Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. These criteria were defined as indicators that measure the performance of the areas of the value chain related to inventory management and were used to classify ABC inventory of the products according to these selected criteria. Therefore, the methodology allows us to solve inventory management DDM based on multicriteria ABC classification and was validated in a Colombian company belonging to the graphic arts sector.Pérez Vergara, IG.; Arias Sánchez, JA.; Poveda Bautista, R.; Diego-Mas, JA. (2020). Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork. Complexity. 2020:1-13. https://doi.org/10.1155/2020/6758108S1132020Poveda-Bautista, R., Baptista, D. C., & García-Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research, 50(17), 4738-4752. doi:10.1080/00207543.2012.657964Castro Zuluaga, C. A., Velez Gallego, M. C., & Catro Urrego, J. A. (2011). 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New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62-74. doi:10.1016/j.ijpe.2014.05.015Raja, A. M. L., Ai, T. J., & Astanti, R. D. (2016). A Clustering Classification of Spare Parts for Improving Inventory Policies. IOP Conference Series: Materials Science and Engineering, 114, 012075. doi:10.1088/1757-899x/114/1/012075Zowid, F. M., Babai, M. Z., Douissa, M. R., & Ducq, Y. (2019). Multi-criteria inventory ABC classification using Gaussian Mixture Model. IFAC-PapersOnLine, 52(13), 1925-1930. doi:10.1016/j.ifacol.2019.11.484Babai, M. Z., Ladhari, T., & Lajili, I. (2014). On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research, 53(1), 279-290. doi:10.1080/00207543.2014.952791Schneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. doi:10.1504/ijssci.2008.017590Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19-34. doi:10.1016/j.omega.2015.07.004Douissa, M. R., & Jabeur, K. (2016). A New Model for Multi-criteria ABC Inventory Classification: PROAFTN Method. Procedia Computer Science, 96, 550-559. doi:10.1016/j.procs.2016.08.233Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2018). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi:10.1080/09537287.2018.1525506Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613. doi:10.1016/j.cie.2016.06.004López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12-21. doi:10.1016/j.eswa.2017.02.048Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. 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European Journal of Operational Research, 184(1), 244-254. doi:10.1016/j.ejor.2006.10.05

Mediatisation in Twitter: an exploratory analysis of the 2015 Spanish general election

[EN] The mediatisation model in politics assumes that media conveys political messages between parties and citizenship, with the risk of promoting issues that frame the electoral content in terms of competition. These dynamics could distract from the debate of ideas and political policies. However, digital media like Twitter provide direct communication channels between parties, candidates and users. The present research explores Twitter content during an electoral campaign focused on the four issues proposed by Patterson (1980) to assess mediatisation: political, policy, campaign and personal (regarding the candidate). The goal of this research study is to evaluate the degree of mediatisation on Twitter using this typology. The research also evaluates the influence of the issue on retweet volume. The study¿s basis was a 15.8 million-tweet corpus obtained during the 2015 Spanish General Election pre-campaign and campaign. This dataset was analysed using an automatic classification system. The results highlighted a predominance of policy issues during both the pre- campaign and campaign, except for the two televised debates, during which campaign issues were the most prevalent. On the election night, users commented much more on political issues. Finally, the kind of issue most likely to be retweeted was policy issues.This research was supported by the Spanish Ministry of Economy and Competitiveness, with Grants CSO2013-43960-R (Los flujos de comunicación en los procesos de movilización política: medios, blogs y líderes de opinión) and CSO2016-77331-C2-1-R (Estrategias, agendas y discursos en las cibercampañas electorales: medios de comunicación y ciudadanos).Baviera, T.; Calvo, D.; Llorca-Abad, G. (2019). Mediatisation in Twitter: an exploratory analysis of the 2015 Spanish general election. Journal of International Communication. 25(2):275-300. https://doi.org/10.1080/13216597.2019.1634619S275300252Antonakaki, D., Spiliotopoulos, D., V. Samaras, C., Pratikakis, P., Ioannidis, S., & Fragopoulou, P. (2017). Social media analysis during political turbulence. PLOS ONE, 12(10), e0186836. doi:10.1371/journal.pone.0186836Barberá, P. (2015). Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis, 23(1), 76-91. doi:10.1093/pan/mpu011Bartholomé, G., Lecheler, S., & de Vreese, C. (2017). Towards A Typology of Conflict Frames. Journalism Studies, 19(12), 1689-1711. doi:10.1080/1461670x.2017.1299033Batrinca, B., & Treleaven, P. C. (2014). Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY, 30(1), 89-116. doi:10.1007/s00146-014-0549-4Baviera, T., Peris, À., & Cano-Orón, L. (2017). Political candidates in infotainment programmes and their emotional effects on Twitter: an analysis of the 2015 Spanish general elections pre-campaign season. Contemporary Social Science, 14(1), 144-156. doi:10.1080/21582041.2017.1367833BLUMLER, J. G., & KAVANAGH, D. (1999). The Third Age of Political Communication: Influences and Features. Political Communication, 16(3), 209-230. doi:10.1080/105846099198596Bor, S. E. (2013). Using Social Network Sites to Improve Communication Between Political Campaigns and Citizens in the 2012 Election. American Behavioral Scientist, 58(9), 1195-1213. doi:10.1177/0002764213490698Brants, K., & Neijens, P. (1998). The Infotainment of Politics. Political Communication, 15(2), 149-164. doi:10.1080/10584609809342363Burnap, P., Gibson, R., Sloan, L., Southern, R., & Williams, M. (2016). 140 characters to victory?: Using Twitter to predict the UK 2015 General Election. Electoral Studies, 41, 230-233. doi:10.1016/j.electstud.2015.11.017Campos-Domínguez, E. (2017). Twitter y la comunicación política. El Profesional de la Información, 26(5), 785. doi:10.3145/epi.2017.sep.01Campos-Domínguez, E., & Calvo, D. (2017). Electoral campaign on the Internet: Planning, impact and viralization on Twitter during the Spanish general election, 2015. Comunicación y Sociedad, 0(29), 93-116. doi:10.32870/cys.v0i29.6423Ceron, A., & Splendore, S. (2016). From contents to comments: Social TV and perceived pluralism in political talk shows. New Media & Society, 20(2), 659-675. doi:10.1177/1461444816668187Chadwick, A. (2013). The Hybrid Media System. doi:10.1093/acprof:oso/9780199759477.001.0001Conway, B. A., Kenski, K., & Wang, D. (2015). The Rise of Twitter in the Political Campaign: Searching for Intermedia Agenda-Setting Effects in the Presidential Primary. Journal of Computer-Mediated Communication, 20(4), 363-380. doi:10.1111/jcc4.12124Couldry, N., & Hepp, A. (2013). Conceptualizing Mediatization: Contexts, Traditions, Arguments. Communication Theory, 23(3), 191-202. doi:10.1111/comt.12019Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). AN INVESTIGATION OF INFLUENTIALS AND THE ROLE OF SENTIMENT IN POLITICAL COMMUNICATION ON TWITTER DURING ELECTION PERIODS. Information, Communication & Society, 16(5), 795-825. doi:10.1080/1369118x.2013.783608D’heer, E., & Verdegem, P. (2014). Conversations about the elections on Twitter: Towards a structural understanding of Twitter’s relation with the political and the media field. European Journal of Communication, 29(6), 720-734. doi:10.1177/0267323114544866Díaz-Parra, I., & Jover-Báez, J. (2016). Social movements in crisis? From the 15-M movement to the electoral shift in Spain. International Journal of Sociology and Social Policy, 36(9/10), 680-694. doi:10.1108/ijssp-09-2015-0101DiGrazia, J., McKelvey, K., Bollen, J., & Rojas, F. (2013). More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior. PLoS ONE, 8(11), e79449. doi:10.1371/journal.pone.0079449Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 205395171664582. doi:10.1177/2053951716645828Filer, T., & Fredheim, R. (2016). Popular with the Robots: Accusation and Automation in the Argentine Presidential Elections, 2015. International Journal of Politics, Culture, and Society, 30(3), 259-274. doi:10.1007/s10767-016-9233-7Freelon, D., & Karpf, D. (2014). Of big birds and bayonets: hybrid Twitter interactivity in the 2012 Presidential debates. Information, Communication & Society, 18(4), 390-406. doi:10.1080/1369118x.2014.952659Giglietto, F., & Selva, D. (2014). Second Screen and Participation: A Content Analysis on a Full Season Dataset of Tweets. Journal of Communication, 64(2), 260-277. doi:10.1111/jcom.12085Gil de Zúñiga, H., Garcia-Perdomo, V., & McGregor, S. C. (2015). What Is Second Screening? Exploring Motivations of Second Screen Use and Its Effect on Online Political Participation. Journal of Communication, 65(5), 793-815. doi:10.1111/jcom.12174Grover, P., Kar, A. K., Dwivedi, Y. K., & Janssen, M. (2019). Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences. Technological Forecasting and Social Change, 145, 438-460. doi:10.1016/j.techfore.2018.09.009Jensen, K. B. (2013). Definitive and Sensitizing Conceptualizations of Mediatization. Communication Theory, 23(3), 203-222. doi:10.1111/comt.12014Jungherr, A. (2014). The Logic of Political Coverage on Twitter: Temporal Dynamics and Content. Journal of Communication, 64(2), 239-259. doi:10.1111/jcom.12087Kalsnes, B., Krumsvik, A. H., & Storsul, T. (2014). Social media as a political backchannel. Aslib Journal of Information Management, 66(3), 313-328. doi:10.1108/ajim-09-2013-0093Lee, K., Palsetia, D., Narayanan, R., Patwary, M. M. A., Agrawal, A., & Choudhary, A. (2011). Twitter Trending Topic Classification. 2011 IEEE 11th International Conference on Data Mining Workshops. doi:10.1109/icdmw.2011.171López García, G., Llorca Abad, G., Valera Ordaz, L., & Peris Blanes, A. (2018). Los debates electorales, ¿el último reducto frente la mediatización? Un estudio de caso de las elecciones generales españolas de 2015. Palabra Clave - Revista de Comunicación, 21(3), 772-797. doi:10.5294/pacla.2018.21.3.6López-Rico, C.-M., & Peris-Blanes, À. (2017). Agenda e imagen de los candidatos de las elecciones generales de 2015 en España en programas televisivos de infoentretenimiento. El Profesional de la Información, 26(4), 611. doi:10.3145/epi.2017.jul.05MAZZOLENI, G., & SCHULZ, W. (1999). «Mediatization» of Politics: A Challenge for Democracy? Political Communication, 16(3), 247-261. doi:10.1080/105846099198613Murthy, D. (2015). Twitter and elections: are tweets, predictive, reactive, or a form of buzz? Information, Communication & Society, 18(7), 816-831. doi:10.1080/1369118x.2015.1006659Russell Neuman, W., Guggenheim, L., Mo Jang, S., & Bae, S. Y. (2014). The Dynamics of Public Attention: Agenda-Setting Theory Meets Big Data. Journal of Communication, 64(2), 193-214. doi:10.1111/jcom.12088Orriols, L., & Cordero, G. (2016). The Breakdown of the Spanish Two-Party System: The Upsurge of Podemos and Ciudadanos in the 2015 General Election. South European Society and Politics, 21(4), 469-492. doi:10.1080/13608746.2016.1198454References to the IBEREVAL Workshop ProceedingsRill, S., Reinel, D., Scheidt, J., & Zicari, R. V. (2014). PoliTwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems, 69, 24-33. doi:10.1016/j.knosys.2014.05.008Rogstad, I. (2016). Is Twitter just rehashing? Intermedia agenda setting between Twitter and mainstream media. Journal of Information Technology & Politics, 13(2), 142-158. doi:10.1080/19331681.2016.1160263Sampedro, V., & Lobera, J. (2014). The Spanish 15-M Movement: a consensual dissent? Journal of Spanish Cultural Studies, 15(1-2), 61-80. doi:10.1080/14636204.2014.938466Shah, D. V., Hanna, A., Bucy, E. P., Lassen, D. S., Van Thomme, J., Bialik, K., … Pevehouse, J. C. W. (2016). Dual Screening During Presidential Debates. American Behavioral Scientist, 60(14), 1816-1843. doi:10.1177/0002764216676245Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018). The spread of low-credibility content by social bots. Nature Communications, 9(1). doi:10.1038/s41467-018-06930-7Stella, M., Ferrara, E., & De Domenico, M. (2018). Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49), 12435-12440. doi:10.1073/pnas.1803470115Stier, S., Bleier, A., Lietz, H., & Strohmaier, M. (2018). Election Campaigning on Social Media: Politicians, Audiences, and the Mediation of Political Communication on Facebook and Twitter. Political Communication, 35(1), 50-74. doi:10.1080/10584609.2017.1334728Vaccari, C., Chadwick, A., & O’Loughlin, B. (2015). Dual Screening the Political: Media Events, Social Media, and Citizen Engagement. Journal of Communication, 65(6), 1041-1061. doi:10.1111/jcom.12187Vaccari, C., & Nielsen, R. K. (2013). What Drives Politicians’ Online Popularity? An Analysis of the 2010 U.S. Midterm Elections. Journal of Information Technology & Politics, 10(2), 208-222. doi:10.1080/19331681.2012.758072Vargo, C. J., Guo, L., McCombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election. Journal of Communication, 64(2), 296-316. doi:10.1111/jcom.12089Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: time series analysis of issue salience and party salience on audience behavior. Information, Communication & Society, 19(10), 1390-1410. doi:10.1080/1369118x.2015.1093526Xu, W. W., Sang, Y., Blasiola, S., & Park, H. W. (2014). Predicting Opinion Leaders in Twitter Activism Networks. American Behavioral Scientist, 58(10), 1278-1293. doi:10.1177/000276421452709

Improved reception of in-body signals by means of a wearable multi-antenna system

High data-rate wireless communication for in-body human implants is mainly performed in the 402-405 MHz Medical Implant Communication System band and the 2.45 GHz Industrial, Scientific and Medical band. The latter band offers larger bandwidth, enabling high-resolution live video transmission. Although in-body signal attenuation is larger, at least 29 dB more power may be transmitted in this band and the antenna efficiency for compact antennas at 2.45 GHz is also up to 10 times higher. Moreover, at the receive side, one can exploit the large surface provided by a garment by deploying multiple compact highly efficient wearable antennas, capturing the signals transmitted by the implant directly at the body surface, yielding stronger signals and reducing interference. In this paper, we implement a reliable 3.5 Mbps wearable textile multi-antenna system suitable for integration into a jacket worn by a patient, and evaluate its potential to improve the In-to-Out Body wireless link reliability by means of spatial receive diversity in a standardized measurement setup. We derive the optimal distribution and the minimum number of on-body antennas required to ensure signal levels that are large enough for real-time wireless endoscopy-capsule applications, at varying positions and orientations of the implant in the human body

On the complexity of collaborative cyber crime investigations

This article considers the challenges faced by digital evidence specialists when collaborating with other specialists and agencies in other jurisdictions when investigating cyber crime. The opportunities, operational environment and modus operandi of a cyber criminal are considered, with a view to developing the skills and procedural support that investigators might usefully consider in order to respond more effectively to the investigation of cyber crimes across State boundaries

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Interoperability, Trust Based Information Sharing Protocol and Security: Digital Government Key Issues

Improved interoperability between public and private organizations is of key significance to make digital government newest triumphant. Digital Government interoperability, information sharing protocol and security are measured the key issue for achieving a refined stage of digital government. Flawless interoperability is essential to share the information between diverse and merely dispersed organisations in several network environments by using computer based tools. Digital government must ensure security for its information systems, including computers and networks for providing better service to the citizens. Governments around the world are increasingly revolving to information sharing and integration for solving problems in programs and policy areas. Evils of global worry such as syndrome discovery and manage, terror campaign, immigration and border control, prohibited drug trafficking, and more demand information sharing, harmonization and cooperation amid government agencies within a country and across national borders. A number of daunting challenges survive to the progress of an efficient information sharing protocol. A secure and trusted information-sharing protocol is required to enable users to interact and share information easily and perfectly across many diverse networks and databases globally.Comment: 20 page