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In recent years, steadily rising and ever‐changing energy prices have required the development of sustainable strategies to reduce the energy consumption of buildings. Measures to improve the energy efficiency of detached houses have already been implemented. This new procedure addresses the energy efficient modernization to entire building stocks of public authorities and therefore targets to meet a key niche issue.

As result of literature research the demand for a strategic approach for energy efficient improvement was pointed out. The model framework refers to four main requirements for a new procedure within a comprehensive view of facilities management (FM), lifecycle orientation, efficient working processes, setting of objectives and monitoring of results. In addition the literature research recorded five main methods and tools which were utilized for the model development.

The new constructed facility‐energy‐efficiency model (FEE‐model) supports the decision process. The starting point is the selection of the buildings with the highest energy saving potential. The next step is a deeper analysis of the as‐is‐state in comparison with a defined target state. After that improvement areas were identified and finally a recommendation for the implementation is given. The model was tested within practical cases of which a municipal kindergarten is documented as example in this article.

The paper provides useful information on energy efficient improvement of entire building stocks.

Infrastructure projects are expected to deliver value to their stakeholders long after completion. Project value is multi-dimensional and subjective and evolves over the project lifecycle. How stakeholders frame the expected value is central to the public debate about proposed infrastructure projects and influences the financing decisions; however, this framing is inadequately understood. The purpose of this paper is to develop new knowledge for shaping infrastructure projects by identifying the ways in which stakeholders frame project value at the project front end.

Three transport infrastructure projects are compared in a qualitative, document-based study. The authors map the dimensions of value at the project front end and identify stakeholders’ approaches to lifecycle-oriented framing of value.

Financial, social and comparative values are dominant in the project front end. The authors frame value into positive and negative dimensions and identify four themes in the lifecycle-oriented framing of value, including uncertainties, timing of cost and benefit realization, project relations and external sponsorship.

The research is limited through the focus on transport infrastructure projects and project front end only, the selection of cases from a single country and the use of document-based data. The systematic analysis approach has yielded novel analytical frameworks that will be useful for further research.

This study identifies value dimensions that are specific to transport infrastructure projects and proposes a framework to assist stakeholders and project managers to better assess and negotiate value when designing their projects.

Regional and comparative values are revealed as novel aspects of value specific to infrastructure projects. The alternative lifecycle-oriented frames offer a new way to understand and structure the co-creation of value and shape negotiation for investment decisions in the project. A portfolio perspective to investment decision making is proposed.

The paper aims to describe and apply a commercially oriented method of forecast performance measurement (cost of forecast error – CFE) and to compare the results with commonly adopted statistical measures of forecast accuracy in an enterprise resource planning (ERP) environment.

The study adopts a quantitative methodology to evaluate the nine forecasting models (two moving average and seven exponential smoothing) of SAP^®'s ERP system. Event management adjustment and fitted smoothing parameters are also assessed. SAP^® is the largest European software enterprise and the third largest in the world, with headquarters in Walldorf, Germany.

The findings of the study support the adoption of CFE as a more relevant commercial decision‐making measure than commonly applied statistical forecast measures.

The findings of the study provide forecast model selection guidance to SAP^®'s 12+ million worldwide users. However, the CFE metric can be adopted in any commercial forecasting situation.

This study is the first published cost assessment of SAP^®'s forecasting models.

Since technological lifecycles do not always match hardware/software (HW/SW) lifecycles, obsolescence becomes a major issue in system lifecycle management as it can cause premature and unscheduled replacement of HW/SW subsystems. The purpose of this paper is to report a dynamic model to predict the obsolescence dates for HW/SW subsystems.

The dynamic model estimates obsolescence dates for HW/SW subsystems based on graph theory concept. The model depicts the stages of subsystem obsolescence through transmittances composed of probability and time-distribution elements. The model predicts probability and mean time to obsolescence for line replaceable units (LRUs) over the lifetime of the system. An illustrative example in signaling systems used in a train control system was used to demonstrate the application of this model.

Generally, the short timespan for HW/SW subsystems, which are periodically replaced with newer technologies, results in the development of new product lines by suppliers while they try to support legacy systems for a reasonable period of time. Obsolescence of HW/SW subsystems increases operation and maintenance costs as legacy systems are typically more expensive to maintain. The costs can be reduced by an optimum time to obsolescence derived from the model.

This research adds to the body of knowledge on asset management and maintenance strategy. This paper may be of particular interest to reliability, maintainability and availability practitioners and project managers.

The originality of this paper lies in developing a graph-based model that predicts probability and mean time to obsolescence for LRUs over the lifetime of the system.

The selection and use of the most appropriate building project contract price forecasting model contribute to the provision of strategic advice that clients can use to make value‐for‐money business decisions. This work seeks to provide a snapshot of current practice in model selection by practitioners based in large‐sized quantity surveying, project management and multi‐disciplinary practices based in the UK.

A quantitative research design was used to capture data from a sample of 300 such organisations in 2004. An initial and follow‐up administration of the postal survey generated an overall response of 54 per cent.

The findings of the study revealed that the traditional types of forecasting model continue to be in widespread use irrespective of organisational type. Lifecycle cost models and in‐house knowledge‐based systems were also found to be in use, but not on such a widespread scale. Newly developed models such as artificial neural nets, fuzzy logic nets, as well as environmental and sustainability cost models were found, as yet, to have only very limited application in practice. Practitioner assessment of model accuracy and value in‐use provided statistically insignificant levels of variance between the organisational types and the models found to be in use.

The work is limited due to the size of the sample frame and the measuring instrument used which could not uncover reasons for the selection of particular types of models.

The outcomes of the work provide benchmarks that can be used to evaluate organisational approach and future research. The paper contributes to the body of knowledge available on the process of building project contract price forecasting that is fundamental to the assessment of project value.

The purpose of this article is to propose a planning process that takes into account that manufacturers of original equipment have products at different stages of the product‐life cycle, and utilizes sales and inventory information collected from distributors and retailers.

The research paper describes the construction and testing of a planning process.

Trials in a case company indicate that supply chain responsiveness can be improved in product launches using the proposed process. Supply chain efficiency in the maturity phase can also be improved.

The usefulness and effectiveness of the proposed process depend on the assumption that product mix changes can be modeled and point‐of‐sales and channel sell‐through data are available regularly and reliably.

Modeling and monitoring the variant mix on the total market level can be used to improve supply chain responsiveness to mix changes in product launches. The introduction of this planning process reduces the need for planning in the sales units.

The paper shows how the quality of variant forecasting for an original equipment manufacturer can be improved with access to channel visibility in the market introduction phase.

It has been claimed that the private finance initiative (PFI) provides value for money in the overall life of the project through the lifecycle costing (LCC) process under the umbrella of lifecycle management (LCM). The available literature points to the fact that LCC is very important in getting value for money from PFI projects. However, there is no literature available on the effect of the use of LCM in PFI projects in the UK. Therefore, the purpose of this paper is to explore the factors that influence the success of LCM in educational PFI projects.

The paper adopts a post-positivist approach to literature review. Purposive sampling is utilised with a mixed methodological approach. 6 qualitative inductive interviews offer key themes, which are further investigated using quantitative deductive questionnaires, of which 35 were issued and 26 were returned.

The paper provides empirical insights about the key success factors of LCM in the education sector. The results highlight the necessity of quality standardized data collection in a big data form. It highlights the need for a cultural shift from short- to long-term profit maximisation and service provision by the use of LCM in the PFI education sector.

A purposive sample was used to maximise the validity of data collection. Although this method has garnered concise and clear results, it is understood that this study is limited into a niche sector and a set of subsequently niche professionals. It is recommended that a larger sample be utilised and the spectrum of PFI sectors be opened up to further explore the topic.

Further investigations across different sectors of PFI project may be viewed as a good comparison, sectors such as health, accommodation and prisons. Gathering responses across all sector types could have resulted in a greater number of responses received and offer greater validity to this study.

While key success factors are clearly identified, fragmentation is seen as a barrier to the wholesale collection of such data. The responsibility, obligation to collect data for the benefit of future projects is not a priority for SMEs with little or no incentive to consider the progression of the sector. Albeit, there is evidence of one particular successful constructor/SPV, which is consolidating their business and are experiencing greater and sustained success.

This paper identifies previously unknown key influencing factors of success for educational PFI projects in relation to LCM.

This paper aims to examine whether firms in the decline stage of lifecycle manipulate core or operating income through misclassification of operating expenses as income-decreasing special items.

The sample comprises of firms from an emerging market, India with data from 1996 to 2011. The paper uses the methodology given in McVay’s (2006) work and multiple regressions.

Managers of Indian firms also engage in classification shifting, primary incentive being the desire to avoid reporting of operating losses. Furthermore, the use of classification shifting is dependent upon the stage of lifecycle in which firm is in. Specifically, firms in the decline stage of lifecycle are more likely to use classification shifting to avoid reporting of operating losses.

The paper sheds light on a critical phase of the firm lifecycle, decline, which increases the possibility of the use of classification shifting, an earnings management technique which is tough to detect. Firms in decline, thus, may be trying to fool the investors who are infusing capital to save the company from going bankrupt; regulators, who are likely to focus less on troubled firms; and auditors, who may not be expecting core income manipulation in such firms.

The paper extends the literature on classification shifting and presents first evidence that such shifting is more likely to take place during the decline phase of firm lifecycle.

Economic costs and benefits are at the core while taking decision to adopt digitalization in the supply chain. The present chapter provides an in-depth exploration of the economic dimensions of digital supply chain management (DSCM) adoption in a firm. Drawing from a diverse source of literature, this chapter discusses the effect of economic outlook on DSCM adoption, the economic benefits of DSCM adoption and costs associated with it, and economic analysis and evaluation methodologies. The chapter also shares the case studies illustrating the real-world implications of economic considerations within DSCM initiatives. The chapter highlights how changing international socioeconomic and political dynamics can influence businesses across the globe. By analyzing the impacts of evolving market trends, changing consumer preferences, and geopolitical tensions, organizations can considerably forecast the possible impacts of these macroeconomic forces adeptly. The chapter also undertakes discussion on the economic cost and benefits associated with DSCM adoption. The economic analysis helps understand that the expected benefits outweigh economic costs, substantiating the economic viability of DSCM projects. The chapter concludes by discussing the examples of some real-world companies, highlighting how organizations have successfully applied economic analyses to their DSCM initiatives. This also highlights as to how showcasing how detailed economic assessments can justify substantial investments, deliver operational efficiencies, and reshape industries.

The purpose of this paper is to present the results of South Africa’s national freight demand model and related logistics cost models, and to illustrate the application of the modelling outputs to inform macrologistics policy.

Spatially and sectorally disaggregated supply and demand data are developed using the input-output (I-O) model of the economy as a platform, augmented by actual data. Supply and demand interaction is translated into freight flows via a gravity model. The logistics costs model is a bottom-up aggregation of logistics-related costs for these freight flows.

South Africa’s logistics costs are higher than in developed countries. Road freight volumes constitute 80 per cent of long-distance corridor freight, while road transport contributes more than 80 per cent to the country’s transport costs. These challenges raise concerns regarding the competitiveness of international trade, as well as the impact of transport externalities. The case studies highlight that domestic logistics costs are the biggest cost contributor to international trade logistics costs and can be reduced through inter alia modal shift. Modal shift can be induced through the internalisation of freight externality costs. Results show that externality cost internalisation can eradicate the societal cost of freight transport in South Africa without increasing macroeconomic freight costs.

Systematic spatially disaggregated commodity-level data are limited. There is however a wealth of supply, demand and freight flow information collected by the public and private sector. Initiatives to create an appreciation of the intrinsic value of such information and to leverage data sources will improve freight demand modelling in emerging economies.

A spatially and sectorally disaggregated national freight demand model, and related logistics costs models, utilising actual and modelled data, balanced via the national I-O model, provides opportunities for increased accuracy of outputs and diverse application possibilities.