Liu, S., Delibasic, B., Butel, L. and Han, X. (2017), "Sustainable knowledge-based decision support systems (DSS): perspectives, new challenges and recent advance", Industrial Management & Data Systems, Vol. 117 No. 7, pp. 1318-1322. https://doi.org/10.1108/IMDS-04-2017-0137Download as .RIS
Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited
1. Sustainable knowledge-based decision support systems (DSS): perspectives, new challenges and recent advance
1.1 Evolution of decision making and DSS
Evidence has clearly shown that excellent business performance can only be achieved based on the right decisions (Liu et al., 2013). The ability to make good decisions is the mark of successful and promotable business leaders and managers (Martinsons and Davison, 2007). Research on decision making can be traced back to the preceding work in two main research streams: theoretical study of organisational decision making undertaken by Simon et al. at the Carnegie Institute of Technology during late 1950s and early 1960s (Simon, 1960), and technical work on interactive computer systems carried out by Gerrity et al. at the MIT in 1960s (Gerrity, 1971). Simon’s three-stage human decision-making process (i.e. intelligence, design and choice) is still one of the most widely cited decision models. However, Gerrity’s work has identified a key issue with human decision making, that is, the fact that there are many constraints on effective decision making, for example, with limited information and limited decision analysis ability. Along with the fast IT advancement over 1970s and 1980s, it has been widely recognised that computers could be used to overcome many human limitations. Subsequently, decision support systems (DSS), defined as an interactive computer-based system to support solving decision problems, have been developed and widely applied in real-world decisions (Shim et al., 2002). DSS was considered to be one of the most popular research areas in information systems during 1980s. Most notably, DSS have evolved from supporting individual decisions to supporting groups and then to supporting organisation-wide decisions (Liu et al., 2009). In addition, the types of decisions that DSS are able to support extend from operational to strategic decision making (Martinsons and Davison, 2007). A number of review papers can be found which have embraced the DSS success over the time (Keen, 1987; Eom, 1999; Carlsson and Turban, 2002; Shim et al., 2002; Liu et al., 2010).
However, DSS have entered into a relatively difficult time in 1990s, because DSS users were no longer satisfied by merely searching for information and obtaining analysis results from running a decision model (Liu et al., 2010). Based on experience in practice, decision makers gradually realised, apart from information and decision analysis, the human element was found to be lacking sufficient knowledge and expertise in order to make faster and more consistent decisions (Bolloju et al., 2002). In response to the new issue identified within human decision-making process, many scholars and researchers invested a significant amount of effort to search for solutions, which resulted in the emergence of knowledge-based systems (KBS), which is also termed as expert systems in America (Dhar and Stein, 1997). Since 1990s, KBS and expert systems have been playing an important role in the new generation of DSS, which led to the development of knowledge-based decision support systems (KB-DSS) (Courtney, 2001). KB-DSS have been generally accepted as decision systems that contain a knowledge base and have a function of inference or reasoning on top of a classical DSS. A comprehensive review of KB-DSS is available from the guest editors’ recent publication (Zarate and Liu, 2016). A special issue (SI) focussed on the technology perspective of KB-DSS is published earlier in the Journal of Decision Systems (Liu et al., 2014). However, the current issues of global sustainability and business performance improvement have presented brand new challenges to decision making and to KB-DSS.
2. New challenges
In recent years, business management has been facing new challenges because of the increasing importance of incorporating the issue of sustainability into the decision-making process, which requires innovative measures and metrics to assess the business performance from triple bottom line of perspective, the environment, the social and the economic perspective (Denzer, 2005). It has been widely accepted that business performance is greatly influenced by the decisions made on the strategy, design, planning and control and continuous improvement of all business operations (Chiou et al., 2011). Making the right decisions to ensure excellent business performance remains a challenging task because of the variety, complexity, uncertainty and dynamics of decisions (Liu et al., 2013). In literature, decision support for business performance management has been discussed in more or less a fragmented manner, e.g. using traditional decision methods such as decision trees, linear programming, analytic hierarchical process, analytic network process and other multi-criteria decision analysis methods. The methods have been employed mainly for classic economic performance management but are less frequently applied to measuring social and environmental outcomes (Chan et al., 2003).
Today in a highly competitive business environment, decisions need to be made more quickly with better precision, supported by proven knowledge and expertise, using state-of-the-art DSS technologies such as innovative KB-DSS, which can adapt to new decision environment surrounded by artificial intelligence, semantic web, social media and “Big Data” analytics (Delibasic et al., 2016). Furthermore, the ultimate aim of developing and employing KB-DSS is to improve decision performance in real industrial/life decision cases (Zarate and Liu, 2016). A gap in literature exists especially in the area of exploring advanced KB-DSS which address the current environmental sustainability issues supporting the reasoning and learning capabilities of decision making and improving business performance in real industrial environment. This SI intends to fill this gap by providing a collection of papers with empirical evidence from industries to make a concerted effort to discuss innovative KB-DSS that aims to help address all aspects of performance measures in business in a coherent manner.
3. Perspectives and recent advances
This SI includes 12 papers addressing the new challenges presented to KB-DSS by the sustainability and business performance requirements. The SI represents the most recent advances in the topic from different perspectives, contributing to real industrial applications in a wide variety of businesses. A number of state-of-the art knowledge management technologies are explored in the SI, including data mining, machine learning, rule-based reasoning, taxonomy, clustering, ontologies and other artificial intelligence (Dhar and Stein, 1997; Zarate and Liu, 2016). In terms of decision technologies incorporated, the SI has achieved consensus on utilisation of multi-criteria decision analysis, mainly because businesses need to address all three pillars of the sustainability (i.e. the environmental, economic and social dimensions) concurrently (Wang et al., 2012). The applications supported by KB-DSS reported in this SI can be classified in four clear streams: with four papers on sustainable supply chain and logistics, three papers on business process improvement and ecosystems, three papers in product, service and project development, and two papers on knowledge and information systems implementation. Table I gives an overview of the papers.
4. Concluding remarks
As Guest Editors, we are glad to have included in this SI a set of 12 high quality and interesting pieces of work, authored by experienced researchers from different institutions across all continents. Editing this SI has been a great pleasure for us. We would like to thank a number of people who have greatly contributed to the successful completion of this SI. First, we need to thank the Editors-in-Chief of IMDS, Professor Hing Kai Chan and Dr Alain Yee Loong Chong, for giving us the opportunity to edit the SI with a topic we are passionate about. Consistent support and timely advice from Professor Chan over the SI editing process is highly appreciated. Thanks also go to colleagues in the Emerald Publishing for setting up the SI submission system through ScholarOne and advertising the SI Call for Papers online. Of course, we would also like to express our gratitude to the reviewers of this SI, for their constructive feedback on the submissions. Last but not least, we wish to sincerely acknowledge the great help from our fellow colleagues on the EWG-DSS Co-ordination Board, namely, Pascale Zarate, Fátima Dargam, Rita Ribeiro, Isabelle Linden and Jason Papathanasiou. They have offered their unreserved support to this SI from the initial topic design through review process to the final decision on the papers included in the SI. This SI would not have been possible without the wonderful support and help from all of them!
Classification of the papers included in the SI
|Theme||Paper||Lead author||Key knowledge management and decision methods/technologies|
|Sustainable supply chain and logistics||Decision support systems for sustainable logistics: a review and bibliometric analysis||Fahham Hasan Qaiser||Review paper, covered wide range of methods and technologies|
|Identification and selection of ICTs for freight transport in product service supply chain diversification||José Moreno-Jiménez||Taxonomy, technology tree, AHP|
|Virtual Enterprise formation in the context of a sustainable partner network||Eduard Shevtshenko||Fuzzy AHP, TOPSIS|
|An intelligent approach to big data analytics for sustainable retail environment using a priori map reduce framework||Neha Verma||Big data analytics, a priori association mining|
|Business process and eco-system||A positive deviance approach to eliminate wastes in business processes: the case of a public organization||Pavlos Delias||Clustering, generalised regression|
|Knowledge sharing and collaborative relationships in business ecosystems and networks – a definition and a demarcation||Anna Wulf||Knowledge sharing, clustering and networking, organisational learning|
|Decision-making framework with double-loop learning through interpretable black-box machine learning models||Marko Bohanec||Double-loop learning, machine learning, data mining, CRISP-DM|
|Product, service and project development||A semantic based decision support platform to assist products’ eco-labelling process||Da Xu||Ontologies, SWRL, RDF, OWL|
|An MCDM project portfolio web-based DSS for business strategic performance improvement||Carolina Martins||MCDA, web-based DSS|
|Development of an intelligent e-Healthcare system for the domestic care industry||Bennie Wong||Data mining, decision trees, a priori, rule-based reasoning|
|Knowledge and information systems implementation||ERP knowledge prioritisation for business performance improvement: perspectives of clients and implementation partners in UK industries||Uchitha Jayawickrama||ERP, AHP, knowledge prioritisation|
|Effects of conventional method in precast concrete building maintenance: towards BIM implementation||Zul-Atfi Ismail||BIM|
Bolloju, N., Khalifa, M. and Turban, E. (2002), “Integrating knowledge management into enterprise environments for the next generation decision support”, Decision Support Systems, Vol. 33 No. 2, pp. 163-176.
Carlsson, C. and Turban, E. (2002), “DSS: directions for the next decade”, Decision Support Systems, Vol. 33 No. 2, pp. 105-110.
Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W. and Ip, R.W.L. (2003), “A conceptual model of performance measurement for supply chains”, Management Decision, Vol. 41 No. 7, pp. 635-642.
Chiou, T.Y., Chan, H.K., Lettice, F. and Chung, S.H. (2011), “The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan”, Transportation Research Part E: Logistics and Transportation Review, Vol. 47 No. 6, pp. 822-836.
Courtney, J.F. (2001), “Decision making and knowledge in inquiring organisations: a new decision making paradigm for DSS”, Decision Support Systems, Vol. 31 No. 1, pp. 17-38.
Delibasic, B., Hernandez, J.E., Papathanasiou, J., Dargam, F., Zarate, P., Ribeiro, R., Liu, S. and Linden, I. (2016), Decision Support Systems V – Big Data Analytics for Decision Making, Lecture Notes in Business Information Processing 216, Springer, Cham Heidelberg, New York, NY, Dordrecht and London.
Denzer, R. (2005), “Generic integration of environmental decision support systems – state of the art”, Environ Model Software, Vol. 20 No. 10, pp. 1217-1223.
Dhar, V. and Stein, R. (1997), Intelligent Decision Support Methods: The Science of Knowledge Work, Prentice Hall Inc., NJ.
Eom, S.B. (1999), “Decision support systems research: current state and trends”, Industrial Management and Data Systems, Vol. 99 No. 5, pp. 213-220.
Gerrity, T.P. (1971), “The design of machine decision systems: an application to portfolio management”, Sloan Management Review, Vol. 12 No. 2, pp. 59-75.
Keen, P. (1987), “Decision support systems: the next decade”, Decision Support Systems, Vol. 3 No. 3, pp. 253-265.
Liu, S., Duffy, A.H.B., Whitfield, R.I. and Boyle, I.M. (2010), “Integration of decision support systems to improve decision support performance”, Knowledge and Information Systems – An International Journal, Vol. 22 No. 3, pp. 261-286.
Liu, S., Duffy, A.H.B., Whitfield, R.I., Boyle, I.M. and McKenna, I. (2009), “Towards the realisation of an integrated decision support environment for organisational decision making”, International Journal of Decision Support Systems Technology, Vol. 1 No. 4, pp. 38-58.
Liu, S., Leat, M., Moizer, J., Megicks, P. and Kasturiratne, D. (2013), “A decision-focused knowledge framework to support collaborative decision making for lean supply chain management”, International Journal of Production Research, Vol. 51 No. 7, pp. 2123-2137.
Liu, S., Zarate, P. and Ribeiro, R. (2014), “Guest editorial on knowledge-based decision systems”, Journal of Decision Systems, Vol. 23 No. 3, pp. 229-231.
Martinsons, M.G. and Davison, R.M. (2007), “Strategic decision making and support systems: comparing American, Japanese and Chinese management”, Decision Support Systems, Vol. 43 No. 1, pp. 284-300.
Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R. and Carlsson, C. (2002), “Past, present, and future of decision support technology”, Decision Support Systems, Vol. 33 No. 2, pp. 111-126.
Simon, H.A. (1960), The New Science of Management Decision, Harper Brothers, New York, NY.
Wang, X., Chan, H.K., Yee, R.W.Y. and Diaz-Rainey, I. (2012), “A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain”, International Journal of Production Economics, Vol. 135 No. 2, pp. 595-606.
Zarate, P. and Liu, S. (2016), “A new trend for knowledge-based decision support systems design”, International Journal of Information and Decision Sciences, Vol. 8 No. 3, pp. 305-324.
About the authors
Shaofeng Liu is a Professor of Operations Management and Decision Making at the University of Plymouth, UK. She is the Research Director for the Product and Service Value Chain Group, specializing in digital business, knowledge management, decision making and value chain innovation. She obtained her PhD Degree from the Loughborough University, UK. She sits on the Management Board for Euro Working Group on Decision Support Systems. She is currently a Senior Editor for Cogent Business and Management, and on editorial boards for a number of international journals. She has undertaken a number of influential research projects funded by UK research councils and the European Commission with a total value over €40M. She is currently the Principal Investigator and Co-investigator for four EU projects (three of which are funded by Horizon 2020 and one by Erasmus Plus) and one project funded by UK research council – Innovate UK. She has published over 150 peer-reviewed research papers. For more details, please refer to her professional website: www.plymouth.ac.uk/staff/sliu1. Shaofeng Liu is the corresponding author and can be contacted at: email@example.com
Boris Delibasic obtained his PhD Degree from Business Decision Making from the University of Belgrade. Boris is an internationally renowned Scholar in the area of data mining, knowledge-based systems and their application to organisational decision making. He is a Member of the Co-ordination Board for EWG-DSS. He has edited the Springer book series Decision Support Systems I-V, with book IV on “Information and Knowledge Management in Decision Processes and book V on Big Data Analytics for Decision Making published recently. He has guested edited a number of special issues for decision related journals. He was the Local Chair for 2015 International Conference on Decision Support System Technology (ICDSST 2015).
Lynne Butel has over 35 years of experience in teaching and researching business performance management and business strategy. She has undertaken 15 knowledge transfer research projects funded by UK governments, research councils and industries. Lynne’s research has achieved great success in solving practical business problems in real industrial environment. She is currently supervising five PhD students and has supervised many PhD students to successful completion. She has published widely, including in high-quality international journals, and also published a book on Business Functions (by Blackwell).
Dr Xue Han over the past 20 years has been undertaking research in the sustainable decision systems and published in high-quality international journals such as Journal of Business Research. She has completed a number of influential research projects commissioned by both Canadian and Chinese Governments in exploring green technologies to address the current industrial issues to improve business performance. She has edited a number of important publications for Chinese Academy of Engineering which achieved high impact.