Structural model for analysis of key performance indicators for sustainable manufacturer–supplier collaboration: A grey‐decision‐making trial and evaluation laboratory‐based approach

Mitarbeiter sind zwar Kostenfaktor und Risikofaktor jedes Unternehmens, aber vor allem auch Leistungs-, Kreativ- und Wertschöpfungsfaktor. Legt man zugrunde, dass Humankapital und Wissen als zentrale Eckpfeiler der Innovations- und Wettbewerbsfähigkeit zu werten sind, ist die entscheidungsorientierte Steuerung von Personalinformationen geradezu ein Muss für eine zukunftsorientierte Unternehmensentwicklung. Kenngrößen sollten Intuition und Erfahrung von Unternehmern sowie Führungskräften bei der Entscheidungsfindung und Entscheidungsdurchsetzung unterstützen. Dabei ist zweierlei zu bedenken: Zum einen sind unternehmensspezifische Bedürfnisse in das Berichtswesen und das Kennzahlencockpit zu integrieren und zum anderen bedeutet die Umsetzung von Personalmaßnahmen vor allem auch Transparenz zu schaffen und Kommunikation zu pflegen. Employees are considered as cost and risk factors but most importantly represent creative and value adding potential for companies. Since personnel and know-how are cornerstones of the company´s innovative and competitive capabilities, utilization of relevant personnel information is absolutely essential for a future orientated development of companies. Key performance indicators support intuition and experience of entrepreneurs and managers in decision making. While doing so company specific needs have to be taken into account in designing reporting and data cockpits on the one hand. On the other hand implementation of personnel related activities always demands transparency as well as timely and continuous communication. Keywords: views, verhaltenssteuerung, querys, personalcontrolling, datenanalyse

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Advanced decision-making in higher education: learning analytics research and key performance indicators

Behaviour and Information Technology ◽

10.1080/0144929x.2018.1512940 ◽

2018 ◽

Vol 37 (10-11) ◽

pp. 937-940 ◽

Cited By ~ 7

Author(s):

Miltiadis D. Lytras ◽

Naif Radi Aljohani ◽

Anna Visvizi ◽

Patricia Ordonez De Pablos ◽

Dragan Gasevic

Keyword(s):

Higher Education ◽

Decision Making ◽

Performance Indicators ◽

Learning Analytics ◽

Key Performance Indicators

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Dynamic Model and Graphical User Interface: A Solution for the Distribution Process of Regional Products

Applied Sciences ◽

10.3390/app10134481 ◽

2020 ◽

Vol 10 (13) ◽

pp. 4481 ◽

Cited By ~ 1

Author(s):

Ernesto A. Lagarda-Leyva ◽

Alfredo Bueno-Solano ◽

Harvey P. Vea-Valdez ◽

Daniel O. Machado

Keyword(s):

Decision Making ◽

Supply Chain ◽

User Interface ◽

Graphical User Interface ◽

Performance Indicators ◽

Process Analysis ◽

Key Performance Indicators ◽

Food Distribution ◽

Technological Solution ◽

Distribution Process

Organizations in the agroindustry sector face shorter delivery schedules; therefore, they are seeking ways to conduct more effective and less costly product distribution. Supply chain management efforts have focused on improving the flow of both products and information. Thus, the aim of this case study was to build a graphical user interface to enable decision-making based on quantitative information for a food distribution process. The problem to be solved was associated with the development of a technological solution to reduce and control variations in transportation times, delivery costs and capacities in cold and dry food distribution. An eight-step system for a dynamics methodology was used: (1) distribution process analysis, (2) route description, (3) variable and parameter description, (4) causal loop diagram creation, (5) current model simulation, (6) validation, (7) quantitative scenario construction based on key performance indicators, and (8) graphical user interface development. The main findings of this research were that the graphical user interface and simulation showed information that represented on average 56.49% of the total distribution costs regarding fuel and that maintenance and tire wearing costs had less of an impact on total costs, representing 9.21% and 3.66% of the total costs, respectively. Additionally, the technological solution—created for the supply chain in the distribution process against the background of changes in policies—makes it possible to improve decision-making based on different scenarios supported by a graphical interface according to key performance indicators. This solution could be used by different organizations who aim to reduce logistics and transportation costs. The main implications of this research were the available and organized information and the restructuring of the distribution process.

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