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Application of a Decision Support System for operational decisions
Abstract
This paper discusses the application of a Decision Support System (DSS) for making operational decisions in a food processing industry. A model is developed for determining the optimum production scenario for every week based on the tradeoffs between service levels, costs, inventories, changeovers and capacity. The experiences of the authors in designing, developing, and implementing the Decision Support System are shared in this paper.
... Moreover as Bozkir and Akcapinar Sezer point out, approaches which forecast the demand for a served menu can present many benefits to institutions as it would optimize the balance of supply and demand in name of saving resources and work power [1]. At this point, Sundararajan et al. address the importance of information technology and services, computer aided tools providing intelligence to make real time decisions [3]. With this motivation, Sundararajan et al. implemented a decision support system for making operational decisions in food processing industry which is based on optimization techniques and focuses on determining optimum production scenario for every week based on the tradeoffs between service levels, inventories, costs and capacity [3]. ...
... At this point, Sundararajan et al. address the importance of information technology and services, computer aided tools providing intelligence to make real time decisions [3]. With this motivation, Sundararajan et al. implemented a decision support system for making operational decisions in food processing industry which is based on optimization techniques and focuses on determining optimum production scenario for every week based on the tradeoffs between service levels, inventories, costs and capacity [3]. As another example, [4] investigated the factors affecting menu demand in universities by employing decision tree method which is a member of predictive data mining methods family. ...
The uncertainty of consumption demand in institutional food courts can cause loses of resources, work power and prestige. In this study, to overcome this problem, decision tree, a predictive data mining method was utilized. Thus, decision tree models sourced from an original 44 monthly dataset were generated and a tool named ADEM was designed and developed to make online prediction against the best model. To determine the best model, 10 fold cross validation technique was applied. As a result, decision tree models reaching up 80.78 accuracy levels (VAF value) were obtained and a user friendly, platform independent and reusable decision tree based decision support tool is presented for managers and nutritionists.
... The overall architecture of these tools depends on the type of the problem, and more importantly, on the phase where the tool assists the decision of the experts. While the type of the problems that computer-aided tools deal with is varying, one can generally distinct two main phases of their implementation in problem solving: the design-decisions phase, in which the computer aided tool helps the expert to build different scenarios during the design stage of the process (Kheawhom Hirao, 2002; Draman et al., 2002) and the operational-decisions phase, during which the expert creates and examines various optimization scenarios for the operational variables of a given process design (Sundararajan et al., 1998). ...
A computer-aided tool for the simulation, optimization and analysis of the combined operation of the hydrodesulphurization (HDS) and fluid catalytic cracking (FCC) processes in an oil refinery is presented. The optimization of these processes is an important yet difficult engineering task, because of the complexity in the integration of the two units, the large number of interacting variables, the product quality specifications and the financial benefits associated. The proposed tool is developed in a user-friendly Visual Basic environment and operates in two different modes: the modelling-prediction mode and the optimization-sensitivity analysis mode. The modelling of the processes is based on 'short form' models, which were created following statistical and neural network approaches. This kind of model usually has short computing time requirements, which is critical for the optimization mode. The optimization algorithm is based on a financial objective function with a flexible form, which gives the user the option to explore a variety of scenarios. Industrial runs have verified the modelling accuracy of the tool. The optimization scenarios examined include the contemporary needs of modern refineries for LPG and gasoline maximization, subject to strict quality specifications. The demonstration of this tool aims to give an insight into the system dependencies and add knowledge on the possibility of a more profitable operation of such a complex process.
... Clinical services are all about decision making. In this regard, we could refer to the application of decision support system for operational decision-making [4]. The increase in health care cost and inefficiencies are partly due to inadequate and tedious purchasing procedures [5]. ...
Rising cost and increasing expectation from patients (consumers) have caused health care planners to seek ways in improving efficiency, while striking a balance with the quality of services offered. Consequently several successful health-management organizations apply advanced computer technology as the catalyst for a whole new set of parameters for product design, development and operation to achieve excellence in their core business process. In this study, computer technologies refer primarily to technologies used for healthcare delivery services, which are related specifically to aid effective and efficient medical care for patients (the consumers). This study also explores how integrated emerging technologies can play a valuable role in cost-effective medical and health care delivery by means of computer technology.
... Clinical services are all about decision making. In this regard, we could refer to the application of the decision support system, introduced by Sundararajan et al. (1998), to make operational decisions in a food-processing industry for determining the optimum production based on the tradeoffs between some decision factors. The fuzzy set theory could also be applicable for decision making under fuzzy factors and uncertainties. ...
Rising costs and increasing expectations from patients (consumers) have caused healthcare planners to seek ways to improve efficiency, while striking a balance with the quality of services offered. This study explores how the integrated emerging technologies can play a valuable role in cost-effective medical and healthcare delivery by means of e-data computer simulation of clinical units, borderless data (such as intelligent patient medical records), engineering e-processes and systems analysis of primary-to-tertiary care systems. Incorporation of costly medical technologies into healthcare delivery is supposed to make: • medical diagnosis more timely and reliable • treatment more efficacious • the delivery system more cost-effective. However, this can only happen if: • all the components of the healthcare delivery system are analysed as a chain system without duplication of services • the submits of each of these components are also operating as a multi-compartmental system with feedback loops tying them into one integrated regulatory system, having the objectives of optimal patient care with streamlined operations. Finally, a virtual diagnostic clinic is constructed by means of a system dynamics approach to illustrate these concepts. China Anhui province. He has served as a Consultant to industry players, including contract manufacturers, construction contractors, equipment/product manufacturers, software developers and loss adjusters. He has been involved in several patent infringement proceedings before the Singapore High Court as an expert witness.
... Decision Making Models. Many research studies have provided evidence that implementing tools, systems (such as decision support systems and supervisory control systems) and training programs can provide more information to the operational staffs and increase their knowledge of the process and operation during decision making [6,10,11,12,13]. The mechanism of improvement in decision quality could be the subtle change in attention and perception in the decision making process (or thinking process) [14,15]. ...
In aluminium smelters, the operational staffs constantly face decision making situations for operation and process control and these decisions can have significant impact on the process. The smelting process involves highly complex mechanisms and has rich information but low observability. In this environment, without support tools, systematic information management, or robust control models, decision making is challenging. This paper discusses different types of decision making processes and demonstrates that naturalistic decision making models (Recognition-Primed Decision Making, ie RPD) are more suitable to describe the situations in smelters. A model which combines an advanced control model, a system and human interactive approach and the thinking process in RPD is proposed to improve the quality of decisions for the operational staffs in smelters, hence the efficiency and productivity of the process.
... Martinsons et al. (1999) designed a balanced scorecard for information system (IS) that measures and evaluates IS activities from the following perspectives: business value, user orientation, internal process, and future readiness. Moreover, Sundararajan et al. (1998) discussed the application of a decision support system (DSS) for operational decision-making in a food processing industry. The above demonstrates that BSC has emerged as a significant decision support tool in strategic management. ...
First developed by Kaplan and Norton [Kaplan R. S., & Norton D. P. (1992). The balance scorecard – measures that drive performance. Harvard Business Review, 70(1), 71–79], balanced scorecard (BSC) provides an integrated view of overall organizational performance and strategic objectives. BSC integrates financial measures with other key performance indicators to create a perspective that incorporates both financial and non-financial aspects. BSC has proven a powerful tool for strategic planning and communicating strategy that assists in strategy implementation. Successful strategy implementation is based on effective strategic planning. Owing to the strategic planning being a virtual necessity in business, this work proposes an integrated approach for the balanced scorecard tool and knowledge-based system using the analytic hierarchy process (AHP) method, and then develops an intellectual BSC knowledge-based system for strategic planning that sets or selects firm management or operational strategies based on the following perspectives: learning and growth, internal/business process, customer, and financial performance. This system can help determine specific strategy weights. The intellectual BSC knowledge-based system facilities efficient automated strategic planning.
In order to find out the critical factors on exogenous pathogeny, we classified 2590 disease cases based on the ID3 decision tree method. Our classification result is acquired by calculating the information entropy, conditional entropy and information gain, according to ldquothe six pathogenic factorsrdquo. In the process, we first gained a set of basic factors of exogenous affection by filtering and pre-process methods. And then we established a decision tree with critical factors. Finally, we transformed the decision tree into production rules to support the diagnostic system.
This paper describes a simulation-based parameter design (PD) approach for optimizing machine operating strategy under stochastic running conditions. The approach presents a Taguchi-based definition to the PD problem in which control factors include machine operating hours, operating pattern, scheduled shutdowns, maintenance level, and product changeovers. Random factors include machine random variables (RVs) of cycle time (CT), time-between-failure (TBF), time-to-repair (TTR), and defects rate (DR). Machine performance, as a complicated function of control and random factors, is defined in terms of net productivity (NP) based on three key performance indicators: gross throughput (GT), reliability rate (RR), and quality rate (QR). It is noticed that the resulting problem definition presents both modeling and optimization difficulties. Modeling complications result from the sensitivity of machine RVs to different settings of machine operating parameters and the difficulty to estimate machine performance in terms of NP under stochastic running conditions. Optimization complications result from the limited capability of mathematical modeling and experimental design in tackling the resulting large-in-space combinatorial optimization problem. To tackle such difficulties, therefore, the proposed approach presents a combined empirical modeling and Monte Carlo simulation (MCS) method to model the sensitive factors interdependencies and to estimate NP under stochastic running conditions. For combinatorial optimization, the approach utilizes a simulated-annealing (SA) heuristic to solve the defined PD problem and to provide optimal or near optimal settings to machine operating parameters. Approach procedure and potential benefits are illustrated through a case study example.
Purpose
The purpose of this paper is to develop energy optimizer (ENEO) – a model‐based decision support system (DSS) for an existing European chemical plant with a multi‐stage continuous production process. The system comprises two modules – energy cost minimization and joined energy cost minimization and output maximization. Following the description of the researched production, the paper presents a gist of the underlying formulations. Then, it tests the DSS on real data instances with a focus on its configuration, practical implications and implementation challenges.
Design/methodology/approach
The design of the planning tool is consistent with that of the model‐based DSS and based on the existing information systems. The defined research problems are explored with the use of quantitative methods – the operations research methodology.
Findings
The findings show that ENEO reflects the essence of the researched production process and can provide benefits in practical business operations.
Research limitations/implications
Both the proposed system configuration and the formulated models lay a foundation to further research within the described industrial setting.
Practical implications
The system can be utilized in daily operations to provide substantial cost savings, improved capacity utilization and reactivity.
Originality/value
This paper contributes to research by bridging the gap between theory and practice. On the one hand, it describes an unexplored problem and its subsequent solution embodied in the DSS. On the other hand, it emphasizes the importance of applying the operations research methodology to the real‐world issues. Therefore, this work is valuable to both academics and practitioners.
This paper presents a Decision Support System (DSS) for integrating tactical and operational decisions in a manufacturing system that is operated in a job shop environment. The system consists of a planning module, a scheduling module, a support database and graphical user interface. The key features are the inclusion of bills of materials and processes in the scheduling module and the option to freeze a part of the previous schedule to allow for multiple pass scheduling exercises. The rationale in designing, developing and implementing the system is discussed in this paper.
In this article Robert S. Kaplan and David P. Norton report on the Balance Scorecard. Before the Balanced Scorecard companies have used various measurement systems that have made incremental improvements and concentrated mostly on the company's financials. The Balanced Scorecard shows you four different perspectives in which to choose measures that can redefine a company's processes measurement system so short term and long term objectives are in balance with each other. When using the Balanced Scorecard a company is no longer needs to worry about small incremental improvements, but a new processes measurement system that will allow a company to get where it wants to go.
The new form of commercial competition rapidly spreading in the technologically most advanced societies has been named `Agile Competition.' It possesses decisive advantages in what are today the most profitable markets for goods and services. The information processing capability to treat masses of customers as individuals is permitting more and more companies to offer individualized products while maintaining high volumes of production. The convergence of computer networking and telecommunication technologies is making it possible for groups of companies to coordinate geographically and institutionally distributed capabilities into a single `Virtual Company,' and in the process, achieve powerful competitive advantages.
Frustrated by the inadequacies of traditional performance measurement systems, some managers have abandoned financial measures like return on equity and earnings per share. "Make operational improvements and the numbers will follow," the argument goes. But managers do not want to choose between financial and operational measures. Executives want a balanced presentation of measures that allow them to view the company from several perspectives simultaneously. During a year-long research project with 12 companies at the leading edge of performance measurement, the authors developed a "balanced scorecard," a new performance measurement system that gives top managers a fast but comprehensive view of the business. The balanced scorecard includes financial measures that tell the results of actions already taken. And it complements those financial measures with three sets of operational measures having to do with customer satisfaction, internal processes, and the organization's ability to learn and improve--the activities that drive future financial performance. Managers can create a balanced scorecard by translating their company's strategy and mission statements into specific goals and measures. To create the part of the scorecard that focuses on the customer perspective, for example, executives at Electronic Circuits Inc. established general goals for customer performance: get standard products to market sooner, improve customers' time-to-market, become customers' supplier of choice through partnerships, and develop innovative products tailored to customer needs. Managers translated these elements of strategy into four specific goals and identified a measure for each.
Dynamic version of the economic lot sizing Model
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