A study on the effects of inter-organizational factors on the supply chain performance

In the current competitive environment, managers do their best to convert organizations under their supervision into competitive and responsive through creating capability of timely delivery of quality products and services. In the other word, they try to create value for their customers, which yield more profitability for stakeholders. In line with this, determining of inter-organizational factors and the relationships among these variables and supply chain performance plays an important role in achieving these objectives. The relationship modeling is a type of multiple criteria decision-making (MCDM) problem, which requires applying experts to determine the relationships. The Decision Making Trial and Evaluation Laboratory (DEMATEL) is an MCDM tool, which not only can convert the relationships among cause and effect criteria into a visual structural framework, but also it can be used as a technique to handle the inner dependences within a set of criteria. This paper proposes an effective solution based on DEMATEL approach to help managers evaluate the relationships between inter-organizational factors and supply chain performance

Tool for Identifying Working Style Based on Event Logs

Äriinfotehnoloogias on tööstiil küllaltki uus uurimusteema. See on tihedalt seotud teistest uurimisvaldkondadest tuntud kontseptsioonidega nagu näiteks organisatsiooniline kaevandamine, visuaalne analüüs ning mustrituvastus. Organisatsioonikaevet abistavaid tööriistu on küll mitmeid, kuid nad on kõik üldiseks otstarbeks eraldiseisvad programmid ning ükski ei spetsialiseeru süviti just tööstiili tuvastamisele. Antud uurimustöö tulemusena valmisdomeenispetsiifiline veebipõhine tööriist. mis aitab kasutajal tuvastada tööstiili identifikaatoreid. Kasutajal on antud keskkonda võimalik importida vaadeldavate isikute tegevuste ajalugu, vaadelda neid andmeid ning koostada päringuid tuvastamaks tööstiili mustreid. Lisaks funktsionaalsetele ning tehnilistele nõuetele on kaetud ka tööriista teostuse üksikasjad.Lisaks arutleb autor arenduse käigus tekkinud raskuste üle ning kirjeldab tööriista valmistamisel kasutatud tehnoloogiates leitud puudujääkide hetkeseisu. Lõputöös tehakse ka mitmeid ettepanekuid tööriista täiendamiseks edasistes töödes ning demonstreeritakse näiteandmestiku abil tööriista kasutamist, uurides programmi erinevaid võimalusi ning aspekte.Working style is a relatively new topic in Business Informatics. It is strictly related to concepts existing in other areas of the research such as Organizational Mining, Visual analytics, Pattern recognition. While there are several tools to tackle the task of organizational mining, they are general purpose stand-alone desktop applications, and none of them concentrate solely and deeply on working style identification. To this end, this thesis introduces a domain-specific web-based tool that aids the user with working styleidentification. The tool allows end users to import event logs. They can explore and query patterns to answer the questions related to the working style of the human actors involved in that event logs. The thesis presents, functional and technical requirements as well as the implementation details of the tool. Also, discusses the difficulties faced during the development and the gaps that have been identified in the current state of practice of the technologies that were used for implementation. Moreover, several possible extensions of the tool are suggested that could be addressed in the future works. Finally, the thesisdemonstrates the usage of the tool by importing the sample data and exploring different capabilities and aspects of the application

Prediction in Economic Networks: Using the Implicit Gestalt in Product Graphs

We define an economic network as a linked set of products, where links are created by realizations of shared outcomes between entities. We analyze the predictive information contained in an increasingly prevalent type of economic network, a “product network” that links the landing pages of goods frequently co-purchased on e-commerce websites. Our data include one million books in 400 categories spanning two years, with over 70 million observations. Using autoregressive and neural-network models, we demonstrate that combining historical demand of a product with that of its neighbors improves demand predictions even as the network changes over time. Furthermore, network properties such as clustering and centrality contribute significantly to predictive accuracy. To our knowledge, this is the first large-scale study showing that a non-static product network contains useful distributed information for demand prediction, and that this information is more effectively exploited by integrating composite structural network properties into one’s predictive models

On the Design of IT Artifacts and the Emergence of Business Processes as Organizational Routines

Much of the BPM literature views business process design and implementation as a top-down process that is built on strategic alignment and managerial control. This view is inconsistent with the observation that information infrastructures, including a company’s business process infrastructure, are at drift, a term that refers to the lack of top-down management control. The paper contributes to resolving this inconsistency by developing a framework that conceptualizes business processes as emergent organizational routines that are represented, enabled, and constrained by IT artifacts. IT artifacts are developed in processes of functional-hierarchical decomposition and social design processes. Organizational routines have ostensive and performative aspects, forming a mutually constitutive duality. A literature review demonstrates that the propositions offered by the framework have been insufficiently considered in the BPM field. The paper concludes with an outlook to applying the framework to theorizing on the emergence of business processes on online social network sites

A Comparative Study of Dimensionality Reduction Techniques to Enhance Trace Clustering Performances

Technology Management/ Information System/ EntrepreneurshipProcess mining aims at extracting useful information from event logs. Recently, in order to improve processes, several organizations such as high-tech companies, hospitals, and municipalities utilize process mining techniques. Real-life process logs from such organizations are usually very large and complicated, since the process logs in general contain numerous activities which are executed by many employees. Furthermore, lots of real-life process logs generate spaghetti-like process models due to the complexity of processes. Traditional process mining techniques have problems with discovering and analyzing real-life process logs which come from less structured processes. To overcome the weaknesses of traditional process mining techniques, a trace clustering has been developed. The trace clustering splits an event log into several subsets, and each subset contains homogenous cases. Even though the trace clustering is useful to handle complex process logs, it is time-consuming and computationally expensive due to a large number of features generated from complex logs. In this thesis, we applied dimensionality reduction (preprocessing) techniques to the trace clustering in order to reduce the number of features. To validate our approach, we conducted experiments to discover relationships between dimensionality reduction techniques and clustering algorithms, and we performed a case study which involves patient treatment processes of a hospital. Among many dimensionality reduction techniques, we used three techniques namely singular value decomposition (SVD), random projection, and principal components analysis (PCA). The result shows that the trace clustering with dimensionality reduction techniques produce higher average fitness values. Furthermore, processing time of trace clustering is effectively reduced with dimensionality reduction techniques. Moreover, we measured similarity between clustering results to observe the degree of changes in clustering results while applying dimensionality reduction techniques. The similarity is resulted differently according to used clustering algorithm.ope

Customer process management A framework for using customer-related data to create customer value

Purpose The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services. Design/methodology/approach This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature. Findings The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature. Originality/value "Using data to advance service" is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company's use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data

Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining

[EN] In order to improve the efficiency and effectiveness of Emergency Rooms (ER), it is important to provide answers to frequently-posed questions regarding all relevant processes executed therein. Process mining provides different techniques and tools that help to obtain insights into the analyzed processes and help to answer these questions. However, ER experts require certain guidelines in order to carry out process mining effectively. This article proposes a number of solutions, including a classification of the frequently-posed questions about ER processes, a data reference model to guide the extraction of data from the information systems that support these processes and a question-driven methodology specific for ER. The applicability of the latter is illustrated by means of a case study of an ER service in Chile, in which ER experts were able to obtain a better understanding of how they were dealing with episodes related to specific pathologies, triage severity and patient discharge destinations.This project was partially funded by Fondecyt Grants 1150365 and 11130577 from the Chilean National Commission on Scientific and Technological Research (CONICYT), the Ph.D. Scholarship Program of CONICYT Chile (CONICYT-Doctorado Nacional/2014-63140180), the Ph.D. Scholarship Program of CONICIT Costa Rica and by Universidad de Costa Rica Professor Fellowships.Rojas, E.; Sepúlveda, M.; Munoz-Gama, J.; Capurro, D.; Traver Salcedo, V.; Fernández Llatas, C. (2017). Question-Driven Methodology for Analyzing Emergency Room Processes Using Process Mining. Applied Sciences. 7(3):1-29. https://doi.org/10.3390/app7030302S12973Welch, S. J., Asplin, B. R., Stone-Griffith, S., Davidson, S. J., Augustine, J., & Schuur, J. (2011). Emergency Department Operational Metrics, Measures and Definitions: Results of the Second Performance Measures and Benchmarking Summit. Annals of Emergency Medicine, 58(1), 33-40. doi:10.1016/j.annemergmed.2010.08.040Jansen-Vullers, M., & Reijers, H. (2005). Business Process Redesign in Healthcare: Towards a Structured Approach. INFOR: Information Systems and Operational Research, 43(4), 321-339. doi:10.1080/03155986.2005.11732733Grol, R., & Grimshaw, J. (1999). Evidence-Based Implementation of Evidence-Based Medicine. The Joint Commission Journal on Quality Improvement, 25(10), 503-513. doi:10.1016/s1070-3241(16)30464-3Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Radnor, Z. J., Holweg, M., & Waring, J. (2012). Lean in healthcare: The unfilled promise? Social Science & Medicine, 74(3), 364-371. doi:10.1016/j.socscimed.2011.02.011Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Neumuth, T., Jannin, P., Schlomberg, J., Meixensberger, J., Wiedemann, P., & Burgert, O. (2010). Analysis of surgical intervention populations using generic surgical process models. International Journal of Computer Assisted Radiology and Surgery, 6(1), 59-71. doi:10.1007/s11548-010-0475-yFernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Partington, A., Wynn, M., Suriadi, S., Ouyang, C., & Karnon, J. (2015). Process Mining for Clinical Processes. ACM Transactions on Management Information Systems, 5(4), 1-18. doi:10.1145/2629446Basole, R. C., Braunstein, M. L., Kumar, V., Park, H., Kahng, M., Chau, D. H. (Polo), … Thompson, M. (2015). Understanding variations in pediatric asthma care processes in the emergency department using visual analytics. Journal of the American Medical Informatics Association, 22(2), 318-323. doi:10.1093/jamia/ocu016Suriadi, S., Andrews, R., ter Hofstede, A. H. M., & Wynn, M. T. (2017). Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs. Information Systems, 64, 132-150. doi:10.1016/j.is.2016.07.011De Medeiros, A. K. A., Weijters, A. J. M. M., & van der Aalst, W. M. P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery, 14(2), 245-304. doi:10.1007/s10618-006-0061-7Aalst, W. M. P. van der. (2005). Business alignment: using process mining as a tool for Delta analysis and conformance testing. Requirements Engineering, 10(3), 198-211. doi:10.1007/s00766-005-0001-xVan der Aalst, W., Adriansyah, A., & van Dongen, B. (2012). Replaying history on process models for conformance checking and performance analysis. WIREs Data Mining and Knowledge Discovery, 2(2), 182-192. doi:10.1002/widm.1045Song, M., & van der Aalst, W. M. P. (2008). Towards comprehensive support for organizational mining. Decision Support Systems, 46(1), 300-317. doi:10.1016/j.dss.2008.07.00