A robustness framework for monitoring real options under uncertainty

This paper presents a problem structuring methodology to assess real option decisions in the face of unpredictability. Based on principles of robustness analysis and scenario planning, we demonstrate how decision-aiding can facilitate participation in projects setting and achieve effective decision making through the use of real options reasoning. We argue that robustness heuristics developed in earlier studies can be practical proxies for real options performance, hence indicators of efficient flexible planning. The developed framework also highlights how to integrate real options solutions in firms’ strategic plans and operating actions. The use of the methodology in a location decision application is provided for illustration.     

Performance measurement of supply chain management using the analytical hierarchy process

Performance measurement of supply chain management (SCM) is a rapidly growing multi-criteria decision-making problem owing to the large number of factors affecting decision-making. The right choice of performance metrics and measures is critical to the success and competitiveness of the firms in the era of globalisation. Recognising the multiple objective nature of the problem, this paper proposes the use of the analytical hierarchy process (AHP) methodology as aid in making SCM evaluation decisions. For pair-wise comparison in AHP, a survey methodology is used. The methodology presented can help firms to prioritise and formulate viable performance measurement strategies in the volatile and complex global decision environment from different balanced scorecard (BSC) perspectives. A demonstration of the application of this methodology in a real life problem is presented.     

AN EVALUATION OF ORDER PICKING POLICIES FOR MAIL ORDER COMPANIES

This paper is concerned with the problem of order picking in mail order companies. Order picking is the retrieval of items from their warehouse storage locations to satisfy customer orders. Five order picking policies, strict order, batch, sequential zone, batch zone, and wave, are evaluated using labor requirements, processing time, and customer service as performance measures. A simulation model was developed to investigate these picking policies in a mail order environment. Prior research has focused on the study of individual picking policies. This study extends the prior research by evaluating multiple picking policies under varying operating conditions. The results of the study seem to indicate that (1) wave picking and batch picking perform well across the range of operating conditions considered in this study, and (2) sequential zone and batch zone picking do not perform well, especially as the order volume increases. However, the benefits and drawbacks to each picking policy must be taken into account. The key to effective implementation of an order picking system is to match the firm's business strategy, capabilities, technology, and space requirements with an order picking policy that maximizes the benefits of order picking to the firm and its customers.     

AN OPERATIONAL APPROACH TO THE EVALUATION OF LEASES BY HOSPITALS*

The evaluation of the lease versus purchase alternative by not-for-profit hospitals presents a problem that stems from the interaction between investment and financial decisions. This paper provides an analytical framework that resolves this issue by neutralizing the impact of the lease contract on the hospital's financial structure and debt capacity. The formulation incorporates the special operating characteristics of not-for-profit hospitals: namely, they are not subject to federal income tax, and part of their revenue is generated through cost-based reimbursement.     

Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies

Abstract

In this contribution, we aim to prioritise the indicators to enhance the organisational supply chain’s (SC) effectiveness from an industrial perspective. It will help industries to develop strategies for managing the SC effectively and ensuring improvement in performance continuously. To achieve this, this work proposes to use a two-phase research methodology. First, 36 SC performance improvement indicators are recognised from a literature survey and from field and industrial expert’s inputs. Secondly, a structural model is proposed using the fuzzy analytical hierarchy process (AHP) to prioritise the indicators strategically to improve the SC performance. The fuzzy AHP method helps determine the priority of concerns of the identified indicators under fuzzy surroundings. Inputs in this research are taken from four ancillary Indian plastic manufacturing firms. Research findings indicate that collaborations and information exchange dimension indicators obtained the highest priority in improving SC performance. The model proposed is considered very useful for the SC managers/practitioners/decision-makers to understand better and distinguish the essential SC performance improvement indicators and to take systematic decisions specifically to improve the performance of business in a SC context. Sensitivity analysis was conducted to examine the priority ranking of the indicators.     

Study of the behaviour of flexible production systems using neural nets

Owing to high development and acquisition costs, production systems require very detailed analysis for pre-design specifications, and an adjustment in operating philosophies. Detailed simulation models are one of the most important instruments to achieve this goal; normally the simulator must be custom-built, based on an accurate study of the specific industrial problem. The proposed management and economical analysis uses mathematical meta-models obtained from experimental designs produced by the simulator; today the detail level and precision required is very high, so these kinds of meta-model operate very close to their limit. Here we present a new methodology based on artificial neural networks that produces new types of meta-models which can correctly represent complex industrial systems. In this work we propose an application of this methodology together with a detailed analysis of the model development phase to highlight the potential of this instrument.     

An application of the integrated AHP-PGP model for performance measurement of supply chain management

The performance measurement problem has gained great attention in business and operations management literature. The first objective of this study is to determine the required performance measures and to develop a model for performance evaluation, based on these selected measures using analytical hierarchy process (AHP) methodology. The second objective is to explain how an integrated AHP-PGP (pre-emptive goal programming) model can be used in performance measurement while optimising the overall performance. An integration of AHP and multi-objective PGP is used to consider both quantitative and qualitative performance measures in optimising the overall performance of the system. The integrated model is presented with a real-world application using source data provided by a survey conducted in India. Findings demonstrate that the integrated AHP-PGP model can be useful to all supply chain industries in their day-to-day performance measurement decisions.     

Logic-based Benders decomposition for an inventory-location problem with service constraints

We study an integrated inventory-location problem with service requirements faced by an aerospace company in designing its service parts logistics network. Customer demand is Poisson distributed and the service levels are time-based leading to highly non-linear, stochastic service constraints and a nonlinear, mixed-integer optimization problem. Unlike previous work in the literature, which propose approximations for the nonlinear constraints, we present an exact solution methodology using logic-based Benders decomposition. We decompose the problem to separate the location decisions in the master problem from the inventory decisions in the subproblem. We propose a new family of valid cuts and prove that the algorithm is guaranteed to converge to optimality. This is the first attempt to solve this type of problem exactly. Then, we present a new restrict-and-decompose scheme to further decompose the Benders master problem by part. We test on industry instances as well as random instances. Using the exact algorithm and restrict-and-decompose scheme we are able to solve industry instances with up to 60 parts within reasonable time, while the maximum number of parts attempted in the literature is 5.     

A guide for supply chain integration in SMEs

Supply chain integration (SCI) is a competitive business approach for enterprises that has not been implemented as widely as expected in small and medium-sized enterprises (SMEs). One of the factors explaining this low level of application is the lack of methodologies to develop SCI that fit the characteristics of this type of enterprise. In this article, we present a methodology that enables SMEs to achieve correct SCI in a manner that is simple, straightforward and oriented towards their particular characteristics. Thus, SMEs will be able to align and coordinate the aims, resources, decisions, methods, business processes and employees involved in the supply chain. The methodology addresses the problem of SMEs SCI and it was produced by applying the case study research method to 30 industrial SMEs. The methodology is organised in phases and the activities to be performed, the techniques and supporting tools to be used, as well as the expected outcomes are all described for each phase. With this methodology, it becomes possible to overcome the main barriers that prevent SMEs from achieving a correct integration of their supply chains. In addition to the methodology, this article also outlines the significant benefits observed in the 30 enterprises as a result of its application.     

Multilocation plant sizing and timing problem: a study of spatial—temporal decomposition procedure

An attempt has been made to develop a spatial-temporal decomposition procedure to solve the multilocation plant sizing and timing problem (MLPSTP). MLPSTP involves the determination of site, location, timing and utilization of the plants to meet the demands of geographically distributed customers. These decisions are assumed to interact through a common objective of minimizing the net present value (NPV) of capital costs and streams of operating and transportation costs. A solution procedure for MLPSTP is proposed using aggregation and disaggregation methodology. In the aggregation phase, MLPSTP is considered to be a single-period problem. This problem is decomposed over different geographical areas to obtain a set of feasible sites and projects. These projects are then sequenced over a finite planning horizon in the disaggregation phase. A multiple criteria based evaluation of spatial decomposition, temporal decomposition, and spatial-temporal decomposition is provided.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

Hedging strategic flexibility in the distribution optimization problem

Motivated by the increasing prevalence of flexibility hedging in corporate-level risk management programs, this paper focuses on the treatment of hedging operational risks in the coordinated replenishment and shipment for distribution systems. The forward option pricing model with the generalized autoregressive conditional heteroskedasticity (GARCH) model for stochastic demand forecasting is adopted for constructing inventory volume flexibility. We therefore propose a hedge-based coordinated inventory replenishment and shipment (HCIRS) methodology for flexibly making inventory hedging and optimal routing assignment decisions as well as coordinating replenishment and shipment policies. The HCIRS methodology provides insight into strategic flexibility adopted for a real-life inventory–distribution problem faced by one of the major East Asia food supply networks and turns out to be very efficient. The proposed HCIRS methodology has provided evidence of better results than the traditional operational techniques for the presented case in this paper.     

AN OPTIMIZATION-BASED METHODOLOGY FOR RELEASE SCHEDULING

Operational-level scheduling decisions on the factory floor include the release policy and the dispatching rule. Recent work by researchers suggests that factory performance depends heavily on the release policy. Several heuristic factory release rules have been developed that generate release decisions based on the status of the bottleneck machine. The heuristic rules perform very well in reducing the work-in-process inventory. However, factory output schedules generated by these rules generally fail to match the demand pattern, creating excessive finished goods inventory and/or excessive backorders. To overcome these difficulties, a computationally efficient integer programming model is proposed for release scheduling.     

AN APPLICATION OF INVENTORY THEORETICAL MODELS TO CASH COLLECTION

A problem common to all companies operating widely dispersed revenue-generating outlets is that of transferring these revenues to central accounts. Company funds held in local banks represent an unusable asset that, in view of the high opportunity cost of money, may substantially increase operating costs. A wide variety of systems can be devised to effect this transfer process. As an example of such systems, this paper examines a transfer procedure that uses depository transfer checks. It is shown that a simple (s, S) policy characterizes the optimal transfer policy.     

Six things to manage--Designs

Transportation in reverse logistics (RL) plays a significant role and has impact on overall RL performance. Efficiency, effectiveness and impact are the distinct components of RL transportation. Balancing these components requires the implementation of an effective performance measurement (PM) system. The perspectives of balanced scorecard have been used where enterprises can link their performance to their transportation practices, which do not provide an understanding of the RL transportation operating objectives and the developing strategies through an analysis of stakeholders. This article proposes the comprehensive PM of transportation system, which is multi-angle perspective for RL enterprises. Recognising the multiple criteria nature of the problem, this article uses analytical hierarchy process methodology in order to find an overall comprehensive performance index, which is critical to the success of the RL transportation operations to assist logistics managers, researchers, transportation planners and decision-makers.     

Robustness of linear models in dynamic multivariate predictions

Linear aggregation models employing unit and equal weights have been shown to be superior to human decisions in a surprising range of decision situations. In addition, decisions based on these models have often been found to be superior to those based on linear regression models (LRMs). This general issue was explored for repetitive decisions in production planning. The problem considered differs in several aspects from the types of problems investigated previously: (1) the problem is dynamic rather than static; (2) a set (or vector) of interactive decisions dependent on previous decisions is required to be made, where a decision in stage t, the dependent variable, becomes an independent variable in stage t + 1; and (3) the criterion function is cost with a quadratic loss function (rather than the correlation measure of R²). Moreover, since repetitive decisions were involved, the parameters of the models were estimated using past human decisions. These were used to predict specific values of the decision variables (rather than rank order), which in turn were employed recursively to predict values of the decision variables at subsequent stages. While decisions from equal weighting rules were found to be superior to human decisions and not greatly inferior to decisions from linear regression models, decisions from unit weighting rules performed poorly. The rationale for such performance is discussed, indicating that previous theoretical and empirical research on linear weighting models is not generally applicable to dynamic, multivariate interactive decisions problems with lagged variables.     

代工模式下的竞争优先权与企业绩效关系研究

根据对江苏省135家中小工业企业的调研,将代工模式下的竞争优先权划分为成本、质量、交货和柔性,企业绩效划分为财务绩效和非财务绩效,利用因子分析探索了竞争优先权与企业绩效之间的相关关系。结果表明,质量、交货和柔性对财务绩效和非财务绩效都有显著的正向影响,其中交货影响最大,但成本对企业绩效没有影响。这为中小工业企业选择竞争要素提供了决策依据。     

New directions for management accounting

Using a general systems rationale, this paper develops a theoretical structure for approaching the problem of management accounting. The management control problem is explicated in terms of maintaining a relationship between the enterprise's structure and its environment. An enterprise's structure is composed of three elements (and their inter-relations): a decision and control subsystem; a financial funds subsystem and an operating (physical transforms) subsystem. Portrayed in these terms, the problem of management accounting is shown to require a methodology which is able to take cognizance of economic, sociological, psychological and other aspects of the enterprise system. The model described here provides a general intellectual frame of reference for ordering the problem.     

CAPACITY AND PRODUCTION DECISIONS IN STOCHASTIC MANUFACTURING SYSTEMS: AN ASYMPTOTIC OPTIMAL HIERARCHICAL APPROACH

We present a new paradigm of hierarchical decision making in production planning and capacity expansion problems under uncertainty. We show that under reasonable assumptions, the strategic level management can base the capacity decision on aggregated information from the shopfloor, and the operational level management, given this decision, can derive a production plan for the system, without too large a loss in optimality when compared to simultaneous determination of optimal capacity and production decisions. The results are obtained via an asymptotic analysis of a manufacturing system with convex costs, constant demand, and with machines subject to random breakdown and repair. The decision variables are purchase time of a new machine at a given fixed cost and production plans before and after the purchase. The objective is to minimize the discounted costs of investment, production, inventories, and backlogs. If the rate of change in machine states such as up and down is assumed to be much larger than the rate of discounting costs, one obtains a simpler limiting problem in which the random capacity is replaced by its mean. We develop methods for constructing asymptotically optimal decisions for the original problem from the optimal decisions for the limiting problem. We obtain error estimates for these constructed decisions.     

Defender–Attacker Decision Tree Analysis to Combat Terrorism

We propose a methodology, called defender–attacker decision tree analysis, to evaluate defensive actions against terrorist attacks in a dynamic and hostile environment. Like most game‐theoretic formulations of this problem, we assume that the defenders act rationally by maximizing their expected utility or minimizing their expected costs. However, we do not assume that attackers maximize their expected utilities. Instead, we encode the defender's limited knowledge about the attacker's motivations and capabilities as a conditional probability distribution over the attacker's decisions. We apply this methodology to the problem of defending against possible terrorist attacks on commercial airplanes, using one of three weapons: infrared‐guided MANPADS (man‐portable air defense systems), laser‐guided MANPADS, or visually targeted RPGs (rocket propelled grenades). We also evaluate three countermeasures against these weapons: DIRCMs (directional infrared countermeasures), perimeter control around the airport, and hardening airplanes. The model includes deterrence effects, the effectiveness of the countermeasures, and the substitution of weapons and targets once a specific countermeasure is selected. It also includes a second stage of defensive decisions after an attack occurs. Key findings are: (1) due to the high cost of the countermeasures, not implementing countermeasures is the preferred defensive alternative for a large range of parameters; (2) if the probability of an attack and the associated consequences are large, a combination of DIRCMs and ground perimeter control are preferred over any single countermeasure.