and operations. Selecting appropriate mitigations to production disruptions is challenging because the relationship between control decisions and the resulting performance is delayed and non-linear. The Short-Term Flow Planning Tool helps analysts understand and plan for current and hypothetical changes and disruptions, such as equipment outages, by simulating the effects of various capacity adjustments, induction rates, part scrap rates, and buffer sizes on WIP, part processing rates, and part transfer rates for each process step and day during the planning period. To help analysts interpret this multivariate data, the Tool provides a set of complementary data displays, including heat maps (Figure 1), arrays of timelines and time series graphs (Figure 2), and animated bar graphs (Figure 3) which show snapshots of daily production status and performance. The operations management research literature provides many insights into manufacturing system behavior as well as methods for improving system performance. Unfortunately, many of these results have not been used by practitioners. Based on operations management principles, the Diagnostic Tree (D-tree) provides a systematic way of reviewing possible ways of improving a production system. A D-tree is composed of nodes which each describe a performance improvement objective. Many of the nodes are linked to sub-nodes that describe more concrete sub-objectives for achieving the objective and describe how the objective’s metric varies with the values of its sub-objective metrics. To help analysts understand these multivariate relationships, a sensitivity analysis function at selected nodes shows how the objective function varies with a user-selected variable, fixing the other variables at user-specified values. The dialog in Figure 1 prompts the user for values of the sub-objectives, and the graph in Figure 2 shows how the objective variable varies with the range variable -- in this case, sequential process batch size.

Visualization in Operations Management Research

The unprecedented availability of data, along with the growing variety of software packages to visualize it, presents both opportunities and challenges for operations management (OM) research. OM researchers typically use data to describe conditions, predict phenomena, or make prescriptions depending on whether they are building, testing, or translating theories to practice. Visualization, when used appropriately, can complement, aid, and augment the researcher’s understanding in the different stages of research (theory building, testing, or translating and conveying results). On the other hand, if used incorrectly or without sufficient consideration, visualization can yield misleading and erroneous claims. This article formally examines the benefits of visualization as a complementary method enhancing each stage of a broader OM research strategy by examining frameworks and cases from extant research in different OM contexts. Our discussion offers guidance with regard to researchers’ use of visual data renderings, particularly toward avoiding misrepresentation, which can arise with the incorrect use of visualization. We close with a consideration of emerging trends and their implications for researchers and practitioners as well as recommendations for both authors and reviewers, regardless of domain, in evaluating the effectiveness of visuals at each stage of research.History: Rema Padman and Kwok Tsui served as senior editors for this article.

Using Mobile Device Data to Understand the Effect of Stay-at-Home Orders on Residents’ Mobility

Problem definition: This study addresses three important questions concerning the effectiveness of stay-at-home orders and sociodemographic disparities. (1) What is the average effect of the orders on the percentage of residents staying at home? (2) Is the effect heterogeneous across counties with different percentages of vulnerable populations (defined as those without health insurance or who did not attend high school)? (3) If so, why are the orders less effective for some counties than for others? Academic/practical relevance: To combat the spread of coronavirus disease 2019 (COVID-19), a number of states in the United States implemented stay-at-home orders that prevent residents from leaving their homes except for essential trips. These orders have drawn heavy criticism from the public because whether they are necessary and effective in increasing the number of residents staying at home is unclear. Methodology: We estimate the average effect of the orders using a difference-in-differences model, where the control group is the counties that did not implement the orders and the treatment group is the counties that did implement the orders during our study period. We estimate the heterogeneous effects of the orders by interacting county features with treatment dummies in a triple-difference model. Results: Using a unique set of mobile device data that track residents’ mobility, we find that, although some residents already voluntarily stayed at home before the implementation of any order, the stay-at-home orders increased the number of residents staying at home by 2.832 percentage points (or 11.25%). We also find that these orders are less effective for counties with higher percentages of uninsured or less educated (i.e., did not attend high school) residents. To explore the mechanisms behind these results, we analyze the effect of the orders on the average number of work and nonwork trips per person. We find that the orders reduce the number of work trips by 0.053 (or 7.87%) and nonwork trips by 0.183 (or 6.50%). The percentage of uninsured or less educated residents in a county negatively correlates with the reduction in the number of work trips but does not correlate with the reduction in the number of nonwork trips. Managerial implications: Our results suggest that uninsured and less educated residents are less likely to follow the orders because their jobs prevent them from working from home. Policy makers must take into account the differences in residents’ socioeconomic status when developing new policies or allocating limited healthcare resources. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1014 .

A novel neutrosophic set based hierarchical challenge analysis approach for servicizing business models: A case study of car share service network

Servicization as a part of circular economy has a prominent role in operations management research by emphasizing the functionality of the products. Servicizing business model is a phenomenon that the products are converted into services for sale, or process of transforming the consumers into users. This newly introduced way of business is defined as a new opportunity for operations management by focusing on; the reliability and durability of the products against the extreme repeatedly use, coordination problems in conducting the business, risk of encountering unknown negative customers, and moral hazard in product usage, operational difficulties, and the degradation of the products. All of the aforementioned issues in implementing the servicizing business models constitute a research question with the high impact of COVID-19 pandemics. Since people avoid using public transportation, car share programs are highly in demand. In addition, there is an obvious research gap in challenge analysis of the car-sharing programs’ literature, an overall service network challenge analysis is missing in the field of study. Hence, the purpose of this study is proposing a novel neutrosophic set based hierarchical challenge analysis approach for the servicizing business models with a step by step guidance, and presenting a real world case study addressing a car sharing company’s entire service network. A practitioner can understand which challenge should be dealt with first, what is the inter-relationship structure of these challenges in a hierarchy, and what is the expected final result of these interactions. The study also contributes to the theoretical background by using neutrosophic sets to propose a novel interpretive hierarchical challenge analysis. In this research, the evaluation of truth and indeterminacy membership degrees of a neutrosophic set are gathered separately instead of applying the defined linguistic variable schemes. The case study is a contemporary field of study attracting attention of innovative entrepreneurs, since there is an increased demand of individual car driving due to the isolation requirement arisen from COVID-19 pandemics.

Prescriptive Analytics in Urban Policing Operations

Problem definition: We consider the case of prescriptive policing, that is, the data-driven assignment of police cars to different areas of a city. We analyze key problems with respect to prediction, optimization, and evaluation as well as trade-offs between different quality measures and crime types. Academic/practical relevance: Data-driven prescriptive analytics is gaining substantial attention in operations management research, and effective policing is at the core of the operations of almost every city in the world. Given the vast amounts of data increasingly collected within smart city initiatives and the growing safety challenges faced by urban centers worldwide, our work provides novel insights on the development and evaluation of prescriptive analytics applications in an urban context. Methodology: We conduct a computational study using crime and auxiliary data on the city of San Francisco. We analyze both strong and weak prediction methods along with two optimization formulations representing the deterrence and response time impact of police vehicle allocations. We analyze trade-offs between these effects and between different crime types. Results: We find that even weaker prediction methods can produce Pareto-efficient outcomes with respect to deterrence and response time. We identify three different archetypes of combinations of prediction methods and optimization objectives that constitute the Pareto frontier among the configurations we analyze. Furthermore, optimizing for multiple crime types biases allocations in a way that generally decreases single-type performance along one outcome metric but can improve it along the other. Managerial implications: Although optimization integrating all relevant crime types is theoretically possible, it is practically challenging because each crime type requires a collectively consistent weight. We present a framework combining prediction and optimization for a subset of key crime types with exploring the impact on the remaining types to support implementation of operations-focused smart city solutions in practice.

Resource Allocation with Sigmoidal Demands: Mobile Healthcare Units and Service Adoption

Problem definition: Achieving broad access to health services (a target within the sustainable development goals) requires reaching rural populations. Mobile healthcare units (MHUs) visit remote sites to offer health services to these populations. However, limited exposure, health literacy, and trust can lead to sigmoidal (S-shaped) adoption dynamics, presenting a difficult obstacle in allocating limited MHU resources. It is tempting to allocate resources in line with current demand, as seen in practice. However, to maximize access in the long term, this may be far from optimal, and insights into allocation decisions are limited. Academic/practical relevance: We present a formal model of the long-term allocation of MHU resources as the optimization of a sum of sigmoidal functions. We develop insights into optimal allocation decisions and propose pragmatic methods for estimating our model’s parameters from data available in practice. We demonstrate the potential of our approach by applying our methods to family planning MHUs in Uganda. Methodology: Nonlinear optimization of sigmoidal functions and machine learning, especially gradient boosting, are used. Results: Although the problem is NP-hard, we provide closed form solutions to particular cases of the model that elucidate insights into the optimal allocation. Operationalizable heuristic allocations, grounded in these insights, outperform allocations based on current demand. Our estimation approach, designed for interpretability, achieves better predictions than standard methods in the application. Managerial implications: Incorporating the future evolution of demand, driven by community interaction and saturation effects, is key to maximizing access with limited resources. Instead of proportionally assigning more visits to sites with high current demand, a group of sites should be prioritized. Optimal allocation among prioritized sites aims at equalizing demand at the end of the planning horizon. Therefore, more visits should generally be allocated to sites where the cumulative demand potential is higher and counterintuitively, often those where demand is currently lower. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.1020 .

Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

Problem definition: Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into or right after overbooked slots. That scheduling decision maximizes the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if a racial group is characterized by a higher no-show risk, then the patients belonging to that racial group will be scheduled into or right after overbooked slots disproportionately to the general population. Academic/Practical Relevance: That scheduling decision is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Thus, the challenge becomes minimizing the schedule cost while avoiding racial disparities. Methodology: Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we analytically study racial disparity in this context. Then, we propose new objective functions that minimize both schedule cost and racial disparity and that can be readily adopted by researchers and practitioners. We develop a race-aware objective, which instead of minimizing the waiting times of all patients, minimizes the waiting times of the racial group expected to wait the longest. We also develop race-unaware methodologies that do not consider race explicitly. We validate our findings both on simulated and real-world data. Results: We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Our race-aware methodology achieves both goals of eliminating racial disparity and obtaining a similar schedule cost as that obtained by the state-of-the-art scheduling method, whereas the race-unaware methodologies fail to obtain both efficiency and fairness. Managerial implications: Our work uncovers that the traditional objective of minimizing schedule cost may lead to unintended racial disparities. Both efficiency and fairness can be achieved by adopting a race-aware objective. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Funding: M. Samorani’s work was partly funded by the 2020 Leavey Research Grant. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2021.0999 .

OM Forum—A Vision of Responsible Research in Operations Management

Are we contributing positively to the society at large by research that we conduct in the field of Operations Management? Currently, the answer is probably closer to “no” than to “yes.” We often do not realize it, but there is very real cost of conducting our research nonresponsibly. In this invited OM Forum contribution, which is based on my Manufacturing and Service Operations Management Distinguished Fellow lecture given at the University of Texas at Dallas in 2018, I discuss what I believe it means to conduct research responsibly, I offer some specific steps to improve the relevance of what we do, and I argue that this approach gives our field a new opportunity to make broader impact by expanding the horizon of our work. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management.

Government Effectiveness from a Citizen View: Linking Public Participation and Information Sources

Recently, evaluations of government effectiveness gained a lot of scholarly attention and developed into a major research area in public and performance management research. This study builds on the rather new approach away from analyzing mere macroeconomic government performance indicators towards addressing the issue on a rather perceptional, citizen-based level. By combining the concepts of deliberative democracy and open government with media and communication theories (media richness, media malaise and virtuous circle), we argue that different forms of (i) getting engaged in decision-making processes and (ii) receiving public information can determine individually perceived government performance. Subsequently, interaction terms are established in order to analyze (iii) whether political participation possibilities can be linked to the consultation of different sources of information and in turn play a decisive role in influencing government effectiveness. Using data from the World Value Survey, multilevel regression estimates delivered significant outcomes for both interaction and main effects indicating that individual information source usage and patterns of political participation depend on each other and consequently affect levels of perceived government effectiveness.

Enablers of Industry 4.0 Technology Adoption: Transformational Leadership and Emotional Intelligence

Many manufacturers are exploring the adoption of Industry 4.0-type technologies in their operations. Employee’s contribution to high-tech initiatives is key to successful Industry 4.0 technology adoption. Few studies have explored the determinants of employee acceptance of Industry 4.0 technology adoption, including the role of their managers. Rooted in the unified theory of acceptance and use of technology model and social exchange theory, this abductive research follows an in-depth comparative case study approach. The two studied Dutch manufacturing firms engaged in the adoption of Industry 4.0 technologies in their primary processes, including cyber-physical systems and augmented reality. A mix of qualitative and quantitative methods was used, consisting of field visits, 14 semi-structured interviews with managers and frontline employees engaged in Industry 4.0 technology adoption, and the same participants filled in an exploratory questionnaire. The cross-case comparison introduces manager’s need to adopt a transformational leadership style for employees to accept Industry 4.0 technology adoption. Secondly, manager’s and employee’s recognition and serving of their own and others’ emotions through emotional intelligence are proposed as moderators to existing Industry 4.0 technology user acceptance theorizing. Synthesizing these insights with those from the domain of Organizational Behavior, propositions and a conceptual model were derived to “bring the manager back” into future Operations Management research.

Validating and Calibrating the Highway Capacity Manual Arterial Travel Time Reliability Methodology

The 6th edition of the Highway Capacity Manual (HCM6) introduced a methodology for estimating and forecasting arterial travel time (TT) distributions (TTD) and their associated travel time reliability (TTR) metrics. Recently, it was shown that the HCM6 severely underestimated both the TTD and the TTR metrics for a test network in Lincoln, NE, U.S. Subsequently, it was shown that the underestimation issue could be eliminated through a proposed calibration methodology. Because this validation and calibration work was done on a single, relatively short section of arterial roadway there is an open research question on whether this finding applies to longer and more congested arterial roadways. The goal of this paper is to validate and calibrate the HCM6 TTR methodology on five arterial roadway testbeds that are longer and more congested than the original testbed. Empirical data from the National Performance Management Research Data Set (NPMRDS) which is managed by INRIX was used to represent the ground truth. Similar to the original study, it was found that the HCM6 TTR methodology severely underestimated the TTDs, and their respective TTR metrics, on all five testbeds. This is problematic, because the HCM6 methodology indicates that the corridors had more reliable TT than the empirical data would suggest. It was also shown that the calibration methodology eliminated this underestimation. It is recommended that users of the HCM6 TTR methodology validate and, if necessary, calibrate the model using local empirical travel data.

OM Forum—Pandemics/Epidemics: Challenges and Opportunities for Operations Management Research

We reviewed research papers related to pandemics/epidemics (disease outbreaks of a global/regional scope) published in major operations management, operations research, and management science journals through the end of 2019. We evaluate and categorize these papers. We study research trends, explore research gaps, and provide directions for more efficient and effective research in the future. In addition, our recommendations include the lessons learned from the ongoing pandemic, COVID-19. We discuss papers in the following categories: (a) warning signals/surveillance, (b) disease propagation leading to pandemic conditions, (c) mitigation, (d) vaccines and therapeutics development, (e) resource management, (f) supply chain configuration, (g) decision support systems for managing pandemics/epidemics, and (h) risk assessment.

Thirteen years of Operations Management Research (OMR) journal: a bibliometric analysis and future research directions

The journal of Operations Management Research (OMR) is a rigorous journal that started its publication in 2008. It publishes short, focused research studies that advance both the theory and practice of operations management. Considering the relevant OMR’s contribution to the field of Operations Management in the last years, this study provides an overall assessment of the journal performance by conducting a retrospective review. To elaborate on OMR's temporal development in terms of publications, authors, affiliated institutions and countries, citation patterns, and conceptual structure, we extract publications from Scopus database for the period 2008–2020. We rely on bibliometric techniques in addition to bibliographic coupling, keyword analysis, and content analysis. 166 documents were analyzed using RStudio, VOSviewer, and Microsoft Excel. Findings emphasize OMR’s steady productivity growth (3.24%). Narrowly, Olhager J. is the most productive authors while Kalchschmidt M. and Stentoft J. are the most influential authors (H-index of 4). Furthermore, USA contributes to the highest number of publications while UK is the most influential country in terms of citations. Cranfield School of Management, UK stands as the top cited university. The analysis of the thematic evolution concludes to three main clusters: "Manufacturing and Supply chain Performance", "Six Sigma and Lean Management", and "Reshoring, Backshoring and Offshoring". This study recommends to further investigate the implications of the fourth industrial revolution and the sequels of COVID-19.

Making Theoretically Informed Choices in Specifying Panel‐Data Models

We argue that in analyzing panel‐data econometric models, researchers rely excessively on statistical criteria to determine model specification, treating it primarily as a matter of statistical inference. This inferential emphasis is most obvious in the common practice of using statistical tests (e.g., the Hausman test) to choose between fixed‐ and random‐effects specifications, often ignoring the assumptions underpinning these tests. For instance, the Hausman test depends on the true within‐panel (longitudinal) and between‐panel (cross‐sectional) parameters being equal. This assumption is often not justified, because longitudinal and cross‐sectional variances and covariances may manifest different underpinning mechanisms. In addition to different mechanisms often resulting in different variables determining within and between effects, within and between variables may also have different meanings. To help researchers make theoretically informed choices, we formulate five questions that can guide researchers to think of model specification in a theoretically rigorous way. We examine these issues with examples from both general management and operations management research. Importantly, we argue that addressing the questions regarding model specification must involve primarily theoretical and contextual judgment, not statistical tests.

The International Journal of Production Research celebrates its 55th anniversary with a content-based evaluation and analysis.

Since its foundation, the International Journal of Production Research (IJPR) has made a substantial contribution to operations management (OM) research, establishing IJPR as one of the most prestigious venues for OM research. This article employs a text mining technique called Latent Semantic Analysis to deduce the core areas of study published in IJPR since its establishment, as well as the evolution of the journal's focus on certain themes over time. The data set includes abstracts of all articles published in IJPR from the journal's founding to December 2015. The report selects sixteen critical areas of research and conducts a detailed analysis of their evolution throughout time. The findings of this data-driven analysis of IJPR's 55-year history of research not only give a detailed evaluation of prior research in the journal, but also provide critical insights into the evolution of key areas of operations management research.

Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics

Problem definition: Translate data from electronic health records (EHR) into accurate predictions on patient flows and inform daily decision making at a major hospital. Academic/practical relevance: In a constrained hospital environment, forecasts on patient demand patterns could help match capacity and demand and improve hospital operations. Methodology: We use data from 63,432 admissions at a large academic hospital (50% female, median age 64 years old, median length of stay 3.12 days). We construct an expertise-driven patient representation on top of their EHR data and apply a broad class of machine learning methods to predict several aspects of patient flows. Results: With a unique patient representation, we estimate short-term discharges, identify long-stay patients, predict discharge destination, and anticipate flows in and out of intensive care units with accuracy in the 80%+ range. More importantly, we implement this machine learning pipeline into the EHR system of the hospital and construct prediction-informed dashboards to support daily bed placement decisions. Managerial implications: Our study demonstrates that interpretable machine learning techniques combined with EHR data can be used to provide visibility on patient flows. Our approach provides an alternative to deep learning techniques that is equally accurate, interpretable, frugal in data and computational power, and production ready. History: This paper has been accepted for the Manufacturing & Service Operations Management Special Section on Responsible Research in Operations Management. Funding: The research was funded by Beth Israel Deaconess Medical Center. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0971 .

Case Article—Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations

This case discusses a process improvement project aimed at maximizing the use of hospital capacity as the flu season looms. Dr. Erin Kelly heads the observation unit and turns to predictive models to improve the assignment of patients to her unit. The case covers three major themes: (1) data analytics life cycle and interface of predictive and prescriptive analytics in the context of process improvement, (2) design and ethical application of machine learning models, and (3) effecting organizational change to operationalize the findings of the analysis. Realistic data, R code, and Excel models are provided. The rich context of the case allows for discussing change management in a healthcare organization, analytics problem framing and model mapping, service process capacity analysis and Little’s law, data summaries and visualizations, interpretable machine learning algorithms, evaluations of predictive model performance, algorithmic bias, and dealing with dirty data. The case is appropriate for use in courses in machine learning, business analytics, operations management, and operations research, both at the advanced undergraduate level and at the master’s/MBA level.

Special Issue: Ensuring Sustainability. New Challenges for Organizational Engineering

This special issue of the Journal of Industrial Engineering and Management is dedicated on the most recent and relevant research, theories and practices in Industrial Engineering and Operations Management presented at the 14th International Conference on Industrial Engineering and Industrial Management and XXIV Congreso de Ingeniería de Organización - CIO 2020.The CIO 2020 was held on 9th and 10th July 2020 in Leganés - Madrid and was organized by the Universidad Politécnica de Madrid (UPM) and Universidad Carlos III de Madrid (UC3M). This conference is promoted by ADINGOR (Asociación para el Desarrollo de la Ingeniería de Organización) and AIM (European Academy for Industrial Management).

How the speed limit policy affects travel speed?: Quasi-experimental approach

The objective of this study is to examine the effect of posted speed limit change on travel speeds. To do this, we use the National Performance Management Research Data Set (NPMRDS), which is a unique and rich dataset provided via the Federal Highway Administration (FHWA). Particularly, we focus on the impact of increased posted speed limit, implemented during June 2015, on interstate highways in Wisconsin including urban and rural areas between 2014 and 2016 by employing a quasi-experimental model with a panel dataset. Our findings clearly demonstrate that the posted speed limit change from 65 to 70 increases travel speed by 1.7 mph after controlling for location and time specific fixed-effects. Specifically, the posted speed limit change on Wisconsin interstate highways increases average travel speed of passenger and freight vehicles by 1.87 and 1.16 mph, respectively. Findings also show that speed variations have increased after the speed limit change, and higher variations do not occur in freight travel speeds, but in passenger vehicle travel speeds. These variations differently exist between urban and rural areas. We suggest that transportation agencies should consider these variations by vehicle types and places when they increase posted speed limit.