A Warning Zone Method for the Multiperiod Cost Variance Investigation Problem

This paper develops a warning zone approach to make variance investigation decisions for a multiperiod process. The assumed cost generation process varies between an in-control and out-of-control state. These states cannot be directly observed, but must be inferred from the reported cost variances. Using the warning zone method of inference, the manager investigates the process whenever an upper threshold is exceeded or a lower threshold is exceeded for two consecutive periods. A four-state Markov chain models the resulting decision process. Steady state probabilities are derived for this chain and are used to obtain explicit formulas for the effectiveness and efficiency of the decision process. These formulas permit computation of the cost savings attainable by the warning zone method. Compared to other decision rules, the warning zone method is much simpler than the theoretically optimal Bayesian revision method, but uses more information than the Markovian control limit method. Numerical comparison of results shows that the warning zone method usually captures most of the available cost savings, even in cases where the Markovian control limit method does not perform well.     

Hierarchical model to design manufacturing systems applied to a metal-mechanic company

This paper describes the application of a model to design and manage the manufacturing process through an integrated system, using a Hierarchical methodology-by means of different aggregation and disaggregation stages with an appropriate time horizon. The hierarchical model obtained allows interfacing between the master schedule and the schedule of the orders. At each level of the hierarchical model some decisions have to be made in order to design the manufacturing system, but having a model that also takes into consideration the management of the system. To do this it is necessary to use aggregated information concerned with the level at which decisions are made. It is obtained from the database, which contains the information at the highest level of aggregation. This model has been applied to a metal mechanic company, that makes final products such as car washers, etc. The benefits of this approach for the company are addressed in this paper.     

On the fair optimization of cost and customer service level in a supply chain under disruption risks

This paper presents a new decision-making problem of a fair optimization with respect to the two equally important conflicting objective functions: cost and customer service level, in the presence of supply chain disruption risks. Given a set of customer orders for products, the decision maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers, and schedule the orders over the planning horizon, to equitably optimize expected cost and expected customer service level. The supplies of parts are subject to independent random local and regional disruptions. The fair decision-making aims at achieving the normalized expected cost and customer service level values as much close to each other as possible. The obtained combinatorial stochastic optimization problem is formulated as a stochastic mixed integer program with the ordered weighted averaging aggregation of the two conflicting objective functions. Numerical examples and computational results, in particular comparison with the weighted-sum aggregation of the two objective functions are presented and some managerial insights are reported. The findings indicate that for the minimum cost objective the cheapest supplier is usually selected, and for the maximum service level objective a subset of most reliable and most expensive suppliers is usually chosen, whereas the equitably efficient supply portfolio usually combines the most reliable and the cheapest suppliers. While the minimum cost objective function leads to the largest expected unfulfilled demand and the expected production schedule for the maximum service level follows the customer demand with the smallest expected unfulfilled demand, the equitably efficient solution ensures a reasonable value of expected unfulfilled demand.     

BAYESIAN MODELS FOR NEW PRODUCT PRICING

This paper develops an information decision system for new product pricing based on Bayesian updating of prior estimates of demand distribution parameter values and on optimization by dynamic programming. The model considers the interaction of production and pricing decisions and emphasizes the simultaneous making of both decisions. After presentation of the basic model, approximate techniques are introduced which obtain most of the benefit of the approach while requiring only a fraction of the computer cost and input data. Numerical examples using growing demand and price sensitivity are given to demonstrate the high computer cost of the first model and the relative performance (on a profit basis) of the approximate techniques.     

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.     

基于产品新鲜度要求的保鲜成本投资与数量集配策略联合决策研究

文章以提供生鲜产品运输配送服务的物流企业为研究对象,考虑生鲜产品质量腐蚀特性,应用随机更新理论建立基于下游零售商产品新鲜度水平要求的保鲜投资与物流集配联合决策随机模型。模型分析指出单位时间的运输成本、集配惩罚成本与保鲜投入成本随保鲜投资决策与物流集配决策变化的内在规律。数值分析指出保鲜投资、物流集配及其联合决策的有效性和产品、成本、需求环境等参数与这些环境参数同时变化对保鲜投资决策、集配决策与企业运营成本的影响。     

Multi-time scale Markov decision process approach to strategic network growth of reverse supply chains

This paper addresses a complex set of decisions that surround the growth over time of reverse supply chain networks that collect used products for reuse, refurbishment, and/or recycling by processors. The collection network growth problem is decomposed into strategic, tactical and operational problems. This paper focuses on the strategic problem which is to determine how to allocate capital budget resource effectively to grow the network to meet long term collection targets and collection cost constraints. We model the strategic problem as a Markov decision process which can also be posed as multi-time scale Markov decision problem. The recruitment problem in a tactical level appears as a sub-problem for the strategic model. Using dynamic programming, linear programming and Q-Learning approaches, an heuristic is implemented to solve realistically sized problems. A numerical study demonstrates that the heuristic can obtain a good solution for the large-scale problem in reasonable time which is not possible when trying to obtain the optimal solution with the exact DP approach.     

AN EXPERIMENTAL EVALUATION OF THE RELATIONSHIP OF UNCERTAINTY IN THE ENVIRONMENT TO INFORMATION USED BY DECISION MAKERS*

This paper discusses the relationship between uncertainty in a decision making environment and the desired characteristics of information used for decision making. The work is aimed at providing a better understanding of the variables that affect the design of Management Information Systems. An experiment was conducted using an inventory simulator. The subjects could decide on inventory control variables and the amount and type of information to be used in monitoring system performance. The demand for inventory was an external random variable under the control of the experimenter. The experiment investigated how different demand variances affected the decision information used, the decisions made, and the resulting decision effectiveness (cost.) It was found that decision review frequency was not affected by demand variability. However, age and degree of summary of information used were greatly affected. Subjects exposed to high variability used data with a shorter history and a higher level of detail than those exposed to low variability. It was also found that the number of reports used increased from the low to middle variance group, then decreased at very high variance. Subjects tended to “give-up” on their information system at high variance, and they relied on additional safety stock to prevent frequent stockouts. Finally, the correlation between the information used and decision effectiveness as measured by cost was low. This result was contrary to the subjects' beliefs that more and better information produced “better” decisions. It indicates that although variability may strongly affect preferences for different types of information, the information used may not in turn affect decision performance.     

基于一致性度量的概率模糊语言多属性群决策方法

人们通常习惯用语言术语来表达他们的偏好，因此概率型语言术语集（Probabilistic Linguistic Term Set，PLTS）在决策过程中有着十分重要的作用。目前PLTS的研究刚刚起步，有关PLTS的相关研究没有关注到一致性度量的问题，对于PLTS的多属性群决策方法有待进一步研究。首先，给出了一种新的PLTS的集结方法，并且在已有的PLTS可能度公式的基础上，构建了PLTS的相似度量方法，在此基础上，进一步提出了基于PLTS一致性度量的多属性群决策方法。该方法在各决策者权重未知的情况下，考虑到各决策者之间的一致性。首先，定义PLTS的一致性度量公式，确定决策者权重；并根据PLTS的集结方法，集结各决策者的评价信息；最后，利用可能度公式对PLTS进行排序。通过案例分析验证了该方法的可行性和优越性。     

Production,Process Investment,and the Survival of Debt‐Financed Startup Firms

Whether to invest in process development that can reduce the unit cost and thereby raise future profits or to conserve cash and reduce the likelihood of bankruptcy is a key trade‐off faced by many startup firms that have taken on debt. We explore this trade‐off by examining the production quantity and cost reducing R&D investment decisions in a two period model wherein a startup firm must make a minimum level of profit at the end of the first period to survive and operate in the second period. We specify a probabilistic survival measure as a function of production and investment decisions to track and manage the risk exposure of the startup depending on three key market factors: technology, demand, and competitor's cost. We develop managerial insights by characterizing how to create operational hedges against the bankruptcy risk: if a startup makes a “conservative” investment decision, then it also selects an optimal quantity that is less than the monopoly level and hence sacrifices some of first period expected profits to increase its survival chances. If it decides to invest “aggressively,” then it produces more than the monopoly level to cover the higher bankruptcy risk. We also illustrate that debt constraint shrinks the decision space, wherein such process investments are viable.     

审计师关注客户所在地区社会信任水平吗? ——基于Pecking Order模型的研究

文章利用CGSS调查数据度量客户所在地区的社会信任水平,进而考察其对审计师决策的影响。文章首先分别考察了社会信任水平与审计定价、出具非标审计意见的倾向、审计师变更之间的关系,结果发现,公司所在地区的社会信任水平与审计定价、审计师变更均呈显著负向关系,表明审计师会对位于高社会信任地区的客户收取更低的审计费用、审计契约更为稳定,但社会信任与审计师出具非标意见的倾向之间没有显著关联。有序Logistic检验结果表明,在审计定价和审计师变更决策之间,审计师存在优先选择次序:对于低社会信任地区客户,他们会优先通过提高审计收费的手段来控制相关风险。只有在风险超出其承受力、不能通过提高审计费用来控制时,才会放弃客户。此外,文章还发现,地区法治水平能够缓解社会信任对审计师决策的影响。这一研究有助于加深社会信任与审计师决策之间关系的理解,并有助于理解审计师的风险管理策略。     

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.     

Cognitive Limitations and Investment “Myopia”*

Optimization of investment decisions in an uncertain and dynamically evolving environment is difficult due to the limitations of the decision-maker's cognitive capacity. Thus, actual investment decisions may deviate from the dynamically optimal decision rule. This paper investigates how a potential investment rule bias affects the expected payoff from a project that has an uncertain development time and an uncertain completion cost. The result shows that the presence of a potential bias in the adopted decision rule dissipates project value and that the dissipating effect is greater for a longer term project if the completion cost is an increasing function of the time to completion.     

Sequential and Simultaneous Decision Making for Optimizing Health Care Resource Flexibilities

Health care administrators commonly employ two types of resource flexibilities (demand upgrades and staffing flexibility) to efficiently coordinate two critical internal resources, nursing staff and beds, and an external resource (contract nurses) to satisfy stochastic patient demand. Under demand upgrades, when beds are unavailable for patients in a less acute unit, patients are upgraded to a more acute unit if space is available in that unit. Under staffing flexibility, nurses cross‐trained to work in more than one unit are used in addition to dedicated and contract nurses. Resource decisions (beds and staffing) can be made at a single point in time (simultaneous decision making) or at different points in time (sequential decision making). In this article, we address the following questions: for each flexibility configuration, under sequential and simultaneous decision making, what is the optimal resource level required to meet stochastic demand at minimum cost? Is one type of flexibility (e.g., demand upgrades) better than the other type of flexibility (e.g., staffing flexibility)? We use two‐stage stochastic programming to find optimal resource levels for two nonhomogeneous hospital units that face stochastic demand following a continuous, general distribution. We conduct a full‐factorial numerical experiment and find that the benefit of using staffing flexibility on average is greater than the benefit of using demand upgrades. However, the two types of flexibilities have a positive interaction effect and they complement each other. The type of flexibility and decision timing has an independent effect on system performance (capacity and staffing costs). The benefits of cross‐training can be largely realized even if beds and staffing levels have been determined prior to the establishment of a cross‐training initiative.     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.     

Approaches to displaying information to assist decisions under uncertainty

The estimation of the costs of a product or project and the decisions based on these forecasts are subject to much uncertainty relating to factors like unknown future developments. This has been addressed repeatedly in research studies focusing on different aspects of uncertainty; unfortunately, this interest has not yet been adopted in practice. One reason can be found in the inadequate representation of uncertainty. This paper introduces an experiment, which engages different approaches to displaying cost forecasting information to gauge the consideration of uncertainty in the subsequent decision-making process. Three different approaches of displaying cost-forecasting information including the uncertainty involved in the data were tested, namely a three point trend forecast, a bar chart, and a FAN-diagram. Furthermore, the effects of using different levels of contextual information about the decision problem were examined. The results show that decision makers tend to simplify the level of uncertainty from a possible range of future outcomes to the limited form of a point estimate. Furthermore, the contextual information made the participants more aware of uncertainty. In addition, the fan-diagram prompted 75.0% of the participants to consider uncertainty even if they had not used this type of diagram before; it was therefore identified as the most suitable method of graphical information display for encouraging decision makers to consider the uncertainty in cost forecasting.     

库存成本变化的VMI供应链协调

提出了库存成本变化的经济订货批量(EOQ)模型，基于该模型研究了库存成本变化时供应商管理库存(VMI)系统的最优协议问题。在该系统中，订货商和供应商达成缺货成本共担协议：当缺货发生时，供应商需要向订货商支付缺货补偿。订货商和供应商分散决策，订货商通过设计协议来减少其成本，而供应商通过制定补货决策来缩小自身成本。通过与传统系统和整合系统的比较，得出了库存成本变化时VMI系统的最优补货决策和缺货成本共担协议。采用数值算例验证了分析结果。结果表明，当且仅当供应商预期成本等于整合系统的最小总成本与固定缺货罚金之和时，VMI系统与整合系统具有相同的补货决策和系统绩效，即能够实现供应链协调。     

Changing risk preferences in supply chain inventory decisions

The risk preferences of managers may cause their inventory ordering decisions to deviate from the optimal policy. Past studies in operations management have produced mixed results. This study examines this proposition using decision data collected from a supply chain experiment. This article finds that changing the risk preferences of managers with respect to demand changes and supplier failures is a significant behavioural factor in explaining deviations in ordering decisions. This result provides an additional behavioural cause in explaining the bullwhip effect in supply chains. It also provides insights on the challenges for reducing the bullwhip effect in supply chains.     

What do we loose when machines take the decisions?

This paper concerns the technical issues raised when humans are replaced by artificial intelligence (AI) in organisational decision making, or decision making in general. Such automation of human tasks and decision making can of course be beneficial through saving human resources, and through (ideally) leading to better solutions and decisions. However, to guarantee better decisions, the current AI techniques still have some way to go in most areas, and many of the techniques also suffer from weaknesses such as lack of transparency and explainability. The goal of the paper is not to argue against using any kind of AI in organisational decision making. AI techniques have a lot to offer, and can for instance assess a lot more possible decisions—and much faster—than any human can. The purpose is just to point to the weaknesses that AI techniques still have, and that one should be aware of when considering to implement AI to automate human decisions. Significant current AI research goes into reducing its limitations and weaknesses, but this is likely to become a fairly long-term effort. People and organisations might be tempted to fully automate certain crucial aspects of decision making without waiting for these limitations and weaknesses to be reduced—or, even worse, not even being aware of those weaknesses and what is lost in the automatisation process.