T. B. Ladinig

57192074675

Publications - 4

Mapping quality linkages based on tacit knowledge

T. B. Ladinig Gyula Vastag

Publication Name: International Journal of Production Economics

Publication Date: 2021-03-01

Volume: 233

Issue: Unknown

Page Range: Unknown

Description:

A structured conceptualization method, concept mapping, is applied to visualize the conceptual domain of explicit and tacit quality linkages in a complex, causally ambiguous production system of a premium automotive OEM. Experts, intimately familiar with all facets of the conceptual domain, defined sources of quality problems and rated their impact on product quality. These inputs, formative measures for a latent construct, were used to create concept maps and clusters for the sources of quality problems. Differences and disagreements between subgroups were highlighted by pattern matching. The concept map and the preferred cluster solution, based on user-defined measures, served as inputs in the development of a causal loop diagram and an action plan for better resource allocation to specific improvement activities. The approach, using formative rather than the more commonly used reflective indicators, uses key informants and explanation building processes of high internal validity. In the spirit of the “proximal similarity model,” the presented methodology is also highly transferable to similar settings of other automotive OEMs and beyond.

Open Access: Yes

DOI: 10.1016/j.ijpe.2020.108006

Sensemaking support system (S3) for manufacturing process improvement

T. B. Ladinig Krishna S. Dhir Gyula Vastag

Publication Name: International Journal of Production Research

Publication Date: 2021-01-01

Volume: 59

Issue: 8

Page Range: 2406-2425

Description:

Production management teams often face unfamiliar situations where each team member must understand new phenomena individually before the team can make mutually understandable and acceptable decisions. Contradicting subjective judgments can distort the group’s decision-making process because team members understand situations differently and are generally prone to behavioural biases. This paper presents the development of a sensemaking support system (S3,S cube) for selecting improvement projects in a complex,small-volume batch production system of a premium car manufacturer. All phases of the sensemaking process are facilitated by making various sources of information available to a team of managers and experts to reduce conflicts regarding the selection of improvement projects. S3 is based on a lens model which combines judgments of the management team with discrete event simulation and provides visual representations of the differences and misjudgements related to various improvement options. The results–that can easily be generalised to many similar settings–indicate different understanding and lack of coherence within the management team which prevents them from defining mutually acceptable actions. This is countered with the creation of an action proposal,summarising and visualising causal relationships,and connecting them to improvement options to improve performance of the production system.

Open Access: Yes

DOI: 10.1080/00207543.2020.1733700

Development of production control in small batch production

T. B. Ladinig Péter Németh

Publication Name: Matec Web of Conferences

Publication Date: 2016-10-25

Volume: 81

Issue: Unknown

Page Range: Unknown

Description:

Our aim with this paper is to develop a new performance measurement and control system for small batch production in the automotive industry. For this reason, we present our previous research results for warehouse performance measurement and adopt its methodology to production control. The proposed method is based on artificial intelligence (neural networks).

Open Access: Yes

DOI: 10.1051/matecconf/20168106001

Use of Artificial Neural Networks in the production control of small batch production

T. B. Ladinig B. Ferenczi Péter Németh

Publication Name: Proceedings of the 2016 International Conference on Artificial Intelligence Icai 2016 Worldcomp 2016

Publication Date: 2016-01-01

Volume: Unknown

Issue: Unknown

Page Range: 237-240

Description:

Our aim with this paper is to test a new performance measurement and control system for small batch production in the automotive industry with the help of Artificial Neural Networks. After the introduction of small batch production at an automotive company a possible use of this method for production control is presented.

Open Access: Yes

DOI: DOI not available