=Paper=
{{Paper
|id=Vol-2025/paper_sami40_1
|storemode=property
|title=Decision Support for Operational Excellence in Manufacturing Systems (Short Paper)
|pdfUrl=https://ceur-ws.org/Vol-2025/paper_sami40_1.pdf
|volume=Vol-2025
|authors=Andreas Felsberger,Bernhard Oberegger,Simon Reisinger,Gerald Reiner
}}
==Decision Support for Operational Excellence in Manufacturing Systems (Short Paper)==
https://ceur-ws.org/Vol-2025/paper_sami40_1.pdf
Extended Abstract -
Decision Support for Operational Excellence in Manufacturing
Systems
Andreas Felsberger and Bernhard Oberegger and Simon Reisinger and Gerald Reiner
University of Klagenfurt
Operations-, Energy-, and Environmental Management
Universitätsstrasse 65-67
Klagenfurt, Austria
1. SHORT ABSTRACT Systems. The aim of this work was to identify requirements for
operational excellence applications in the manufacturing industry.
In order to remain competitive in the digital transformed economic world, A literature review was conducted to identify grounded literature
the perfect match of supply and demand through supply chain and op- within this topic. Therefore we observed relevant topics of ”Op-
erations management is of essential importance. Flexibility, quality, costs erational Excellence”, ”Performance Measurement”, ”Manufactur-
and customer satisfaction are of major interest for companies. Programs ing Execution Systems”, Business Intelligence” and ”Manufactur-
aimed at improving these factors are often launched under the label ”Oper- ing Intelligence” within the meta-database Web of Science.
ational Excellence” (OPEX), which literally means ”excellent operational
performance” [Dahm and Brückner 2014]. The pursuit of operational excel-
lence contributes significantly to the success of companies [Issar and Navon 3. OPEX DSS
2016] and is intended to secure long-term survival [Dahm and Brückner The use of an OPEX decision support system can enable produc-
2014]. The aim of this work is to evaluate how decision support systems tion control by quickly summarizing the essential information and
can help to achieve operational excellence. For this purpose, literature was conducting a wide range of analyses. A continuous analysis and
analyzed to derive requirements for decision support via an OPEX frame- improvement of the operational performance requires continuous
work in manufacturing systems. monitoring of critical activities and the use of appropriate indi-
General Terms: Decision Support Systems, Operations Management cators [Issar and Navon 2016]. These key performance indicators
are intended to help identify gaps between expectation and perfor-
Additional Key Words and Phrases: OPEX, Business Manufacturing Intel- mance and to subsequently develop appropriate actions [Wouters
ligence, P&OM and Wilderom 2008]. An example of a key performance indicator
for operational excellence is the overall equipment effectiveness,
2. INTRODUCTION consisting of the factors availability, performance and quality of
Nowadays, companies use elements of different management sys- manufacturing processes [Kemper et al. 2004].
tems and concepts simultaneously. These management systems Despite large quantities of operational data, companies face the
provide fundamental insights for Operational Excellence. The main challenge to derive useful information from this data. Business In-
challenge is to combine these systems to gain the ability to react telligence systems (BIS) are expected to close this gap [Zeng et al.
better and faster to market volatility including quick response times 2012]. BIS have the goal to improve decision-making ”quality”
to emerging customer requirements and the adaption of new tech- through faster availability and higher data quality [Negash 2004].
nologies. Operational Excellence (OPEX) is achieved through con- The data which create the basis for BIS are gathered from various
tinuous adaptation and optimization of processes [Gleich 2008] and sources, such as Enterprise Resource Planning (ERP) or Manufac-
illustrates a collective concept for various management approaches turing Execution Systems (MES), which are of different quality or
to align all business processes to customer requirements, quality exist in different formats. ERP is the integrated management of core
and efficiency [Dahm and Brückner 2014]. Gleich et al. [Gleich business processes and MES provides traceability and enables the
2008] define Operational Excellence as the dynamic ability to real- control of multiple elements of the production process to support
ize effective and efficient core processes of the value chain through the decision making process in manufacturing. The integration of
the integrative use and design of technological, cultural and or- this data is therefore a major challenge for BI systems and as the
ganizational factors on the basis of the strategy. In this work, the storage of large amounts of data becomes increasingly favorable,
OPEX framework is considered as an information platform that in- companies collect unstructured data in large quantities [Chaudhuri
tegrates current systems used in the production environment and et al. 2011]. Business intelligence systems are intended to support
summarizes the data and information from these various systems the decision-making process by integrating these increasing vol-
collected in the supply chain process. Moreover, this paper pro- umes of unstructured data from internal and external sources [Isik
vides an overview of an OPEX framework. Thus, the OPEX frame- et al. 2011]. Intelligent decision-making is one of the current key-
work is assigned to the the category of Manufacturing Intelligence words in this research field and is interrelated to modern business
development. The potential of big data and advanced artificial in-
telligence offers new insights for innovations on Decision Support
SamI40 workshop at i-KNOW ’17, October 11–12, 2017, Graz, Austria Systems (DSS) and for decision-making in the form of more objec-
Copyright c 2017 for this paper by its authors. Copying permitted for pri- tive and evidence-based smart decisions [Abbasi et al. 2016; Zhou
vate and academic purposes. et al. 2015]. The main impact and key aspect of these intelligent
2
systems is an improved method of data analysis. The mere collec- Solomon Negash. 2004. Business intelligence. The communications of the
tion, storage and unregulated use of data has no direct impact on Association for Information Systems 13, 1 (2004), 54.
decision-making so far [Babiceanu and Seker 2016]. DSS research Daniel J Power. 2008. Decision support systems: a historical overview. In
and development will benefit from progress in huge data bases, ar- Handbook on Decision Support Systems 1. Springer, 121–140.
tificial intelligence and human-machine interactions [Power 2008]. Marc Wouters and Celeste Wilderom. 2008. Developing performance-
In the case of the Industry 4.0 paradigm, the massive increase of measurement systems as enabling formalization: A longitudinal field
data allows the optimization and improvement of models to en- study of a logistics department. Accounting, Organizations and Society
hance error analysis and the prediction of specific situations to set 33, 4 (2008), 488–516.
up counteractive measures [Andreadis et al. 2014]. Decisions made Li Zeng, Ling Li, and Lian Duan. 2012. Business intelligence in enterprise
to optimize efficiency and effectiveness of manufacturing systems computing environment. Information Technology and Management 13, 4
are reaching from the strategic level to tactical and operational (2012), 297–310.
production scheduling and control. Automating these decisions by Hong Zhou, Christopher Noble, and Julie Cotter. 2015. A Big
using innovative algorithms and intelligent software applications Data Based Intelligent Decision Support System for Sustainable Re-
based on the knowledge in the field of production and operations gional Development. In 2015 IEEE International Conference on Smart
management, the performance of a manufacturing system can be City/SocialCom/SustainCom (SmartCity). IEEE, 822–826.
improved [Felsberger et al. 2016].
ACKNOWLEDGMENTS
This research work has been performed in the EU project Power
Semiconductor and Electronics Manufacturing 4.0 (SemI40),
which is funded by the programme Electronic Component Sys-
tems for European Leadership (ECSEL) Joint Undertaking (Grant
Agreement No. 692466) and the programme ”IKT der Zukunft”
(project number: 853343) of the Austrian Ministry for Transport,
Innovation and Technology (bmvit) between May 2016 and April
2019. More information on IKT der Zukunft can be found at
https://iktderzukunft.at/en/. Moreover, the project SemI40 is co-
funded by grants from Germany, Italy, France, and Portugal.
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