The Future of Retail Operations

Retailing consists of all the activities associated with the selling of goods to the final consumer. In this article, we review the research on retail operations published in Manufacturing & Service Operations Research (M&SOM) since 1999. We then discuss the current retail landscape and the new research directions it offers, in which M&SOM can play a prominent role

Advertising and Word-of-Mouth Effects on Pre-launch Consumer Interest and Initial Sales of Experience Products

This study examines how consumers' interest in a new experience product develops as a result of advertising and word-of-mouth activities during the pre-launch period. The empirical settings are the U.S. motion picture and video game industries. The focal variables include weekly ad spend, blog volume, online search volume during pre-launch periods, opening-week sales, and product characteristics. We treat pre-launch search volume of keywords as a measure of pre-launch consumer interest in the related product. To identify probable persistent effects among the pre-launch time-series variables, we apply a vector autoregressive modeling approach. We find that blog postings have permanent, trend-setting effects on pre-launch consumer interest in a new product, while advertising has only temporary effects. In the U.S. motion picture industry, the four-week cumulative elasticity of pre-launch consumer interest is 0.187 to advertising and 0.635 to blog postings. In the U.S. video game industry, the elasticities are 0.093 and 1.306, respectively. We also find long-run co-evolution between blog and search volume, which suggests that consumers' interest in the upcoming product cannot grow without bounds for a given level of blog volume

Development of Machine Learning based approach to predict fuel consumption and maintenance cost of Heavy-Duty Vehicles using diesel and alternative fuels

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and effective methods to predict fuel consumption, maintenance costs, and total cost of ownership for heavy-duty vehicles. Every improvement so achieved in this direction is a direct contributor to driving the reduction in the total cost of ownership for a fleet owner, thereby bringing economic prosperity and reducing oil imports for the economy. Motivated by these crucial goals, the present research considers integrating data-driven techniques using machine learning algorithms on the historical data collected from medium- and heavy-duty vehicles. The primary motivation for this research is to address the challenges faced by the medium- and heavy-duty transportation industry in reducing emissions and operating costs. The development of a machine learning-based approach can provide a more accurate and reliable prediction of fuel consumption and maintenance costs for medium- and heavy-duty vehicles. This, in turn, can help fleet owners and operators to make informed decisions related to fuel type, route planning, and vehicle maintenance, leading to reduced emissions and lower operating costs. Artificial Intelligence (AI) in the automotive industry has witnessed massive growth in the last few years. Heavy-duty transportation research and commercial fleets are adopting machine learning (ML) techniques for applications such as autonomous driving, fuel economy/emissions, predictive maintenance, etc. However, to perform well, modern AI methods require a large amount of high-quality, diverse, and well-balanced data, something which is still not widely available in the automotive industry, especially in the division of medium- and heavy-duty trucks. The research methodology involves the collection of data at the West Virginia University (WVU) Center for Alternative Fuels, Engines, and Emissions (CAFEE) lab in collaboration with fleet management companies operating medium- and heavy-duty vehicles on diesel and alternative fuels, including compressed natural gas, liquefied propane gas, hydrogen fuel cells, and electric vehicles. The data collected is used to develop machine learning models that can accurately predict fuel consumption and maintenance costs based on various parameters such as vehicle weight, speed, route, fuel type, and engine type. The expected outcomes of this research include 1) the development of a neural network model 3 that can accurately predict the fuel consumed by a vehicle per trip given the parameters such as vehicle speed, engine speed, and engine load, and 2) the development of machine learning models for estimating the average cost-per-mile based on the historical maintenance data of goods movement trucks, delivery trucks, school buses, transit buses, refuse trucks, and vocational trucks using fuels such as diesel, natural gas, and propane. Due to large variations in maintenance data for vehicles performing various activities and using different fuel types, the regular machine learning or ensemble models do not generalize well. Hence, a mixed-effect random forest (MERF) is developed to capture the fixed and random effects that occur due to varying duty-cycle of vocational heavy-duty trucks that perform different tasks. The developed model helps in predicting the average maintenance cost given the vocation, fuel type, and region of operation, making it easy for fleet companies to make procurement decisions based on their requirement and total cost of ownership. Both the models can provide insights into the impact of various parameters and route planning on the total cost of ownership affected by the fuel cost and the maintenance and repairs cost. In conclusion, the development of a machine learning-based approach can provide a reliable and efficient solution to predict fuel consumption and maintenance costs impacting the total cost of ownership for heavy-duty vehicles. This, in turn, can help the transportation industry reduce emissions and operating costs, contributing to a more sustainable and efficient transportation system. These models can be optimized with more training data and deployed in a real-time environment such as cloud service or an onboard vehicle system as per the requirement of companies

The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions

The motion picture industry has provided a fruitful research domain for scholars in marketing and other disciplines. The industry has high economic importance and is appealing to researchers because it offers both rich data that cover the entire product lifecycle for many new products and because it provides many unsolved “puzzles.” Although the amount of scholarly research in this area is rapidly growing, its impact on practice has not been as significant as in other industries (e.g., consumer packaged goods). In this article, we discuss critical practical issues for the motion picture industry, review existing knowledge on those issues, and outline promising research directions. Our review is organized around the three key stages in the value chain for theatrical motion pictures: production, distribution, and exhibition. Focusing on what we believe are critical managerial issues, we propose various conjectures—framed either as research challenges or specific research hypotheses—related to each stage in the value chain and often involved in understanding consumer movie-going behavior

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

The Impact Challenge

This book explores the role of businesses in delivering positive societal and financial outcomes as they seek to bridge the gap between short-term organizational behaviors and long-range sustainability commitments. By addressing the inevitable data challenges associated with the strategic integration of a sustainability mindset, it enables faster adoption of social, environmental and governance metrics that generate lasting enterprise value. Inspired by the experience of practitioners that have successfully influenced the learning behaviors of complex organizations, this book helps readers drive systemic innovations as they leverage sustainability initiatives in a programmatic and intentional manner. Features: Defines a toolkit to generate sustainable business value by focusing on the organizational design underpinning sustainability-oriented initiatives. Provides a multidisciplinary lens on shaping the impact dialogue through applied frameworks. Discusses the need to analytically identify an organizational learning curve before developing impact targets and framing sustainability commitments around them. Combines theory and practice in a practical style by presenting a variety of real-life applications at a global level

Additive Manufacturing: A Summary of the Literature

The Center for Economic Development at the Levin College of Urban Affairs at Cleveland State University prepared this report for the Ohio Manufacturing Institute (OMI) at The Ohio State University. The objective of this study is to provide background analysis of additive manufacturing (AM) for the OMI as they prepare a roadmap for the future and recommendations on AM for the Ohio Development Services Agency (ODSA).1 This report is a literature review and summary of findings. Literature on AM was collected from various sources. Academic articles, reports, and studies were collated and analyzed from databases, internet searches, and publications. The goal of this report is to provide a clear context of the state, national, and international conversation on AM, as well as delineate opportunities and challenges as it relates to this technology. It is important to note two major considerations in this literature review: the designation between AM and 3D printing, and overall technical specifications: (1) AM is known in the mainstream media as “3D printing,” but in actuality, this designation is a subset of the AM concept. On occasion, this report will single out 3D printing technology as a subset of AM. There is a vast amount of material on 3D printing because it is currently a popular subject for the media. At times, the literature refers to 3D printing separately from AM, and it is unknown to the authors of this report whether these different designations are deliberate. To avoid confusion, we use both concepts and report the labeling as the literature refers to it. This provides clarity for the reader. (2) There is a significant amount of technical information in the AM literature that is not covered in this report. This report is designed only to consider AM in the context of the overall conversation. No technical information, mathematics, or other technical concepts involved in the AM conversation will be covered

Developing strong social enterprises : a documentary approach

Social enterprises are diverse in their mission, business structures and industry orientations. Like all businesses, social enterprises face a range of strategic and operational challenges and utilize a range of strategies to access resources in support of their venture. This exploratory study examined the strategic management issues faced by Australian social enterprises and the ways in which they respond to these. The research was based on a comprehensive literature review and semi-structured interviews with 11 representatives of eight social enterprises based in Victoria and Queensland. The sample included mature social enterprises and those within two years of start-up. In addition to the research report, the outputs of the project include a series of six short documentaries, which are available on YouTube at http://www.youtube.com/user/SocialEnterpriseQUT#p/u. The research reported on here suggests that social enterprises are sophisticated in utilizing processes of network bricolage (Baker et al. 2003) to mobilize resources in support of their goals. Access to network resources can be both enabling and constraining as social enterprises mature. In terms of the use of formal business planning strategies, all participating social enterprises had utilized these either at the outset or the point of maturation of their business operations. These planning activities were used to support internal operations, to provide a mechanism for managing collective entrepreneurship, and to communicate to external stakeholders about the legitimacy and performance of the social enterprises. Further research is required to assess the impacts of such planning activities, and the ways in which they are used over time. Business structures and governance arrangements varied amongst participating enterprises according to: mission and values; capital needs; and the experiences and culture of founding organizations and individuals. In different ways, participants indicated that business structures and governance arrangements are important ways of conferring legitimacy on social enterprise, by signifying responsible business practice and strong social purpose to both external and internal stakeholders. Almost all participants in the study described ongoing tensions in balancing social purpose and business objectives. It is not clear, however, whether these tensions were problematic (in the sense of eroding mission or business opportunities) or productive (in the sense of strengthening mission and business practices through iterative processes of reflection and action). Longitudinal research on the ways in which social enterprises negotiate mission fulfillment and business sustainability would enhance our knowledge in this area. Finally, despite growing emphasis on measuring social impact amongst institutions, including governments and philanthropy, that influence the operating environment of social enterprise, relatively little priority was placed on this activity. The participants in our study noted the complexities of effectively measuring social impact, as well as the operational difficulties of undertaking such measurement within the day to day realities of running small to medium businesses. It is clear that impact measurement remains a vexed issue for a number of our respondents. This study suggests that both the value and practicality of social impact measurement require further debate and critically informed evidence, if impact measurement is to benefit social enterprises and the communities they serve

How to Create an Innovation Accelerator

Too many policy failures are fundamentally failures of knowledge. This has become particularly apparent during the recent financial and economic crisis, which is questioning the validity of mainstream scholarly paradigms. We propose to pursue a multi-disciplinary approach and to establish new institutional settings which remove or reduce obstacles impeding efficient knowledge creation. We provided suggestions on (i) how to modernize and improve the academic publication system, and (ii) how to support scientific coordination, communication, and co-creation in large-scale multi-disciplinary projects. Both constitute important elements of what we envision to be a novel ICT infrastructure called "Innovation Accelerator" or "Knowledge Accelerator".Comment: 32 pages, Visioneer White Paper, see http://www.visioneer.ethz.c