Decision Models in Engineering and Management

Defining cash balance is a classic problem in firms’ financial management. For this reason, the aim of this study is to carry out a literature review, presenting the main cash flow management models from the Baumol and Tobin models in 1950s, to Miller–Orr model in 1960s and their development since the 1980s, focused essentially in stochastic models, with publication in economic, financial, and operation research journals. Thus, this chapter provides a review on cash balance management models in order to obtain a more consistent model on a par with investment analysis, observing the characteristics associated with cash maintenance, as well as diversification of financial applications and resources and the lack of literature in stochastic models for this problem.

This paper aims to present a methodology for modeling the financial performance of Brazilian banks. The methodology is based on multi-attribute utility methods that are applied in the aggregation of financial ratios. The weights of these ratios are obtained by the principle of maximum entropy, with the intention of making an objective and non-biased analysis. The methodology is applied to samples of Brazilian banks in the period from 2004 to 2013, and the results are analyzed using a scale of ten risk groups. The methodology proves to be valid in analyzing the financial performance of banks and can be used to support the formulation of industry performance restrictions for problems of portfolio optimization, resource allocation, and credit analysis.

Economic crises that emerge from systemic risks suggest that credit risk management in banks is paramount not only for the survival of companies themselves but also for a resilient worldwide economy. Although regulators establish strictly standards for financial institutions, i.e., capital requirements and management best practices, unpredictability of market behavior and complexity of financial products may have strong impact on corporate performance, jeopardizing institutions, and even economies. In this chapter, we will explore decision models to manage credit risks, focusing on probabilistic and statistical methods that are coupled with machine learning techniques. In particular, we discuss and compare two ensemble methods, bagging and boosting, in studies of application scoring.

Manufacturing sites are primarily dynamic, that is, production plans and schedules are usually affected by disturbances and environmental changes. On the other hand, the decision analysis models for engineering management must aim to represent reality with accuracy. Thus, the study of the dynamic models in the engineering management field is paramount. In this chapter, dynamic decision models for manufacturing and supply chain are discussed. First, an overall review of the deterministic dynamic models based on control theory and state representation is presented. After that, a set of models specifically applied to scheduling and production control is discussed in detail. A comparative analysis of these models is also presented, followed by some directions for future research.

In process-oriented organizations, process owner is accountable for monitoring, controlling and improving the processes for which he/she is responsible and for ensuring that everything is running smoothly. However, he/she does not have enough time to devote equally to all processes, and as different ones require different attention levels, he/she should give priority to those processes that require the highest levels of attention. On that basis, this chapter proposes a management model which supports manager in classifying his/her business processes into categories of managerial procedures for the purposes of planning in the short term. This classification, based on a multiple criteria evaluation, enables activities that are associated with each business process to be effectively managed.

The success of a company depends directly on the performance of its suppliers. In this sense, all aspects that contribute to the competitiveness of a good or service must be considered during the selection of suppliers; these aspects include price, quality, lead time, etc. Therefore, the management of the supply chain should ensure a structured procedure for selecting its suppliers, in which a multi-criteria analysis approach is effectively considered during the selection. This chapter presents a multi-criteria decision support system for selecting suppliers; the system is divided into three main parts: (i) a structural phase, in which the parameters of the model are defined; (ii) the application of the multi-criteria method, in which the multi-criteria method PROMETHEE II is applied to construct a ranking of suppliers according to the parameters defined in the previous step; and (iii) the analysis of the results, in which a sensitivity analysis is performed to verify the robustness of the results. Also, in this chapter, this system was applied to a problem in the civil construction industry and shows that the proposed decision support system is a powerful tool to be used by any organization to support their supplier selection processes. The system assures an evaluation of suppliers based on a set of aspects that are important with regard to the purchasing of a good; also, the decision support system encourages impartiality during the selection and improves the transparency of the process, which indicates that the system is appropriate to support the selection process that occurs in public organizations.

In an increasingly complex, dynamic and highly competitive environment, there has been a proliferation of interorganizational relationships with an emphasis on cooperative relationships. For companies to define or redefine the terms of interdependence in established transactions, negotiations are required, and trust has been considered crucial for enabling joint gains in the development and implementation of the agreement. Negotiators who trust each other transfer their thoughts more comfortably and more readily accept the ideas of the other party because trust not only reduces transaction costs but also has a mutually causal relationship with information sharing, which further creates value in the exchange ratio. The present study analyses trust in the negotiation processes of interorganizational partnerships. The three areas of study, interorganizational relationships, trust and negotiation, are interlinked; therefore, a conceptual model for analysing the dimensions of trust in an integrative negotiation is proposed. The analysis covers the three stages of a negotiation process between two organizations: pre-negotiation, negotiation and post-negotiation. From the results of this study, it is concluded that identifying and developing actions to strengthen the trust dimensions in the processes of relationship negotiations between organizations is important for cooperatively developing and encouraging mutual benefits for the companies involved.

Decision making related to select partners, considering the strategic nature of decisions related to information sharing in partnerships, is essential. This chapter proposes a general and systematic model related to decisions of partnerships management. The model is focused in the strategic decision of partner’s choice for information sharing, under an MCDA perspective, which helps to avoid the bullwhip effect in supply chain management. Thus, it is shown that is possible the suppliers’ categorization in levels of partnerships driving suitable suppliers to share information in appropriate ways. A numerical application is presented to illustrate the application of the model.

Due to the complexity of processes and energy flow in industries, energy management systems play an important role in order to provide guidance to improve energy performance in industrial energy systems, regarding organisational barriers for energy efficiency improvement. Industrial processes are characterized by diverse actors and several criteria (technical, economical, etc.) resulting in a complex decision-making process. Therefore, multicriteria decision models are important tools to support decision makers in energy management systems. A decision model was applied to industrial motor systems using the PROMETHEE II method in order to sort technologies to be replaced. The results present a complete ranking of technologies taking into account the organisation concerns. This contributes for the transposition of some organisational barriers. The work recommends the application of decision model in organisations in order to support decision makers in Energy Management Systems to improve the energy performance.

Many problems in a water supply network, such as control of physical and economic losses, waste, and lack of a maintenance plan, involve different stakeholders to analyze complex decision making. Thus, several studies and models had been developed to aid decisions making in order to reduce unnecessary exploitation of water, and losses in the water distribution networks. Some successful experiences in this sector regarding models to assist decision makers to deal with problems in the water distribution engineering maintenance area are shown in this chapter. The topics discussed in this chapter involve three major problems that may be addressed with multicriteria analysis in water supply systems: (1) sorting measurement flow areas; (2) ordering maintenance alternatives, which are potentially effective, to be implemented in the priority areas according to several decision makers’ point of view; and (3) aggregation of group members’ preferences. In the multicriteria analysis, the models developed can help to give a clear view of the problem, making it possible to recognize priority classes to support the maintenance management, and the major potential alternatives to reduce water loss. Several issues will also be approached in the chapter such as water supply and distribution networks infrastructure; economic and environmental impacts; and methods for decision making among others.

Discussions addressing basic sanitation frequently revolve around the question of urban drainage and solid waste. In this context, the management of public policies in Brazil has been undergoing an intense change in recent years, with the incorporation of new methods and tools for analysis of areas of intervention, public programs monitoring, and in decision making in general. This chapter illustrates the use of information by a public official in order to decide whether to invest and how much to invest in basic sanitation and waste collection. The information is paramount to evaluate the prospects of a policy. In order to demonstrate the decision-making process regarding the systematic relation between alterations in income and the quality of the environment, we use the environmental Kuznets curve (EKC).

Family farming is one of the primary generators of work, employment and income in rural areas of Brazil. Based on this knowledge, the federal government has adopted incentives for producers in an attempt to make this productive sector more competitive and efficient. However, much remains to be done to make the development truly sustainable. By evaluating this dynamic, this paper proposes a model based on multimethodologies to assist the actors in defining and applying public policies. To facilitate and provide a greater understanding of the problem, the Strategic Options Development and Analysis (SODA) method was applied through cognitive maps in conjunction with the Strength, Weakness, Opportunities, Threats (SWOT) approach, which allowed a higher level of analysis of the alternatives. With the alternatives available, the multicriteria method PROMETHEE II was applied to ranking the alternatives to be implemented. The proposed model provides the people involved with a greater level of insight into the problem to be analysed and facilitates the adoption of public policies that meet the needs and preferences of farmers.

Choosing the correct location for a construction project is a crucial decision in the practice of civil engineering; in fact, knowledge of the economic potential of available locations can orient the analyst in the decision analysis process to optimise her/his resources, aiming for a profit that overcomes the cost of construction. In this context, PROMETHEE IV and its kernel density estimator can help the analyst through her/his decision analysis process in what is known as decision-making for civil engineering. In this chapter, we present how PROMETHEE IV and the kernel density estimator (KDE) could be used to choose available locations for construction, aiming to choose the best locations and to avoid the worst. In addition, an application using Columbus data from Anselin (1988) is also presented.

Environmental regulation compliance and economic benefits represent the main motivation factors for electronic waste (e-waste) management improvement. Some issues regarding social and economic advantages, environmental impact mitigation, and technological improvement of devices seem to be conflicting; at the same time, stakeholders need to be prepared to lead with a wide range of knowledge to minimize error during decision-making, and for assisting in those tasks, information systems seem to be a very helpful tool. A considerable number of modeling tools are available to waste electrical electronic equipment (WEEE) management and for decision analysis, ranging from supporting in the location of logistic facilities to more operational issues such as deciding what to do with end-of-life (EOL) devices. In this context, we analyze different decision-making tools [decision support systems (DSS)], dealing with the management of e-waste, showing the opportunities that arise around these computer-assisted models. In order to provide sharper information and inspire stakeholders, this review presents a chronological approach about the main tools available, as well as digs looking for promising new approaches that in this context could be useful for the decision makers.

The decision on who will pay for environmental damage to Brazilian agriculture and how this will be done looks like a zero-sum game between environmentalists and farmers. Their different interests, however, do not prevent the development of strategies that maximize social welfare. This work shows that it is possible to produce more with fewer resources and less environmental impact. Following Färe et al. (Rev Econ Stat 71:90–98, 1989) and Picazo-Tadeo et al. (Eur J Oper Res 220:798–809, 2012), we use data envelopment analysis (DEA) and directional distance functions (DDF) to evaluate the impact of environmental regulations on the drop in the productivity and eco-efficiency of Brazilian agriculture. The methodology is applied to the data from 33 decision-making units (DMUs): 27 States of the Federation of Brazil as a whole, and the five geographical regions taken in their entirety—relating to three inputs and three outputs, one of which is desirable and the other two undesirable. The results show that when DMUs face environmental rules preventing free disposal of undesirable output, their potential to increase desirable output is affected. Also an estimate was made of a set of eco-efficiency indicators that, satisfying the Pareto optimality concept, can support the formulation of strategies consistent with the simultaneous optimization of economic and environmental goals. Comparing the results with the commitments made by Brazil to reduce emissions in agriculture by 2020, it is concluded that the country can surpass the goal with technology given only improving the emissions, however, maintaining the level of resource consumption, production, and degraded lands. On the other hand, while trying to maximize production while minimizing land degradation and CO₂ emissions, a maximum of two years is needed to attain this goal. Additional measures may be used, such as adopting international best practices and developing clean technology innovation. Accordingly, it is recommended that a high priority be given to strategies aimed at improving eco-efficiency and eliminating institutional barriers to transferring and spreading knowledge of best national and international technologies. These strategies can generate greater results in productivity and environmental conservation than other actions aimed at fostering technological innovation. While the cost of imitation of existing clean technology is lower than the cost of innovation, eco-inefficient units can approach the leaders, thus creating the conditions to sustain the convergence of economic and environmental development.