Search

Industrial relocation (IR) is a business strategy consisting of moving operations locations. The purpose of this paper is to present how to assess, with multi-attribute decision-making (MADM), alternatives for IR. With MADM, IR strategies can be assessed not only based on a single attribute, as costs, or profits. This paper presents the application of MADM in a real case of IR. Four leading methods of MADM were applied: analytic hierarchy process (AHP), multi-attribute utility theory (MAUT), multi-attribute value theory (MAVT), and technique of order preference by similarity to ideal solution (TOPSIS). Results of AHP, MAUT, MAVT, and TOPSIS were quite similar, indicating the decision for the company not to relocate. A joint comparison of results with compatibility indices and correlation coefficients is the major novelty presented by this paper to the field of Operations Research, known as MADM. [ABSTRACT FROM AUTHOR]

The paper investigates the importance of banks' business classification in shaping the risk profile of financial institutions on a global scale. We employ a rare-event logit model based on a state-of-the-art list of major global distress events from the global financial crisis. When clustering banks by their business strategies using a community detection approach, we show that (i) capital enhanced resilience only for traditional banks that were on average less capitalized than other banks; (ii) boosting ROE, usually associated with riskier exposures, improved resilience for stable funded and asset diversified banks; (iii) conversely, higher levels of ROA exacerbated banks' vulnerability when associated with concentrated (not-diversified) investment structures; (iv) size in terms of total assets contributed to instability only for wholesale-funded institutions due to their high levels of unstable funding. Liquidity, on the contrary, reduced the institution likelihood of being in distress, regardless of its business classification. Although our findings refer to the recent financial crisis, they provide evidence that a tailored risk monitoring based on a proper peer group identification can facilitate banks' distresses prediction. [ABSTRACT FROM AUTHOR]

Supply uncertainty has been elevated to a strategic level concern in driving supply chain success. This paper considers that a firm faces a deterministic demand and procures from two unreliable suppliers: one is subject to a random disruption and might deliver all or nothing of the firm's order, while the other one exposes to a random yield risk for the firm's order. We explore the models of no recourse and ordering with recourse, and derive optimal solutions for each model. We provide the conditions under which single or dual sourcing strategy should be used. We also compare the decisions and profits among different models through computational experiments and find that it is more beneficial to order from the relatively expensive supplier as a backup source. [ABSTRACT FROM AUTHOR]

Conventional wisdom views that in an assembly system, suppliers have to accept a unified payment contract when transacting with a manufacturer. However, in practice, the implementation of a payment contract is clearly more dependent on the channel power of the supplier in the supply chain. This paper considers an assembly system consisting of two suppliers with different channel powers, and identifies three payment contracts, namely, on-delivery payment, on-agreed-time payment, and ready-to-assemble payment. We investigate the equilibrium delivery and timing decisions of firms under three different cases distinguished by the combinations of different payment contracts in the system. Based on both theoretical and quantitative analyses, three major results are obtained. First, the delivery times of suppliers are cost-driven and time-related. Second, the buffer time of the manufacturer can balance the production lead times of the supplier. Third, the supply chain achieves the lowest cost when the core supplier chooses an on-agreed-time payment contract, and the general supplier adopts a ready-to-assemble payment contract. [ABSTRACT FROM AUTHOR]

Project portfolios are considered "powerful strategic weapons" for implementing corporate strategy. Projects are exposed to different types of risks. Studies on project portfolio optimization have addressed risks either by maximizing the expected net present value or including constraints that place an upper bound on portfolio risk score. However, no study has attempted to minimize the risk of severe low returns by adopting a risk-averse measure. The present study contributes by addressing this research gap and utilizes a risk measure conditional value at risk (CVaR) for decision making. The present paper considers a case study of a dairy firm. It captures financial risk in the form of uncertain project cash inflows and evaluates strategic alignment scores and risk scores for technical, schedule, economic and political, organizational, and statutory clearance risks of projects using an analytical hierarchy process. Further, it formulates three project portfolio selection and scheduling models namely, risk-neutral (max_E), risk-averse (max_CVaR) and combined compromise (max_E_CVaR) models. A comparison of results shows that the max_CVaR model ensures that the lowest return in the worst scenario is maximized to the greatest extent possible, thereby yielding high returns even when the confidence levels are low. The model exploits the diversification approach for risk management and its portfolios contain at least one project from each project category (derivative, platform and breakthrough). The results obtained using max_E_CVaR model can be utilized by decision makers to select and schedule project portfolios according to their risk appetite and acceptable trade-off between risk-averse and risk-neutral objectives. [ABSTRACT FROM AUTHOR]

Supply chains (SCs) can be managed at many levels. The use of tactical SC planning models with multiple flexibility options can help manage the usual operations efficiently and effectively, whilst improve the SC resiliency in response to inherent environmental uncertainties. This paper defines tactical SC flexibility and identifies tactical flexibility measures and options for development of flexible SC planning models. A classification of the existing literature of SC planning is introduced that highlights the characteristics of published flexibility inclusive models. Additional classifications from the reviewed literature are presented based on the integration of flexibility options used, solution methods utilized, and real world applications presented. These classifications are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field. [ABSTRACT FROM AUTHOR]

Lean as a business strategy is used to improve quality and service, eliminate waste, reduce time and costs, and enhance overall organizational effectiveness. Heightening challenges in competition in recent years have prompted many small and medium-sized enterprises (SMEs) to adopt lean to enhance firms' competitiveness. This paper attempts to present an all-inclusive study and it examines various factors associated with the implementation of lean in SMEs in the U.S. The findings suggest that most of SMEs have a relatively accurate understanding of lean concept and philosophy. The primary reasons to implement lean are mainly internal, including reduce cost, improve profit margin, improve utilization of plant/facility, and maintain competitive position. A hierarchical cluster analysis was conducted to investigate lean status. It was discovered that both advanced adopters and beginners of lean are discovered. ANOVA test results show that there exist quite significant differences in terms of the degrees of lean implementation in SMEs. Varied lean tools and programs have been applied and they are positively related with firms' performance. Lastly, the paper provides evidences that major lean barriers are encountered by SMEs regarding management or people related factors as well as key knowledge and know-how. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to formally describe new optimization models for telecommunication networks with distributed processing. Modem distributed networks put more focus on the processing of information and less on the actual transportation of data than we are traditionally used to in telecommunications. This paper introduces new approaches for modelling decision support at operational, tactical and strategic levels. One of the main advantages of the technological framework we are working within is its inherent flexibility, which enables us to dynamically plan and consider uncertainty when decisions are made. When we present the models, emphasis is placed on the modelling discussions around the shift of focus towards processing, the new technological aspects, and how to utilize flexibility to cope with uncertainty. [ABSTRACT FROM AUTHOR]

This paper concerns the multi-period newsvendor problem. In this problem, the decision maker has to decide the order quantity of an item in the subsequent period in which the demand is usually unknown. No statistical assumptions are made about the unknown demand. We adopt an online learning method from the field of prediction with expert advice to study the non-stationary newsvendor problem. We propose newsvendor strategies for both real-valued and integer order quantities. Taking the non-stationary strategies that can switch between different order quantities as benchmark, we prove that our proposed strategies can guarantee that the newsvendor's cumulative gains are almost as large as those of the best switching strategies with not too many switches. Simple computational experiments are further performed to illustrate the effectiveness of our strategies. [ABSTRACT FROM AUTHOR]

Scheduling algorithms and their role in supply chain planning are topics that have been discussed in scheduling literature for many years. Based on examples and experience with commercial supply chain planning software, this paper presents background information about production planning and scheduling functionality in commercial supply chain planning software and interesting scheduling coordination problems in supply chain planning for researchers. We first provide an overview of different planning activities in supply chain planning, while taking into consideration existing functionalities that are available in commercial supply chain planning software. As a second step, we show three scheduling coordination problems in supply chain planning, namely the integration of production planning and production scheduling, the integration of sales order confirmation and production scheduling and the integration of VMI planning and production scheduling. We conclude this paper with a detailed discussion of an implementation of a supply chain planning solution at the tissue producer SCA Hygiene in Sweden. [ABSTRACT FROM AUTHOR]

In many industries the pricing of a product over time can be used to manage demand for the product. Lead time, or promised delivery time is often a significant factor in price negotiations. However, the production planning literature has largely treated pricing decisions as exogeneous while focusing on the allocation of production capacity between products over time. On the other hand, the marketing literature has generally ignored the effects of capacity constraints and focused on the effects of pricing. In this paper, we begin by reviewing the existing literature on integrative production-marketing research, focusing on those models that consider lead times and capacity. We suggest a number of directions for future research that take advantage of recent developments in production planning models, as well as explicit modeling of feedback loops governing key parameters, which suggest a broader view of the problem. [ABSTRACT FROM AUTHOR]

Pricing for new products is usually a difficult task for a firm, for there always exists uncertainty of consumers' valuation with respect to the new products. Moreover, the presence of strategic consumers even complicates the situation, due to their inter-temporal purchase choice behavior and their uncertain proportion in the whole demand pool. In this paper, facing the twofold uncertainty, we develop a stylized model to study the optimal pricing for new fashion products in the presence of strategic consumers. The optimal pricing strategy for the firm and the optimal purchase timing for strategic consumers are obtained; a framework is also built to investigate the expected value of demand information. Through numerical studies, we find that the price skimming strategy dominates the penetration strategy only when the firm's discount factor is large enough, consumers' strategic purchasing behavior diminishes the firm's ability to adopt skim pricing, and the revealing strategy is most valuable when the firm is (almost) indifferent between skimming and penetration. In addition, some other managerial insights are also derived. [ABSTRACT FROM AUTHOR]

As we have argued in previous papers, multi-level decision problems can often be modeled as multi-stage stochastic programs, and hierarchical planning systems designed for their solution, when viewed as stochastic programming heuristics, can be subjected to analytical performance evaluation. The present paper gives a general formulation of such stochastic programs and provides a framework for the design and analysis of heuristics for their solution. The various ways to measure the performance of such heuristics are reviewed, and some relations between these measures are derived. Our concepts are illustrated on a simple two-level planning problem of a general nature and on a more complicated two-level scheduling problem. [ABSTRACT FROM AUTHOR]

In project planning and control, it is of vital importance to take into account that different categories of resources have different work patterns. Algorithms for time analysis, resource smoothing and resource scheduling are almost always presented as if this has no importance. Practical planning tools, however, have different work patterns as a natural part of the data. In this paper, we discuss modelling aspects of introducing work patterns. [ABSTRACT FROM AUTHOR]

In this paper, we develop a theoretical framework for the common business practice of rolling horizon decision making. The main idea of our approach is that the usefulness of rolling horizon methods is, to a great extent, implied by the fact that forecasting the future is a costly activity. We, therefore, consider a general, discrete-time, stochastic dynamic optimization problem in which the decision maker has the possibility to obtain information on the uncertain future at given cost. For this non-standard optimization problem with optimal stopping decisions, we develop a dynamic programming formulation. We treat both finite and infinite horizon cases. We also provide a careful interpretation of the dynamic programming equations and illustrate our results by a simple numerical example. Various generalizations are shown to be captured by straightforward modifications of our model. [ABSTRACT FROM AUTHOR]

Supply chain flexibility is widely recognized as an approach to manage uncertainty. Uncertainty in the supply chain may arise from a number of sources such as demand and supply interruptions and lead time variability. A tactical supply chain planning model with multiple flexibility options incorporated in sourcing, manufacturing and logistics functions can be used for the analysis of flexibility adjustment in an existing supply chain. This paper develops such a tactical supply chain planning model incorporating a realistic range of flexibility options. A novel solution method is designed to solve the developed mixed integer nonlinear programming model. The utility of the proposed model and solution method is evaluated using data from an empirical case study. Analysis of the numerical results in different flexibility adjustment scenarios provides various managerial insights and practical implications. [ABSTRACT FROM AUTHOR]

In a given project network, execution of each activity in normal duration requires utilization of certain resources. If faster execution of an activity is desired then additional resources at extra cost would be required. Given a project network, the cost structure for each activity and a planning horizon, the project compression problem is concerned with the determination of optimal schedule (duration) of performing each activity while satisfying given restrictions and minimizing the total cost of project execution. This paper considers the project compression problem with time dependent cost structure for each activity. The planning horizon is divided into several regular time intervals over which the cost structure of an activity may vary. But the cost structure of the activities remains the same (constant) within a time interval. Key events of the project attract penalty for finishing earlier or later than the corresponding target times. The objective is to find an optimal project schedule minimizing the total project cost. We present a mathematical model for this problem, develop some heuristics and an exact branch and bound algorithm. Using simulated problems we provide an insight into the computational performances of heuristics and the branch and bound algorithm. [ABSTRACT FROM AUTHOR]

Recommendation systems help companies construct online personalization strategies for customers who are often overwhelmed by the abundance of product choices available. To extend existing operations research literature on recommendation systems, this article proposes a decision analytic framework for interpretable recommendation systems with multiple input data sources for e-commerce settings. The impact of multiple data sources on recommendation performance is investigated and two hybridization data fusion strategies, i.e., a posteriori weighting and input data source combination using factorization machines are benchmarked. Furthermore, a new importance score mechanism is introduced to provide insight into the input data sources' and underlying variables' impact on recommendation performance. The framework is empirically validated on 164,338 customers and 51,367 products across eight real-life data sets with four input data sources (product, customer, raw behavioral, and aggregated behavioral data) obtained from a large European e-commerce company. With this new decision analytic framework, e-commerce companies are able to open their recommendation system's black box, to identify the most predictive input data sources and the best hybridization strategy for their business context. [ABSTRACT FROM AUTHOR]

Because heterogenous and unknown shareholder utility functions make it difficult to define a corporate objective common to all shareholders based on utility, the traditional theory of the firm concentrates on wealth maximization as the main measure of performance. Using the concept of ranked marginal utility, we develop a multi-dimensional measure of firm performance (TPM) that reflects the preferences of all risk averse shareholders towards all aspects of risk. We verify empirically that this is, in fact, the case for the first four moments of a large sample of US stocks over the period 2002–2010. Then, using the manager/shareholder agency conflict as the analytical framework, we show that TPM is a reliable, multi-dimensional performance measure and that one dimensional performance measures, such as mean returns, volatility or Tobin's Q can lead to erroneous inference. By including shareholder preferences towards risk in the measure of firm performance as the corporate objective, we bring together the corporate finance literature and the literature on portfolio investment theory and practice. [ABSTRACT FROM AUTHOR]

We study the carrier-shipper contracting issue in a single time period (i.e., 1 year) consisting of a low demand season followed by a high demand season under asymmetric information arising from the ocean freight industry. In order to address the contract default problem that is prominent in this industry, we propose a proportion requirement policy in which the capacity allocated to the shipper at the contract price in the high demand season is proportional to his quantity commitment in the low demand season. The shipper has private information about the low season demand while the carrier does not. To induce truthful information revelation, the carrier offers a bundle consisting of two two-part tariff contracts, each of which targets for one market state. In our model, the carrier determines the contract bundle to maximize her expected profit in both seasons. The shipper decides which contract to sign after observing the market state, and then determines the optimal shipping quantity to minimize his total expected cost. We characterize the carriers optimal contract bundle under asymmetric information and the shippers optimal strategy for each market state. The analytical results show that both the carrier and the shipper can be better off with an appropriately designed contract bundle compared with replying solely on the spot market under certain conditions. We also investigate how the outcomes of the system (e.g., the shippers optimal commitment quantity, the optimal contract price, the information rent and the carriers expected profit) are affected by the proportion requirement parameter, the degree of information asymmetry, and the uncertainty of the market state type. [ABSTRACT FROM AUTHOR]

This study addresses stock selection modeling and portfolio construction and implementation in global and U.S. markets in the context of multi-objectives optimization framework. We will show how forecasted earnings acceleration factors can enhance returns in global and U.S. stock markets. We construct Markowitz portfolios for Global and U.S. domestic Markets that offer superior returns-to-risk ratios, relative to domestic portfolios. We show how stock repurchases and corporate exports can be estimated and implemented as the third objective to generate statistically significant excess returns in global and U.S. stock markets. It is particularly interesting to note the conflicting corporate strategies’ impacts on stockholder wealth. [ABSTRACT FROM AUTHOR]

Fast fashion is a timely, influential and well observed business strategy in the fashion retail industry. An effective fast fashion supply chain relies on quick and competent forecasts of highly volatile demand that involves multiple stock keeping units. However, there are multiple sources of uncertainty, such as market situation and rapid changes of the fashion trends, which makes demand forecasting more challenging. Therefore, it is crucial for the fast fashion companies to carefully select the right forecasting models to thrive and to succeed in this ever changing business environment. In this study, we first review a selected set of computational models which can be applied for fast fashion demand forecasting. We then perform a real sale data based computation analysis and discuss the strengths and weaknesses of these versatile models. Finally, we conduct a survey to learn about the perceived importance of different demand forecasting systems' features from the fashion industry. Finally, we rank the fast fashion demand forecasting systems using the AHP analysis and supplement with important insights on the preferences on the demand forecasting systems of different groups of fashion industry experts and supply chain practitioners. [ABSTRACT FROM AUTHOR]

This work presents an optimization model to support decisions in the aggregate production planning of sugar and ethanol milling companies. The mixed integer programming formulation proposed is based on industrial process selection and production lot-sizing models. The aim is to help the decision makers in selecting the industrial processes used to produce sugar, ethanol and molasses, as well as in determining the quantities of sugarcane crushed, the selection of sugarcane suppliers and sugarcane transport suppliers, and the final product inventory strategy. The planning horizon is the whole sugarcane harvesting season and decisions are taken on a discrete fraction of time. A case study was developed in a Brazilian mill and the results highlight the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Strategic planning, by nature, is concerned with the organization's interaction with its environment and is normative because it deals with organizational motives and self image. The salient problem confronting strategic planning is to reflect the abstract and implicit value system held by management within a disciplined and informed analytical methodology. The Gas Research Institute has developed a strategic planning process, centered upon the annual development of a projection of the U.S. energy/economic situation. The process, which requires collaboration of top management and analytical staff, brings current environmental data and sophisticated methodology to bear on the outlook for the gas industry and gas-related technologies, but it ensures that analytical judgements will be influenced and, if necessary, overriden by current management values. Contradictions between managament perceptions and objective analysis are raised and resolved in an explicit process. [ABSTRACT FROM AUTHOR]

In the hierarchical scheduling mode! to be considered, the decision at the aggregate level to acquire a number of identical machines has to be based on probabilistic information about the jobs that have to be scheduled on these machines at the detailed level The objective is to minimize the sum of the acquisition costs and the expected average completion time of the jobs. In contrast to previous models of this type, the second part of this objective function corresponds to a well solvable scheduling problem that can be solved to optimality by a simple priority rule. A heuristic method to solve the entire problem is described, for which strong asymptotic optimality results can be established. [ABSTRACT FROM AUTHOR]