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The purpose of the study is to explore the dominant ideas in research on the management of time in construction. The focus of research has been to improve techniques for optimising the timing and sequence of activities.

A critical review of research on construction time management, challenging the typical focus. We examine the assumptions different authors make, underline the limitations of the dominant research approaches and examine the prospects for developing a new approach to researching these issues.

The dominant approach in literature focuses on unique activity traits in construction planning and measurable patterns between time-related variables. This assumes that time in construction can be managed by changing the way activities are calculated. These approaches have not been correlated with improvement in performance. Social practice theory may help to explain how programmes figure as one of many objects used during construction.

The focus is on reviewing indicative literature from key journals in construction management. The implication is that research is needed about how such documents are used in practice, which goes further than optimising plans in theory.

Future research could focus on understanding the context of construction planning practice and shift the debate from a focus on optimisation to practice.

An interpretivist approach with a focus on how tools such as planning documents are used on site. Social practice theory may provide a clearer explanation of the place of construction planning within the practice of construction management. This could provide solutions that deal effectively with stakeholder expectations around timely completion of construction projects.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

A comprehensive intervention test platform, Valla Coach, including quantitative and qualitative methods to measure and improve work flows on site and promote coaching and collective learning has been developed jointly by researchers and practitioner. The study aims to describe the methodological underpinning of the platform, and discusses the preliminary results and implications.

Expansive Learning Theory grounded in a cultural–historical perspective underpins the conceptual realisation of the test platform. The viability of the platform was tested in four contextually different construction sites (and contractors), combining established and new measurement tools, qualitative intervention approaches and coaching according to the production challenges at hand.

Valla Coach created a space on site where researchers-as-coaches and operatives converged to co-construct (new) knowledge and learn together. The knowledge that emerged from the interactions gained legitimacy through its situatedness and practical value for the operatives.

Valla Coach provides opportunities for researchers and practitioners to probe the taken-for-granted. Moreover, a variety of methods and tools are tested in different contexts. The interventions prompt questioning of assumptions and make contradictions visible. Valla Coach remains work-in-progress and needs further evaluation and validation.

We contribute insights from negotiating socio-technical complexities, evaluating digital measurement tools and technologies and experiences of operatives. Tensions at the interface between the organisation and project are rendered visible.

A bottom–up approach that is a combination of practice-based tools and methods and of theories of learning and sustainable and continuous improvements where the operators are the main actors that enable productive activity.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.

Purpose – This study aims to determine the results of productivity index, profitability and improvement of company prices and to understand the relationship between partial input factors and productivity, profitability, and price fixing.

Design/Methodology/Approach – In this work, the productivity at the palm oil factory PT Sayaukath Sejahtera was measured and evaluated by using The American Productivity Center (APC) model approach.

Findings/Results – The results showed that each index that has been analyzed has a 5.143% decrease in the productivity index per year with a profitability equal to 0.286% per year and an increase in the price improvement index of 5.143% per year. Thus, it is concluded that from each index that has been analyzed, there is a decrease in the productivity index and profitability per year and there is an annual increase in the price improvement index.

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