Relevant bibliographies by topics / Decision support systems; Data warehouse; DW

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Decision support systems; Data warehouse; DW'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Decision support systems; Data warehouse; DW"

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Sakka, Amir, Sandro Bimonte, Francois Pinet, and Lucile Sautot. "Volunteer Data Warehouse." International Journal of Data Warehousing and Mining 17, no. 3 (2021): 1–21. http://dx.doi.org/10.4018/ijdwm.2021070101.

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With the maturity of crowdsourcing systems, new analysis possibilities appear where volunteers play a crucial role by bringing the implicit knowledge issued from practical and daily experience. At the same time, data warehouse and OLAP systems represent the first citizen of decision-support systems. They allow analyzing a huge volume of data according to the multidimensional model. The more the multidimensional model reflects the decision-makers' analysis needs, the more the DW project is successful. However, when volunteers are involved in the design of DWs, existing DW design methodologies p
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Tiwari, Rajdev, Anubhav Tiwari, and Manu Pratap Singh. "Fuzzy-Rule Based Adaptive Data Warehouse." International Journal of Applied Evolutionary Computation 3, no. 1 (2012): 47–65. http://dx.doi.org/10.4018/jaec.2012010103.

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Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and i
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Azaiez, Noura, and Jalel Akaichi. "What is the Impact of Mobility Data Integration on Decision Support Systems' Modelling and Evolution?" International Journal of Information Systems in the Service Sector 8, no. 1 (2016): 1–12. http://dx.doi.org/10.4018/ijisss.2016010101.

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Classic data seems unable to keep pace with the technology era. In fact, the incredible progress related to geographic information systems, pervasive computing, and positioning technologies have motivated classic data to evolve towards new data kind called mobility data. For decisional purposes, these later have to be analysed; therefore, their integration into a decision support system becomes a must. However, the data warehouse used to store classical data seems to be inadequate for mobility data storage and analysis. This gave the birth of a new central repository type called trajectory dat
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Benkhaled, Hamid Naceur, Djamel Berrabah, and Faouzi Boufares. "Data Warehouses and Big Data." International Journal of Organizational and Collective Intelligence 10, no. 3 (2020): 1–13. http://dx.doi.org/10.4018/ijoci.2020070101.

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Before the arrival of the Big Data era, data warehouse (DW) systems were considered the best decision support systems (DSS). DW systems have always helped organizations around the world to analyse their stored data and use it in making decisive decisions. However, analyzing and mining data of poor quality can give the wrong conclusions. Several data quality (DQ) problems can appear during a data warehouse project like missing values, duplicates values, integrity constrains issues and more. As a result, organizations around the world are more aware of the importance of data quality and invest a
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Gascueña, Concepción M., and Rafael Guadalupe. "Modelling Spatial Decision Support Systems Focused on the Development of a Data Warehouse." International Journal of Decision Support System Technology 6, no. 3 (2014): 43–64. http://dx.doi.org/10.4018/ijdsst.2014070103.

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Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning medium and long-term “the modus operandi” of these organizations. Despite the growing importance of these systems, most proposals do not include its total development, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fi
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Mohseni, Mohsen, and Mohammad Karim Sohrabi. "MVPP-Based Materialized View Selection in Data Warehouses Using Simulated Annealing." International Journal of Cooperative Information Systems 29, no. 03 (2020): 2050001. http://dx.doi.org/10.1142/s021884302050001x.

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The process of extracting data from different heterogeneous data sources, transforming them into an integrated, unified and cleaned repository, and storing the result as a single entity leads to the construction of a data warehouse (DW), which facilitates access to data for the users of information systems and decision support systems. Due to their enormous volumes of data, processing of analytical queries of decision support systems need to scan very large amounts of data, which has a negative effect on the systems’ response time. Because of the special importance of online analytical process
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Chakiri, Houda, Mohammed El Mohajir, and Nasser Assem. "A data warehouse hybrid design framework using domain ontologies for local good-governance assessment." Transforming Government: People, Process and Policy 14, no. 2 (2020): 171–203. http://dx.doi.org/10.1108/tg-04-2019-0025.

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Purpose Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government.
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Nazier, Mostafa Medhat, Dr Ayman Khedr, and Assoc Prof Mohamed Haggag. "Business Intelligence and its role to enhance Corporate Performance Management." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 3, no. 3 (2013): 08–15. http://dx.doi.org/10.24297/ijmit.v3i3.1745.

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As every small or large organization requires information to promote their business by forecasting the future trends, information is now the primary tool to understand the market trends and understand their own position in the market comparison to its competitors. Business intelligence is the use of an organizations disparate data to provide meaningful information and analyses to employees, customers, suppliers, and partners for more efficient and effective decision-making. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OL
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Younsi, Fatima-Zohra, Ahmed Bounnekar, Djamila Hamdadou, and Omar Boussaid. "Integration of Multiple Regression Model in an Epidemiological Decision Support System." International Journal of Information Technology & Decision Making 18, no. 06 (2019): 1755–83. http://dx.doi.org/10.1142/s0219622019500408.

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Prevention and control of influenza epidemics are major challenges for public health care services. Early identification of flu outbreak is an important step towards implementing effective disease interventions for reducing mortality and morbidity in human populations. Indeed, health officials need a real geo-making tool for monitoring and prediction. The aim of the current study is to discuss a novel spatiotemporal tool for monitoring and predicting the phenomenon of the seasonal influenza epidemic spread in the human population using multiple regression analysis. The suggested tool is mainly
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Bala, Mahfoud, Omar Boussaid, and Zaia Alimazighi. "Extracting-Transforming-Loading Modeling Approach for Big Data Analytics." International Journal of Decision Support System Technology 8, no. 4 (2016): 50–69. http://dx.doi.org/10.4018/ijdsst.2016100104.

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Due to their widespread use, Internet, Web 2.0 and digital sensors create data in non-traditional volumes (at terabytes and petabytes scale). The big data characterized by the four V's has brought with it new challenges given the limited capabilities of traditional computing systems. This paper aims to provide solutions which can cope with very large data in Decision-Support Systems (DSSs). In the data integration phase, specifically, the authors propose a conceptual modeling approach for parallel and distributed Extracting-Transforming-Loading (ETL) processes. Among the complexity dimensions
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Dissertations / Theses on the topic "Decision support systems; Data warehouse; DW"

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El-Ragal, Ahmed Abdel Hameed Hassan. "Building DSS using knowledge discovery in database applied to admission and registration functions." Thesis, University of Plymouth, 2001. http://hdl.handle.net/10026.1/1971.

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This research investigates the practical issues surrounding the development and implementation of Decision Support Systems (DSS). The research describes the traditional development approaches analyzing their drawbacks and introduces a new DSS development methodology. The proposed DSS methodology is based upon four modules; needs' analysis, data warehouse (DW), knowledge discovery in database (KDD), and a DSS module. The proposed DSS methodology is applied to and evaluated using the admission and registration functions in Egyptian Universities. The research investigates the organizational requi
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Simões, Carlos Enrique Hernández. "Geovisualização analítica: desenvolvimento de um protótipo de um sistema analítico de informações para a gestão da coleta seletiva de resíduos urbanos recicláveis." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/3/3138/tde-17082010-111747/.

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Os resíduos urbanos descartados de forma irregular constituem um problema sério, principalmente nas grandes cidades, causando entupimento de bueiros e drenagem com conseqüentes inundações, sujeira e transmissão de doenças tais como leptospirose e dengue além de ser um estorvo para o trânsito e acarretarem gastos para a Prefeitura. Por outro lado, reciclar este material é uma fonte de receitas e um gerador de empregos. A Geovisualização Analítica pode ser de grande auxílio para a análise desse problema complexo e para a tomada de decisão. Com essa motivação, a presente dissertação procura forne
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Agrawal, Vikas R. "Data warehouse operational design : view selection and performance simulation." Toledo, Ohio : University of Toledo, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=toledo1104773641.

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Dissertation (Ph.D.)--University of Toledo, 2005.<br>Typescript. "Submitted as partial fulfillment of the requirements for the Doctor of Philosophy degree in Manufacturing Management and Engineering. " "A dissertation entitled"--at head of title. Title from title page of PDF document. Bibliography: p. 113-118.
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De, la Rosa Sean Paul. "Internal control risks within the data warehouse environment." Diss., University of Pretoria, 1999. http://hdl.handle.net/2263/24315.

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Aldinhas, Helena. "A aplicação de um data warehouse às bibliotecas portuguesas." Master's thesis, Universidade de Évora, 2009. http://hdl.handle.net/10174/21060.

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O desenvolvimento das novas Tecnologias da Informação e Comunicação tem obrigado as bibliotecas a um esforço constante na actualização dos seus procedimentos. Num contexto de mudança, a biblioteca deve continuar a saber dar respostas adequadas aos seus utilizadores, combinando os serviços tradicionais com estratégias de funcionamento renovadas e serviços e actividades inovadores. O principal objectivo deste estudo é desenhar um Data warehouse passível de ser aplicado em bibliotecas portuguesas, uma vez que este sistema permite armazenar e gerir grandes quantidades de dados em vários formatos e
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Esterhuyse, Jacques. "The use of object oriented systems development methodologies in data warehouse development / J. Esterhuyse." Thesis, North-West University, 2008. http://hdl.handle.net/10394/3661.

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Research has shown that data warehouses potentially offer great investment opportunities to business. To benefit from this, business needs to invest large sums of money. Such investments are very risky, as no guarantee of the success of these ventures can be given. Object-oriented development has proved successful for developing operational systems in industry. This study researches object-oriented techniques to discover whether these techniques could be used successfully in data warehousing. A literature study focuses on the definition of an information systems development methodology and def
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Oliveira, Itelvina Silva de. "Teste baseado em defeitos para ambientes de data warehouse." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1413.

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As organizações necessitam gerenciar informações para obter a melhoria contínua dos seus processos de negócios e agregar conhecimento que ofereça suporte ao processo decisório. Estas informações, muitas vezes, são disponibilizadas por ambientes de Data Warehouse (DW), nos quais os dados são manipulados e transformados. A qualidade dos dados nesses ambientes é essencial para a correta tomada de decisão, tornando-se imprescindível a aplicação de testes. O objetivo deste trabalho é elaborar e validar a aplicação de uma abordagem de teste para DW com o emprego de critérios da técnica de teste base
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Cazarini, Aline. "Auxílio do Data Warehouse e suas ferramentas à estratégia de CRM analítico." Universidade de São Paulo, 2002. http://www.teses.usp.br/teses/disponiveis/18/18140/tde-06052016-143213/.

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Atualmente, uma das grandes vantagens competitivas que uma empresa possui em relação a seu concorrente é a informação sobre seu cliente. As estratégias de Customer Relationship Management (CRM), propiciam o profundo conhecimento do cliente, para que a empresa possa tratá-lo de forma personalizada e reconhecê-lo como seu principal patrimônio. Segundo TAURION (2000) e DW BRASIL (2001), para suportar essa tecnologia, é necessário que as empresas possuam um repositório de dados históricos de clientes. O Data Warehouse (DW) possui diversas características que utilizam, de forma adequada e eficiente
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Meira, Katia Milena Gonçalves. "Utilização da tecnologia Data Warehousing e da ferramenta OLAP para apoiar a captação de doadores de sangue: estudo de caso no Hemonúcleo Regional de Jáu." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/18/18140/tde-01082017-115123/.

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As pesquisas de apoio a decisão na área de saúde, muitas vezes enfocam o diagnóstico e não o caráter gerencial da instituição. Portanto, as unidades de saúde com todas as suas particularidades, necessitam de informações confiáveis e precisas para auxiliá-las na tomada de decisão. Os hemonúcleos convivem com uma luta constante na captação de doadores de sangue para que possam garantir hemocomponentes em quantidade necessária e qualidade para a sua região de abrangência e, informações que possam auxiliá-los na manutenção desses estoques são imprescindíveis. A tecnologia Data Warehousing pode tra
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Koylu, Caglar. "A Case Study In Weather Pattern Searching Using A Spatial Data Warehouse Model." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609573/index.pdf.

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Data warehousing and Online Analytical Processing (OLAP) technology has been used to access, visualize and analyze multidimensional, aggregated, and summarized data. Large part of data contains spatial components. Thus, these spatial components convey valuable information and must be included in exploration and analysis phases of a spatial decision support system (SDSS). On the other hand, Geographic Information Systems (GISs) provide a wide range of tools to analyze spatial phenomena and therefore must be included in the analysis phases of a decision support system (DSS). In this regard, this
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Books on the topic "Decision support systems; Data warehouse; DW"

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J, Watson Hugh, ed. Decision support in the data warehouse. Prentice Hall, 1998.

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Poe, Vidette. Building a data warehouse for decision support. Prentice Hall PTR, 1996.

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Patricia, Klauer, and Brobst Stephen, eds. Building a data warehouse for decision support. 2nd ed. Prentice Hall PTR, 1998.

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L, Reeves Laura, ed. Building a data warehouse for decision support. Prentice Hall PTR, 1997.

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Poe, Vidette. Building a data warehouse for decision support. 2nd ed. Prentice Hall, 1998.

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1945-, Hackathorn Richard D., ed. Using the data warehouse. Wiley, 1994.

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Whelan, Barry. Critical factors in implementing a data warehouse. University College Dublin, Graduate School of Business, 1998.

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Solving operational business intelligence with Infosphere Warehouse advanced edition. Vervante, 2012.

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Gautam, Mitra, ed. Decision modelling and information systems: The information value chain. Kluwer Academic Publishers, 2003.

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Koutsoukis, Nikitas-Spiros. Decision modelling and information systems: The information value chain. Kluwer Academic Publishers, 2004.

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Book chapters on the topic "Decision support systems; Data warehouse; DW"

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Shanks, G., and P. Darke. "Incorporating Context to Improve Understanding of a Data Warehouse." In Context Sensitive Decision Support Systems. Springer US, 1998. http://dx.doi.org/10.1007/978-0-387-35356-2_16.

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de Moura, Ana Maria C., Marcio Victorino, and Asterio Tanaka. "Combining Mediator and Data Warehouse Technologies for Developing Environmental Decision Support Systems." In Geographic Information Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45799-2_14.

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Wells, John D., and Traci J. Hess. "Understanding Decision-Making in Data Warehousing and Related Decision Support Systems." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch129.

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Many businesses have made or are making significant investments in data warehouses that reportedly support a myriad of decision support systems (DSS). Due to the newness of data warehousing and related DSS (DW-DSS), the nature of the decision support provided to DW-DSS users and the related impact on decision performance have not been investigated in an applied setting. An explanatory case study was undertaken at a financial services organization that implemented a particular type of DW-DSS, a Customer Relationship Management (CRM) system. The DSS-decision performance model has provided some theoretical guidance for this exploration. The case study results show that the decision-making support provided by these systems is limited and that an extended version of the DSS-decision performance model may better describe the factors that influence individual decision-making performance.
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Wells, John D., and Traci J. Hess. "Understanding Decision-Making in Data Warehousing and Related Decision Support Systems." In Advances in Information Resources Management. IGI Global, 2004. http://dx.doi.org/10.4018/978-1-59140-253-4.ch014.

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Many businesses have made or are making significant investments in data warehouses that reportedly support a myriad of decision support systems (DSS). Due to the newness of data warehousing and related DSS (DW-DSS), the nature of the decision support provided to DW-DSS users and the related impact on decision performance have not been investigated in an applied setting. An explanatory case study was undertaken at a financial services organization that implemented a particular type of DW-DSS, a Customer Relationship Management (CRM) system. The DSS-decision performance model has provided some theoretical guidance for this exploration. The case study results show that the decision-making support provided by these systems is limited and that an extended version of the DSS-decision performance model may better describe the factors that influence individual decision-making performance.
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Malinowski, Elzbieta. "Requirement Specification and Conceptual Modeling for Data Warehouses." In Handbook of Research on Innovations in Database Technologies and Applications. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-242-8.ch008.

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Data warehouses (DWs) integrate data from different source systems in order to provide historical information that supports the decision-making process. The design of a DW is a complex and costly task since the inclusion of different data items in a DW depends on both users’ needs and data availability in source systems. Currently, there is still a lack of a methodological framework that guides developers through the different stages of the DW design process. On the one hand, there are several proposals that informally describe the phases used for developing DWs based on the authors’ experience in building such systems (Inmon, 2002; Kimball, Reeves, Ross, &amp; Thornthwaite, 1998). On the other hand, the scientific community proposes a variety of approaches for developing DWs, discussed in the next section. Nevertheless, they either include features that are meant for the specific conceptual model used by the authors, or they are very complex. This situation has occurred since the need to build DW systems that fulfill user expectations was ahead of methodological and formal approaches for DW development, just like the one we had for operational databases.
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David, Perez, Somodevilla Maria, and Pineda Ivo. "Fuzzy Spatial Data Warehouse: A Multidimensional Model." In Decision Support Systems Advances in. InTech, 2010. http://dx.doi.org/10.5772/39389.

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Olaru, Marius Octavian, and Maurizio Vincini. "A Data Warehouse Integration Methodology in Support of Collaborating SMEs." In Advances in Business Information Systems and Analytics. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7272-7.ch014.

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Collaborative business making is emerging as a possible solution for the difficulties that Small and Medium Enterprises (SMEs) are having in recent difficult economic scenarios. In fact, collaboration, as opposed to competition, may provide a competitive advantage to companies and organizations that operate in a joint business structure. When dealing with multiple organizations, managers must have access to unified strategic information obtained from the information repositories of each individual organization; unfortunately, traditional Business Intelligence (BI) tools are not designed with the aim of collaboration so the task becomes difficult from a managerial, organizational, and technological point of view. To deal with this shortcoming, the authors provide an integration, mapping-based, methodology for heterogeneous Data Warehouses that aims at facilitating business stakeholders' access to unified strategic information. A complete formalization, based on graph theory and the RELEVANT clustering approach, is provided. Furthermore, the authors perform an experimental evaluation of the proposed method by applying it over two DW instances.
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Prakash, Deepika. "Data Warehouse Support for Policy Enforcement Rule Formulation." In New Perspectives on Information Systems Modeling and Design. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7271-8.ch011.

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It is believed that a data warehouse is for operational decision making. Recently, a proposal was made to support decision making for formulating policy enforcement rules that enforce policies. These rules are expressed in the WHEN-IF-THEN form. Guidelines are proposed to elicit two types of actions, triggering actions that cause the policy violation and the corresponding correcting actions. The decision-making problem is that of selecting the most appropriate correcting action in the event of a policy violation. This selection requires information. The elicited information is unstructured and is “early.” This work is extended by proposing a method to directly convert early information into its multi-dimensional form. For this, an early information mode is proposed. The proposed conversion process is a fully automated one. Further, the tool support is extended to accommodate the conversion process. The authors also apply the method to a health domain.
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Chatziantoniou, Damianos, and George Doukidis. "Incorporating Data Stream Analysis into Decision Support Systems." In Encyclopedia of Information Science and Technology, First Edition. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-553-5.ch252.

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Traditional decision support systems (DSS) and executive information systems (EIS) gather and present information from several sources for business purposes. It is an information technology to help the knowledge worker (executive, manager, analyst) make faster and better decisions. So far, this data was stored statically and persistently in a database, typically in a data warehouse. Data warehouses collect masses of operational data, allowing analysts to extract information by issuing decision support queries on the otherwise discarded data. In a typical scenario, an organization stores a detailed record of its operations in a database, which is then analyzed to improve efficiency, detect sales opportunities, and so on. Performing complex analysis on this data is an essential component of these organizations’ businesses. Chaudhuri and Dayal (1997), present an excellent survey on decision-making and on-line analytical processing (OLAP) technologies for traditional database systems.
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Eder, Johann, and Karl Wiggisser. "Data Warehouse Maintenance, Evolution and Versioning." In Enterprise Information Systems. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-61692-852-0.ch301.

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Data Warehouses typically are building blocks of decision support systems in companies and public administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical Processing tools, which provide sophisticated features for aggregating and comparing data. Decision support applications depend on the reliability and accuracy of the contained data. Typically, a data warehouse does not only comprise the current snapshot data but also historical data to enable, for instance, analysis over several years. And, as we live in a changing world, one criterion for the reliability and accuracy of the results of such long period queries is their comparability. Whereas data warehouse systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for supporting changes is, first of all, being aware of their existence. Second, once you know that a change took place, it is important to know which change (i.e., knowing about differences between versions and relations between the elements of different versions). For data warehouses this means that changes are identified and represented, validity of data and structures are recorded and this knowledge is used for computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the common understanding and introduces the different aspects of data warehouse maintenance. Furthermore, several approaches addressing the problem are presented and classified by their capabilities.
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Conference papers on the topic "Decision support systems; Data warehouse; DW"

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Bondarev, Aleksey, Dilmurat Zakirov, and Dilmurat Zakirov. "Data warehouse on Hadoop platform for decision support systems in education." In 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO). IEEE, 2015. http://dx.doi.org/10.1109/icecco.2015.7416884.

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Yutao Zhou, Tao Ji, Lin Qiu, et al. "Implementation for EDW (enterprise data warehouse)-based decision support systems in Chongqing electric power in China." In 20th International Conference and Exhibition on Electricity Distribution (CIRED 2009). IET, 2009. http://dx.doi.org/10.1049/cp.2009.0656.

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Chen, I.-Chang, Shu-Keng Hsu, Teh-Juan Wu, et al. "RDSP: A Railway Decision Support Platform for Integrating and Bridging Existed Legacy Systems." In 2013 Joint Rail Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/jrc2013-2442.

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Railway system operation is a very complicated task and must be supported by the coordination of several systems including engineering, transportation, locomotive maintenance and management, ticket system and passengers service, etc. Ideally, a modern railway enterprise information system should be an integrated, consisted, and site-opened database to support railway system operation. However, multiple isolated applications instead of one integrated enterprise information system are often formed due to historical factors such as independent departments, budgetary constraint and application req
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Giat, Yahel, and Dan Bouhnik. "A Decision Support System for Pricing and Minimizing Product Returns." In InSITE 2020: Informing Science + IT Education Conferences: Online. Informing Science Institute, 2020. http://dx.doi.org/10.28945/4522.

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Aim/Purpose: The first goal is to develop a decision support system for pricing and production amounts for a firm facing high levels of product returns. The second goal is to improve the management of the product returns process. Background: A food importer and manufacturer in Israel with a significant product returns rate. Methodology: A decision support module was added to the plant’s information system. The module is based on a supply chain pricing model and uses the sales data to infer future demand’s distribution. Ergonomic models were used to improve the design of the returns warehouse a
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