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This study aims to devise generalized unconstrained optimization models for ascertaining the optimal level of product quality and production capacity level by modeling both product price and production cost as a function of product quality. Further, interrelations among investment for quality, product quality and production volume are considered. This study contributes toward the extant research, in that nuances related to price, production volume, and product quality are fused together such that two broad operational strategies of product quality optimization and production capacity optimization can be contrasted.

To achieve the research objectives, the authors evolve unconstrained optimization models such that optimal product quality level and optimal production capacity level can be obtained employing the principles of differential calculus aimed at maximizing the manufacturer's profit. Specifically, nuances related to quality technology and efficiency, and quality loss cost has also been integrated in the integrated model. Thereafter, employing numerical analysis for a generalized product, the detailed workings of evolved models are demonstrated. The authors further carry out the sensitivity analysis to understand the impact of investment for quality onto the manufacturer's profit for both operational strategies.

The research demonstrates that the manufacturer would be better off adopting production capacity optimization strategy as an operational policy, as opposed to product quality optimization policy for the manufacturer's profit maximization. Further, considering the two operational strategies, the manufacturer does not obtain the highest possible theoretical profit when pertinent variables (product quality and production capacity) are set at highest possible theoretical level. This research discusses that in low-volume and high-margin products, it might be useful to adopt a product quality optimization strategy as a production capacity optimization strategy results in significantly high quality loss cost.

The findings of our study have a significant implication for industries such as steel-making, cement production, automotive industry wherein the conventional wisdom dictates that higher level of production capacity utilization always results in higher level of revenues. However, the authors deduce that beyond certain production capacity utilization, striving for higher utilization does not fetch additional profit. This work also adds to the extant research literature, in that it integrates the nuances of product quality, production volume and pricing in an integrative manner.

Process flexibility (PF) is seen as a hedging instrument against demand uncertainty. This paper aims to examine capacity decisions for both flexible and dedicated processes under production policies such as make-to-order and make-to-stock. The study identifies some relative benefits, in terms of expected profit, of the process flexible plant over the dedicated ones. Furthermore, the advantage appears to be contingent upon the decision on the preset service level.

Using the sample-based optimization procedure, a detailed computational analysis is undertaken to identify the conditions under which a flexible plant is preferred over a dedicated plant. A combination of genetic algorithm and sample-based optimization procedure is used to capture the effects of preset service level. The factors controlled in this paper include the demand variance, demand correlation, capacity investment cost and the product price.

According to this study, in a dedicated process changing to a flexible process is not justified for the same level of demand correlation even with high demand variance. In fact, a strict control on the preset service level prefers the dedicated strategy. The advantage of a flexible plant increases as the demand correlation decreases, product price decreases, price asymmetry increases or capacity investment cost increases. With a preset service level constraint, a flexible process should be preferred to a dedicated one only when the capacity investment cost is high or the products have low contribution margins.

The PF index is introduced in this paper to measure the benefit of a flexible plant over a group of dedicated plants. The benefits were found to be contingent upon the decision on the required service level.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This paper aims to identify all variables and parameters related to business and emission within the petrochemical industry. The variables and parameters specified will be modeled into a system dynamic model that will be a baseline for the proposed best scenario(s) to address the business issue related to emission reduction in the petrochemical industry.

Literature review and stakeholder interviews were conducted to define the key factors contributing to the emission reduction of the petrochemical industry. The key factors are then developed into a system dynamic model to measure the quantitative impact of changes in those variables on emission and industry profitability.

This paper provides an analysis of system dynamic model. It suggests that process optimization can lead to a slight amount reduction in emissions. In contrast, a significant reduction shows in the simulation result of bio-based feedstock utilization and implementation of advanced technology. To sustain the emission reduction, strong commitment from stakeholders and support from the government will play an important role.

This research is limited to problem analysis of the primary product (high-value chemical) of the petrochemical industry by only considering the changes in the key factors of emission reduction.

This paper includes implications for interventions that can be imposed to reduce emission while retaining the business profitability.

The contribution of this study is to find the best scenario that can boost emission reduction within Indonesia’s petrochemical industry.

In this paper, the organism model for knowledge‐based enterprise is proposed. A dynamic capacity grey set is defined and analyzed based on the definition of the growth and development for knowledge‐based enterprise organism. The structure of the capacity whiten core, a subset of the capacity grey set, is optimized for different periods of the organism's life cycle. The organism grey topological structure model of knowledge‐based enterprise is described to possess the essential capacity grey set.

This study aims to describe the fundamentals of teaching risk management in a classroom setting, with an emphasis on the learning interface between higher education and the workplace environment for business management students.

The study reviews literature that uses spreadsheets to visualize and model risk and uncertainty. Using six distinct case-based activities (CBAs), the study illustrates the practical applications of software like Palisade @RISK in risk management education. It helps to close the gap between theory and practice. The software assists in estimating the likelihood of a risk event and the impact or repercussions it will have if it occurs. This technique of risk analysis makes it possible to identify the risks that need the most active control.

@RISK can be used to create models that produce results to demonstrate every potential scenario outcome. When faced with a choice or analysis that involves uncertainty, @RISK can be utilized to enhance the perspective of what the future might contain.

The insights from this study can be used to develop critical thinking, independent thinking, problem-solving and other important skills in learners. Further, educators can apply Bloom’s taxonomy and the problem-solving taxonomy to help students make informed decisions in risky situations.

The internet of things (IoT) is one of the pillars of Industry 4.0. Prior OM research has conceptualized IoT, and analyzed potential applications and risks and challenges associated with its adoption. However, little empirical evidence exists on the main types of IoT projects undertaken by organizations and on their impacts. The purpose of this paper is to close this gap by searching for a taxonomy of IoT projects that may be associated to different IoT readiness levels. The dynamic capability lens is used as the theoretical background for the analysis.

A database of secondary IoT case studies is used to identify an IoT project taxonomy through two-step cluster analysis. The taxonomy obtained allows classifying projects into homogenous groups by technological novelty, IoT capabilities and functional areas of application. ANOVA is then used to test for the association between cluster membership and alternative operational impacts. Finally, the analysis of selected case studies from the database allows throwing light on the nature of the projects typical of each cluster.

Five clusters of projects have been identified and positioned along varying degrees of capabilities, novelty and scope. The taxonomy is consistent with a three layer IoT technological readiness model. In turn, the three IoT readiness levels correspond to three managerial capabilities: monitoring, control and optimization. Combining cluster results with detailed case analysis suggests that IoT technological readiness can be interpreted as a dynamic capability enabling knowledge creation that can support competitive advantage.

This is a first attempt to describe projects firms undertake when adopting IoT. Building on cluster analysis, the study suggests that different IoT readiness levels are needed to reach different impacts.

Airport capacity constraints lead to operational congestion and delays, which have become major threats to the aviation industry. They impose large costs on airlines and their passengers. Uncertainty in demand or unexpected events can cause a mismatch between capacity and demand, resulting in either capacity oversupply, with a decrease in efficiency, or airport congestion over an extended period. Moreover, airport capacity is rather difficult to define due to its multifaceted and dynamic nature, and it depends both on the available infrastructure and on operating procedures. Additionally, traditional capacity management methods do not consider relevant behavioral economic challenges to conventional analysis, particularly failure of the expected utility hypotheses and dependence of valuations on reference points. This study aims to develop a preliminary framework to include economic concepts when evaluating expansions of airport capacity.

This paper reviews major opportunities in airport demand and capacity management from an economic perspective while appraising the challenges involved in airport capacity expansion processes that have not been fully completely in past studies. Although welfare economics provides the conceptual foundations for demand/capacity analyses, the authors integrate the findings regarding capacity definition, uncertainty management and behavioral economics into standard economics to guide the measurement of the airport capacity expansion problem.

The authors obtain several insights regarding airport capacity and demand management. First, airport capacity is a complex metric when evaluating airport expansion, and it depends both on the available infrastructure and on operating procedures. Furthermore, airport throughput is highly conditioned by factors that shape capacity and delay and shows significant variability when these factors are modified. Second, a marginal change in capacity at congested airports may have a great impact on demand distribution, airline competition, aircraft types, fares, operating revenues, route map and other characteristics of a given airport. Behavior after capacity expansion is highly reliant on the slot allocation models. Additionally, overall social welfare is usually affected after changes in infrastructure in terms of increased connectivity, economic benefits and negative externalities, including noise and local pollution. Third, on-time performance is clearly nonlinear, and thus sensitive to variations in demand and capacity. Finally, airport capacity and demand management involve a trade-off between mitigating congestion and maximizing capacity utilization, so decision-making tools are required to support and enhance policy and managerial choices. Three main challenges arise when developing new methods for evaluating airport expansions: the definition of capacity, the management of uncertainty in demand and the need to consider economic concepts.

This paper explores and produces an in-depth understanding of the problem of airport capacity and demand balance. The authors propose a preliminary framework that considers the challenges that have been previously identified and that, particularly, provides an economic perspective for airport capacity expansion processes. This framework is completed with a theoretical model to help policymakers and airport operators when faced with a capacity development decision.

Smart Manufacturing (SM) lies at the core of Industry 4.0. Operations management research has investigated the determinants of SM advancement but there is still limited understanding of the linkages between SM and organizational factors and about whether both the technological and organizational subsystems for SM are guided by firms’ competitive priorities. To close these gaps, building on operations strategy theory, this paper aims to empirically test a model positing that competitive priorities drive SM advancement. The relation between competitive priorities and SM advancement is assumed to be mediated by organizational microfoundations.

Using data from a single respondent survey with 234 firms in the automotive component industry, structural equation modeling is adopted to test the model hypotheses. Relevant constructs are measured with reference to the lead plant for SM.

Findings highlight that SM advancement is driven by the need to simultaneously compete in terms of cost, quality and delivery, thus suggesting that manufacturers view SM as a mean to develop multiple manufacturing capabilities. Organizational microfoundations fully mediate the relation between competitive priorities and SM advancement.

Results have implications for SM research, as they provide an understanding of the strategic priorities of firms engaging in SM. Findings also bear relevance for manufacturing executives engaged in the SM transformation, as they provide quantitative evidence that shaping an adequate organizational environment is a prerequisite for SM advancement.

While the adversarial nature of precast concrete (PC) building construction is frequently cited in the PC building construction press, only a few researchers have investigated construction supply chain management within the construction industry. Due to the interdisciplinary transportation environment, which inevitably results in disruption, the uses of construction supply chain and recovery from construction supply chain risk must be a subject of real interest, yet transportation management research in this area is scarce.

The purpose of this study is to discuss the weakness in system approaches and their application for managing precast concrete building in the context of construction supply chain practice and how to overcome it. As a precursor to this paper, the paper reviews current construction supply chain management occurrence on PC building construction and explores the hybrid intelligent vehicle tools and techniques currently being used on such management. This paper also presents the new hybrid intelligent vehicle-based approach to manage construction supply chain risk and reduce associated tension on PC building construction schemes.

The findings reveal the need for more sophisticated construction supply chain management solutions which accord with the needs of PC building construction schemes.

The paper concludes by presenting a research framework for developing such a system in the future.