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Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

This study aims to facilitate consistency of information in building information modelling (BIM) and address the current BIM gaps through the perspectives of the productization concept and product structure (PS).

The study follows a conceptual research approach in conjunction with a single case study. First, the previous studies on BIM implementation, productization and PS are reviewed. Further, a case study is used to analyse the current state of productization in the construction sector and develop a functional PS for construction.

A Part-Phase-Elements Matrix is proposed as a construction-specific PS to facilitate consistency in information and to enhance BIM. The proposed matrix provides new avenues to facilitate consistent information exchange through the interconnection between conceptual PS and standard building objects library, and encourage collaborative communication between stakeholders.

This study explores the core of the productization concept and PS as means to facilitate consistency of information and thus address the current gaps in BIM. This as building projects progressively move towards systematic modular and prefabricated construction where the flow of reliable information about product and construction offerings becomes increasingly important.

The purpose of this research is to support the customization ability for industrial house building companies striving to offer individualized products but with a strategy which includes a production facility. This is accomplished by analyzing the as-is state in terms of existing engineering assets and by proposing a to-be state using the design platform and product lifecycle management (PLM) support.

This study is based on design research methodology and collected data are in-depth interviews, document reviews and workshops and method development. The theoretical baseline is product platforms and the design platform.

The analysis showed that despite use of a platform, inherent assets are disorganized. Still, the identified object-based engineering assets were possible to include in a conceptual proposal for better management, both in the process and product view, using an asset relationship matrix and a PLM system.

The results should be applicable for industrial house building and off-site construction companies and offers an approach to identify and manage their assets and platforms which are crucial to stay competitive.

Previous research on design platforms has focused on engineer-to-order companies within the mechanical industry. The contribution of this paper lies in the application and support of the design platform for industrial house building and the introduction of PLM system support.

The purpose of the study is to describe the implications of strategic lead times (SLTs) for return on investment (ROI).

This study was part of an interactive research project and is based on the logic of theory application leading to theory building. It uses a multiple case study with five holistic single cases. Empirical data (ED) have mainly been collected from interviews and focus groups.

The length of and uncertainty in SLTs have implications for companies' financial performance. These implications vary in strength and can be either direct or indirect. These findings are incorporated into a framework on SLTs' implications for ROI.

The presented array of SLTs' implications for ROI could be further investigated, focussing on their strength. Additionally, it would be interesting to substantiate the findings in the context of environmental and social sustainability (i.e. the triple bottom line).

The findings offer practitioners a rich description and understanding of SLTs' actual implications for financial performance in terms of ROI. This knowledge can support practitioners in analysing supply chain designs based on financial performance.

Using a combination of a relative financial performance measure (ROI) and a set of SLTs (systems perspective), this study focuses on SLTs' actual implications for ROI. The findings provide evidence that different sections of a supply chain can have different implications for revenue, cost and investment (i.e. the three absolute measures related to ROI).