Prefabricated construction has long faced problems due to the industry’s fragmentation. Building Information Modeling (BIM) has thus appeared as an efficient solution to provide a favorable environment for efficient completion of projects. Despite its benefits, implementing BIM successfully in small and medium-sized enterprises (SMEs), which represent the vast majority of manufacturers in Quebec, requires deep risk analysis and rigorous strategies. Hence, this work aims to study BIM implementation barriers, strategies, and best practices in wood prefabrication for SMEs through a literature review, semi-structured interviews, and an online survey. After qualitative content analysis, 30 critical barriers, 7 strategic milestones, and 31 best practices to maximize BIM benefits were revealed. One of the critical barriers concerns the effort required to develop BIM software libraries and programs to translate information from the BIM model to production equipment. Among the best strategies, it is essential to start by analyzing the current business model of the SMEs and to appoint a small BIM committee whose main responsibilities are management, coordination, and modeling. The prevalent best practices were to support the implementation team and encourage communication and collaboration. Previous studies show that BIM is not fully exploited in prefabrication for various reasons. This study highlights the critical barriers, strategies, and best practices for BIM adoption and proposes a framework for BIM implementation in prefabrication SMEs in Quebec, Canada. It also provides a summary of current knowledge and guidelines to promote BIM adoption in this sector.

Timber structures in construction have become more popular in recent years. Nevertheless, besides the complexity of designing, contracting and building these structures, a barrier to their market growth is the complexity of their supply chain relationships encompassing architects, engineers, builders and suppliers. The objective of this study is therefore to identify and characterize the supply chain relationships shared by these stakeholders within a massive timber construction project. Twenty-seven semi-structured interviews with architects, structural engineers, builders and timber element suppliers from nine countries, participant observations and secondary data were used to study the various relationship levels involved in timber construction projects. Triangulation and qualitative data analysis were also conducted. Three levels of relationships were then identified: “Contractual,” “Massive timber construction project” and “Massive timber construction industry development.” Results showed that timber structures involve value-added stakeholder relationships rather than linear relationships. These relationships appeared closer and more frequent and involved knowledge and information sharing. Furthermore, prefabricated systems allow for smoother relationships by limiting the number of stakeholders while promoting innovative thinking.

Planning decisions are generally subject to some level of uncertainty. In forestry, data describing the resources available have a major impact on operations performance and productivity. This paper aims to present a method to improve decision-making in the forest supply chain by taking supply uncertainty into account using the results of data quality assessments. The case study describes the operations planning process of a Canadian forest products company dealing with an uncertain volume of wood supply. Three approaches to constructing probability distributions based on data quality are tested. Each approach offers a different level of precision: (1) a frequency distribution of accuracy, (2) a normal distribution based on average accuracy, and (3) a normal distribution based on data quality classification. Using stochastic programming to plan transport and production shows that lower costs can be achieved with a general characterisation of the data accuracy. Not considering uncertainty when planning operations leads to a significant replanning transportation cost. Using classes of data quality to include uncertainty in operations planning contributes to reducing the transportation cost from $15.90/m³ down to $15.32/m³ representing 3.6%.

The project-based construction industry finds itself in a paradoxical situation: while it weighs heavily in the world economy, it does have a history of low productivity. One important issue that plagues the industry is related to the challenges that stem from collaborative efforts (or lack thereof) between actors. The objective of this paper is to explore how actors of the construction industry organize their inter-firm relationships while examining the characteristics of such interactions and the elements affecting them (drivers, barriers, facilitators, outcomes). These interactions and elements were uncovered using a systematic literature review. A qualitative content analysis was carried out to categorize these elements and to generate dimensions describing the forms. The 139 articles retrieved depicted 12 relational forms established between construction companies (in descending order of citation): partnering, alliancing, project delivery methods, supply chain integration, joint ventures, integrated project delivery, joint risk management, collaborative design, contingent collaboration, quasi-fixed network, resource sharing, and collaborative planning. A multitude of drivers, barriers, facilitators, and outcomes were found. An analysis of the results led to the conceptualization of a multidimensional profile, which allows for a practical and flexible identification of the relationship form potential partners in the construction sector intend to establish. To provide guidelines for the implementation of this profile, a three-step framework was developed.

The use of timber as structural building material is growing and a greater number of firms are looking to enter this raising market. Erecting a complex timber building usually involves combining the work of architects, structural engineers, builders, suppliers and/or supplier–builders, all of them having their own business models. The purpose of this research was to uncover the specific nature of business models in the timber structure building industry. First, a thorough mapping of these business models was undertaken. Second, underlying patterns were uncovered within these models. A triangulation method of secondary data, semi-structured interviews and participant observation was used to allow for an in-depth study of 23 stakeholder business models. The analysis shows that knowledge sharing appears as crucial and may be achieved through sustained collaboration. As a result, collaborative contract procurement modes seem to be the most appropriate for timber construction. Tight relationships with suppliers and supplier–builders also appear as prerequisites. Furthermore, stakeholder partnerships with universities appear common in the field, while prefabrication is increasing in popularity. These findings can be useful to grasp the prevailing business models in this industry given the sustained growth of the timber structure building market.

Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This article proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.