ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Framework for selecting manufacturing simulation software in industry 4.0 environment

Even though the use of simulation software packages is widespread in industrial and manufacturing companies, the criteria and methods proposed in the scientific literature to evaluate them do not adequately help companies in identifying a package able to enhance the efficiency of their production system. Hence, the main objective of this paper is to develop a framework to guide companies in choosing the most suitable manufacturing simulation software package. The evaluation framework developed in this study is based on two different multi-criteria methods: analytic hierarchy process (AHP) integrated with benefits, opportunities, costs, risks (BOCR) analysis and the best-worst method (BWM). The framework was developed on the basis of the suggestions from the literature and from a panel of experts, both from academia and industry, trying to capture all the facets of the software selection problem. For testing purposes, the proposed approach was applied to a mid-sized enterprise located in the south of Italy, which was facing the problem of buying an effective simulation software for Participatory Design. From a practical perspective, the application showed that the framework is effective in identifying the most suitable simulation software package according to the needs of the company. From a theoretical point of view, the multi-criteria methods suggested in the framework have never been applied to the problem of selecting simulation software; their usage in this context could bring some advantages compared to other decision-making tools

Selecting SaaS CRM Solution for SMEs

The use of CRM system has helped many organisations to manage and analyze business interaction with customers in several ways. However, Small and Medium-sized Enterprises (SMEs) are often struggling to find an optimal solution to fit their business and financial conditions. When faced with different SaaS-based CRM solutions, SMEs need to save investments in IT infrastructure, the cost of CRM deployment, and the maintenance cost. To select the best solution for the SME is critical for the business. This paper look at What systematic approach SMEs needs to follow when choosing the best SaaS CRM solution that would suit their company business strategy? To answer this question, this study presents a comprehensive decision-making framework for SaaS CRM evaluation and selection approach using the Analytical Hierarchical Process (AHP). The proposed selection approach is also applied to an SME, Biyemfat Enterprise, a small private real estate company. A case study of selecting a SaaS CRM solution for Biyemfat Enterprise is presented with an evaluation of the cost-benefit analysis

Identifying innovation opportunities emerging from technology and business trends

PhD thesis in TechnologyBusinesses are continuously looking for opportunities to innovate. There is a gap in the literature regarding innovation models and approaches that are systematic, practical and easy to apply. This thesis addresses this issue through investigation of the potential for identifying innovation opportunities emerging from technology and business trends and of how to evaluate ideas. Technology trends depict the evolving direction of technology; can they be used to innovate products? Inspired by ideation literature, a novel model is presented which combines technology trends with product breakdown to generate product innovation ideas. The empirical evidence suggests that the model can generate quality ideas. Further investigation of technology trends indicated that the largest trend of the near future will be autonomation, which suggests that many products and services will be delivered in a totally autonomous way. Operations that can be standardized have a high likelihood of being autonomated in the near future. This is because sensor advancement has made it possible to install low-cost sensors on machines; these act as senses for the machine, and then the sensor data can be processed in digital algorithms to carry out fine-tuned decision-making for the machine. This enables a paradigm shift in how machines and applications are operated. The megatrend of electrification has dominated the past century, during which the leading inspiration for innovators was how to electrify industry, households, automobiles, etc. The inspiration for future innovation could likewise be how to autonomate the same. Anecdotal evidence supports this claim. A model and case study are presented in this regard. In a similar way to technology trends, business trends are also agents of change, revealing how businesses are evolving. The largest trend observed is servitization. Companies are gradually shifting away from the traditional model of selling just products towards selling the functionality of the products as services. The shelf life of many products has been significantly reduced, and there is stiff competition in the market. Services, on the other hand, are more sustainable. Servitization is here defined as reducing tangibility in the product. A utility-driven approach is developed, in which the products are broken down into the utility features that encourage the customer to purchase the product and barriers that prohibit the customer from purchasing the product. The model presented in the study presents options to gradually enhance utility and reduce both barriers and the overall tangibility of the product. That can assist users in transforming their products into services. Another way to servitize is to add services to a product in the form of product-service-system. Financing/ownership value added services are explored, and the changes they bring to the business model are studied. These services do not require changes to the product or technological development and can add service benefits to the product. A systematic framework is presented, in which the options can be individually evaluated, and suitable value-added service options can be selected. Another important business trend observed is outsourcing. Start-ups and high growth companies have limited resources, and they do not have the flexibility to carry out all business activities internally. Companies tend to outsource business activities, to survive with limited resources. However, sometimes outsourcing the core activities of the business can invite competition. In this thesis, a decision tree for evaluating business activities for outsourcing purposes is presented. The decision tree assists users in evaluating those activities that can be outsourced with minimal side effects for the business. Traditionally, ideas are screened based on subjective judgement after a brainstorming session. In this thesis, a systematic high-level idea screening tool is presented, which is useful for screening ideas in a short period of time. Six key parameters, which are producibility, problem size, market size, novelty, profit margin and business alignment, are pillars of the idea screening tool, compiled by assorting the idea screening literature. The tool is useful for screening the ideas generated in the aforementioned models. Together, the appended papers contribute to filling the gap in the innovation literature regarding practical guidelines to innovate businesses

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR COMPANIES IN THE ENERGY FROM BIOMASS AREA, APPLYING CIRCULAR ECONOMY PRINCIPLES WITH A LIFE CYCLE THINKING APPROACH

The biomass supply chain (BSC) for energy production has emerged as a promising alternative to traditional fossil fuels, playing a crucial role in mitigating climate change and promoting sustainable development. Biomass utilisation offers numerous environmental, economic, and social benefits, including reduced greenhouse gas (GHG) emissions, enhanced energy security, and job creation in rural areas, which are known as important aspects of sustainable development. Moreover, the use of waste, by-products, and residue in BSC is essential to improving the circular economy (CE) in agriculture, wood, and paper processing industries, as well as waste treatment and management. Therefore, to further harness the potential of biomass energy production in sustainability and transition to the CE context, it is significant for companies in the BSC to apply circular business models (CBM).While the role of biomass in the CE has been confirmed, the gap still exists in evaluating the application of CE to the BSC. Up to the authors’ knowledge, currently, there is no set of circularity and sustainability indicators as standard for the company in the BSC. The variety of CE approaches and indicators makes it difficult to convert linear business models into circular ones. In addition, the variety of biomass materials, differences in biomass processing technology and multiple end-products lead to transformation into a CE model in many alternatives with many stages and different technology processes. Furthermore, some indicators assessing aspects of sustainability and circularity of different alternatives are subject to conflict and trade-offs. A more sustainable solution might not necessarily be better in terms of circularity and similar trade-offs exist within the pillars of sustainability. Given the trade-offs between sustainability and circularity, decision support systems (DSS) based on life cycle thinking with a standard set of indicators are promising tools for evaluating and selecting the best alternative of sustainability and circularity BSC.For what is above, this PhD research project was focused on developing a decision support system for a biomass company in the energy sector based on CE and sustainability models with a life cycle thinking approach. With the CE and sustainability model, a set of circularity and sustainability indicators is developed, and it is considered a criteria set to assess the circularity and sustainability of biomass companies and BSC. The life cycle thinking approach is employed to provide a comprehensive assessment for BSC. It is also basic to collect data from BSC and give value to indicators for assessing and ranking alternatives. The trade-off existing in alternatives is solved by using Multiple-criteria decision-making methods. That is integrated into the methodology framework of the decision support system.The PhD research project is structured around two main objectives. First, from CE and sustainability models, a set of circularity and sustainability is development. Secondly, a DSS tool is created. The set of developed indicators considers various stages during the BSC, such as feedstock plantation, processing, transportation, energy conversion, and end-of-life management, being aligned with the United Nations Sustainable Development Goals (SDGs) and the EC’s guidelines on the transition to CE. Meanwhile, the creation of a DSS includes proposing a methodology framework for DSS, creating software in MATLAB GUI and Script as a new tool for DSS, and applying this tool to the rice straw supply chain as testing for the case study.Regarding the case study, a rice straw supply chain for energy production in the Pavia region of Italy is selected. The data for the case study was collected during the internship period at the ENI company, such as parameters of the plant and process. The current of the rice straw supply chain is assessed by the DSS tool, and a re-edited version of this tool was taken. The alternatives of CE applications in the case study were performed through an external internship at the IMDEA Energy Institute (Spain). The data on alternatives is gathered based on the results of the simulation of the chemical process by Aspen plus@ at the IMDEA Energy Institute for suitable parameters of the current supply chain. The sustainability and circularity indicators methodology framework and case study developed during this PhD research project have been published in international journals and conference proceedings. The results of the application and details of the decision support system are present in this thesis. The results of calculating indicators for all indicators show that global warming potential (GWP) is 1.21E+03 ton CO2eq/yr to 55.7E+03 ton CO2eq/yr. Meanwhile, rice straw's acidification potential (AP) in this study ranges from 9.66 tonnes of SO2 eq/yr to 563 tonnes of SO2 eq/yr. The internal rate of return (IRR) of the rice straw supply chain is from 5.92% to 11.3%. In addition, the net present value (NPV) of the case study ranges from 0.72 to 5.79 million euros. Furthermore, the rate of informal labour is from 71.9% to 82.10%, while the percentage of recycling rate out of all waste is from 96.61% to 99.2%, the circular material use is from 54.8% to 88.2%, and the proportion of material losses in primary material is from 14.61% to 15.5%. The ranking results indicate that the digestate pyrolysis option has the best sustainability and circularity points among the other options.This PhD project research shows that the application of a comprehensive approach encompassing Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (SLCA) to identify sustainability indicators brings about significant advantages to the biomass supply chain. Existing research seldom integrates all three methodologies simultaneously. This integrated approach enhances the understanding of sustainability implications across the biomass supply chain, paving the way for a more holistic assessment.Moreover, the utilization of the Life Cycle Thinking (LCT) tool and Material Flow Analysis (MFA) for circularity indicators introduces a novel dimension to the existing literature. The incorporation of these tools instills confidence in simulating both circularity and sustainability, a consideration often overlooked in previous studies. The resulting circularity and sustainability indicators offer a standardised set that serves as a step-by-step guide for achieving Sustainable Development Goals (SDGs) and transitioning to a circular economy, aligning with the European Commission's roadmap.The development of a Decision Support System (DSS) methodology framework marks another crucial contribution, particularly by integrating circularity and sustainability within a unified framework for biomass companies in the supply chain. Unlike existing frameworks, this approach employs the PROMETHEE II and Entropy methods, leveraging life cycle results to enhance reliability and streamline calculations. Overcoming the limitations of PROMETHEE, this framework incorporates a multiple-criteria decision-making approach to address trade-offs in sustainability and circularity alternatives. This not only improves the robustness of the framework but also extends its applicability to general companies beyond the biomass sector.Furthermore, the accompanying software in this study presents a more practical and potent DSS tool for ranking alternatives. Its flexibility, allowing the use of the DSS tool for calculating sustainability and circularity indicators for individual alternatives, provides users with a versatile platform. The ability to choose indicator groups and methods for weighting indicators enhances the adaptability of the framework, making it applicable in various scenarios for policymakers and researchers committed to advancing circular economy and sustainability initiatives. In summary, based on methods for application, methodology framework and useful software, the DSS tool developed in this thesis can be used to support companies in the biomass supply chain, managers, practitioners, policy-makers, and researchers in assessing and selecting alternatives for application of CBMs to transfer into CE

DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR COMPANIES IN THE ENERGY FROM BIOMASS AREA, APPLYING CIRCULAR ECONOMY PRINCIPLES WITH A LIFE CYCLE THINKING APPROACH

The biomass supply chain (BSC) for energy production has emerged as a promising alternative to traditional fossil fuels, playing a crucial role in mitigating climate change and promoting sustainable development. Biomass utilisation offers numerous environmental, economic, and social benefits, including reduced greenhouse gas (GHG) emissions, enhanced energy security, and job creation in rural areas, which are known as important aspects of sustainable development. Moreover, the use of waste, by-products, and residue in BSC is essential to improving the circular economy (CE) in agriculture, wood, and paper processing industries, as well as waste treatment and management. Therefore, to further harness the potential of biomass energy production in sustainability and transition to the CE context, it is significant for companies in the BSC to apply circular business models (CBM).While the role of biomass in the CE has been confirmed, the gap still exists in evaluating the application of CE to the BSC. Up to the authors’ knowledge, currently, there is no set of circularity and sustainability indicators as standard for the company in the BSC. The variety of CE approaches and indicators makes it difficult to convert linear business models into circular ones. In addition, the variety of biomass materials, differences in biomass processing technology and multiple end-products lead to transformation into a CE model in many alternatives with many stages and different technology processes. Furthermore, some indicators assessing aspects of sustainability and circularity of different alternatives are subject to conflict and trade-offs. A more sustainable solution might not necessarily be better in terms of circularity and similar trade-offs exist within the pillars of sustainability. Given the trade-offs between sustainability and circularity, decision support systems (DSS) based on life cycle thinking with a standard set of indicators are promising tools for evaluating and selecting the best alternative of sustainability and circularity BSC.For what is above, this PhD research project was focused on developing a decision support system for a biomass company in the energy sector based on CE and sustainability models with a life cycle thinking approach. With the CE and sustainability model, a set of circularity and sustainability indicators is developed, and it is considered a criteria set to assess the circularity and sustainability of biomass companies and BSC. The life cycle thinking approach is employed to provide a comprehensive assessment for BSC. It is also basic to collect data from BSC and give value to indicators for assessing and ranking alternatives. The trade-off existing in alternatives is solved by using Multiple-criteria decision-making methods. That is integrated into the methodology framework of the decision support system.The PhD research project is structured around two main objectives. First, from CE and sustainability models, a set of circularity and sustainability is development. Secondly, a DSS tool is created. The set of developed indicators considers various stages during the BSC, such as feedstock plantation, processing, transportation, energy conversion, and end-of-life management, being aligned with the United Nations Sustainable Development Goals (SDGs) and the EC’s guidelines on the transition to CE. Meanwhile, the creation of a DSS includes proposing a methodology framework for DSS, creating software in MATLAB GUI and Script as a new tool for DSS, and applying this tool to the rice straw supply chain as testing for the case study.Regarding the case study, a rice straw supply chain for energy production in the Pavia region of Italy is selected. The data for the case study was collected during the internship period at the ENI company, such as parameters of the plant and process. The current of the rice straw supply chain is assessed by the DSS tool, and a re-edited version of this tool was taken. The alternatives of CE applications in the case study were performed through an external internship at the IMDEA Energy Institute (Spain). The data on alternatives is gathered based on the results of the simulation of the chemical process by Aspen plus@ at the IMDEA Energy Institute for suitable parameters of the current supply chain. The sustainability and circularity indicators methodology framework and case study developed during this PhD research project have been published in international journals and conference proceedings. The results of the application and details of the decision support system are present in this thesis. The results of calculating indicators for all indicators show that global warming potential (GWP) is 1.21E+03 ton CO2eq/yr to 55.7E+03 ton CO2eq/yr. Meanwhile, rice straw's acidification potential (AP) in this study ranges from 9.66 tonnes of SO2 eq/yr to 563 tonnes of SO2 eq/yr. The internal rate of return (IRR) of the rice straw supply chain is from 5.92% to 11.3%. In addition, the net present value (NPV) of the case study ranges from 0.72 to 5.79 million euros. Furthermore, the rate of informal labour is from 71.9% to 82.10%, while the percentage of recycling rate out of all waste is from 96.61% to 99.2%, the circular material use is from 54.8% to 88.2%, and the proportion of material losses in primary material is from 14.61% to 15.5%. The ranking results indicate that the digestate pyrolysis option has the best sustainability and circularity points among the other options.This PhD project research shows that the application of a comprehensive approach encompassing Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (SLCA) to identify sustainability indicators brings about significant advantages to the biomass supply chain. Existing research seldom integrates all three methodologies simultaneously. This integrated approach enhances the understanding of sustainability implications across the biomass supply chain, paving the way for a more holistic assessment.Moreover, the utilization of the Life Cycle Thinking (LCT) tool and Material Flow Analysis (MFA) for circularity indicators introduces a novel dimension to the existing literature. The incorporation of these tools instills confidence in simulating both circularity and sustainability, a consideration often overlooked in previous studies. The resulting circularity and sustainability indicators offer a standardised set that serves as a step-by-step guide for achieving Sustainable Development Goals (SDGs) and transitioning to a circular economy, aligning with the European Commission's roadmap.The development of a Decision Support System (DSS) methodology framework marks another crucial contribution, particularly by integrating circularity and sustainability within a unified framework for biomass companies in the supply chain. Unlike existing frameworks, this approach employs the PROMETHEE II and Entropy methods, leveraging life cycle results to enhance reliability and streamline calculations. Overcoming the limitations of PROMETHEE, this framework incorporates a multiple-criteria decision-making approach to address trade-offs in sustainability and circularity alternatives. This not only improves the robustness of the framework but also extends its applicability to general companies beyond the biomass sector.Furthermore, the accompanying software in this study presents a more practical and potent DSS tool for ranking alternatives. Its flexibility, allowing the use of the DSS tool for calculating sustainability and circularity indicators for individual alternatives, provides users with a versatile platform. The ability to choose indicator groups and methods for weighting indicators enhances the adaptability of the framework, making it applicable in various scenarios for policymakers and researchers committed to advancing circular economy and sustainability initiatives. In summary, based on methods for application, methodology framework and useful software, the DSS tool developed in this thesis can be used to support companies in the biomass supply chain, managers, practitioners, policy-makers, and researchers in assessing and selecting alternatives for application of CBMs to transfer into CE

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

A Systematic Mapping Review of Software Quality Measurement: Research Trends, Model, and Method

Software quality is a key for the success in the business of information and technology. Hence, before be marketed, it needs the software quality measurement to fulfill the user requirements.  Some methods of the software quality analysis have been tested in a different perspective, and we have presented the software method in the point of view of users and experts. This study aims to map the method of software quality measurement in any models of quality. Using the method of Systematic Mapping Study, we did a searching and filtering of papers using the inclusion and exclusion criteria. 42 relevant papers have been obtained then. The result of the mapping showed that though the model of ISO SQuaRE has been widely used since the last five years and experienced the dynamics, the researchers in Indonesia still used ISO9126 until the end of 2016.The most commonly used method of the software quality measurement Method is the empirical method, and some researchers have done an AHP and Fuzzy approach in measuring the software quality

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

A dynamic systems engineering methodology research study. Phase 2: Evaluating methodologies, tools, and techniques for applicability to NASA's systems projects

A study of NASA's Systems Management Policy (SMP) concluded that the primary methodology being used by the Mission Operations and Data Systems Directorate and its subordinate, the Networks Division, is very effective. Still some unmet needs were identified. This study involved evaluating methodologies, tools, and techniques with the potential for resolving the previously identified deficiencies. Six preselected methodologies being used by other organizations with similar development problems were studied. The study revealed a wide range of significant differences in structure. Each system had some strengths but none will satisfy all of the needs of the Networks Division. Areas for improvement of the methodology being used by the Networks Division are listed with recommendations for specific action