Target value design: using collaboration and a lean approach to reduce construction cost

Target Costing is an effective management technique that has been used in manufacturing for decades to achieve cost predictability during new products development. Adoption of this technique promises benefits for the construction industry as it struggles to raise the number of successful outcomes and certainty of project delivery in terms of cost, quality and time. Target Value Design is a management approach that takes the best features of Target Costing and adapts them to the peculiarities of construction. In this paper the concept of Target Value Design is introduced based on the results of action research carried out on 12 construction projects in the USA. It has been shown that systemic application of Target Value Design leads to significant improvement of project performance – the final cost of projects was on average 15% less than market cost. The construction industry already has approaches that have similarities with elements of the Target Value Design process or uses the same terminology, e.g. Partnering and Target Cost Contracts, Cost planning, etc. Following an exploration of the similarities and differences Target Value Design is positioned as a form of Target Costing for construction that offers a more reliable route to successful projects outcomes

Monitoring and classification of dimensional faults for automotive body assembly

Sudden process changes occurring during automobile body assembly processes will influence the downstream assembly process and the functionality and final appearance of the vehicle. Furthermore, these faults could result in a decreased production rate and an increase in the cost if sudden process changes are so serious that the production line has to be stopped for investigation. Thus, sudden process changes should be detected and eliminated as soon as possible to prevent defective products from being produced and to reduce the cost of repairs/reworks. A monitoring algorithm is developed to detect, classify, and group process changes by analyzing the dimensional data of car bodies. The results of this monitoring algorithm can help diagnose the root causes of variation according to the locations of measurement points, body structure, assembly sequence, and tooling layout. Measurement data obtained from an optical coordinate measuring machine (OCMM) are used to demonstrate the monitoring technique.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45577/1/10696_2005_Article_BF01358905.pd

The role of the supply chain in the elimination and reduction of construction rework and defects: an action research approach

Since 2007, Ireland has suffered a circa 80% reduction in construction output. This has resulted in bankruptcy, unemployment and bad debt. Contractors have attached greater emphasis to production efficiency and cost reduction as a means of survival. An Action Research (AR) strategy was used in this research to improve processes adopted by a SME contractor for the control of defects in its supply chain. It is conservatively estimated that rework, typically accounts for, circa 5% of total project costs. Rework is wasteful and presents an obvious target for improvement. The research reported here concerns the (first) diagnosing stage of the AR cycle only, involving: observation of fieldwork, analysis of contract documents, and semi-structured interviews with supply chain members. The results indicate potential for supply chain participants to identify root causes of defects and propose solutions, having regard to best practice to avoid re-occurrence. A lack of collaborative forums to contribute to production improvement was identified. Additionally the processes, used to collect, manage and disseminate data were unstructured and uncoordinated, indicating scope for developing more efficient methods. The research indicates good understanding of the potential benefits for supply chain collaboration but suggests that the tools and knowledge to collaborate are currently lacking in the SME sector

Correspondence with W.A. Shewhart (American Telephone and Telegraph Company and Bell Telephone Laboratories)

March 1930 - March 1956. 18 letters

Managing Software Process Improvement (SPI) through Statistical Process Control (SPC)

Measurement based software process improvement is nowadays a mandatory activity. This implies continuous process monitoring in order to predict its behavior, highlight its performance variations and, if necessary, quickly react to them. Process variations are due to common causes or assignable ones. The former are part of the process itself while the latter are due to exceptional events that result in an unstable process behavior and thus in less predictability. Statistical Process Control (SPC) is a statistical based approach able to determine whether a process is stable or not by discriminating between the presence of common cause variation and assignable cause variation. It is a well-established technique, which has shown to be effective in manufacturing processes but not yet in software process contexts. Here experience in using SPC is not mature yet. Therefore a clear understanding of the SPC outcomes still lacks. Although many authors have used it in software, they have not considered the primary differences between manufacturing and software process characteristics. Due to such differences the authors sustain that SPC cannot be adopted “as is” but must be tailored. In this sense, we propose an SPC-based approach that reinterprets SPC, and applies it from a Software Process point of view. The paper validates the approach on industrial project data and shows how it can be successfully used as a decision support tool in software process improvement