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Data centers (DCs) are similar to traditional factories in many aspects like response time constraints, limited capacity, and utilization levels. Several indicators have been developed to monitor and compare productivity in manufacturing. However, in DCs most used indicators focus on technical aspects of infrastructure, not efficiency of operations. The purpose of this paper is to rely on operations management to define a commensurate and proportionate DC performance indicator: the energy-efficient utilization indicator (EEUI). EEUI makes objective and comparative assessment of efficiency possible independently of the operating environment and its constraints.

The authors followed a design science approach, which follows the practitioner’s initial steps for finding solutions to business relevant problems prior to theory building. Therefore, this approach fits well with this research, as it is primarily motivated by business and management needs. EEUI combines both the amount of energy consumed by different components and their current energy efficiency (EE). It reaches its highest value when all server components are optimally loaded in EE sense. The authors tested EEUI by collecting data from three scientific DCs and performing controlled laboratory tests.

The results indicate that the optimization of EEUI makes it possible to run computing resources more efficiently. This leads to a higher EE and throughput of the DC while reducing the carbon footprint associated to DC operations. Both energy-related costs and the total cost of ownership are consequently reduced, since the amount of both energy and hardware resources needed decrease, while improving DC sustainability.

In comparison with current DC operations, the results imply that using the EEUI could help increase the EE of DCs. In order to optimize the proposed EEUIs, DC managers and operators should use resource management policies that increase the resource usage variation of the jobs being processed in the same computing resources (e.g. servers).

The paper provides a novel approach to monitor the EE at which computing resources are used. The proposed indicator not only considers the utilization levels at which server components are used but also takes into account their EE and energy proportionality.

Delivery punctuality is essential in supply chain management, yet the cost of untimely delivery is usually assumed to be given or based on intuition and not quantified by facts.

The authors used a data set containing detailed transaction data for a nine-year period on orders and deliveries of sport goods. The methodology is based on applying a polynomial distributed lag model to longitudinal data on supply chain transactions.

The results indicate that small delivery delays up to two weeks decrease the sales by maximum 10% during a period of 3–4 weeks. Longer delays, up to 45 days, have a larger negative effect on sales, which can also last longer. For this case company, the estimated lost sales due to late deliveries (=5 days) were 5.1% of the delivery value. The longer delays got, the large the cost was: delays at least 45 days long were the most costly causing almost 40% of the estimated lost sales.

This study offers a methodology for quantifying lost sales due to delivery delays and estimating how long the poor delivery performance affects retailers' order behaviour.

The results give a quantitative decision-making tool for supply chain managers to estimate the profitability of investments in the supply chain performance, especially on improving punctuality.

The purpose of this paper is to study how variations in weather affect demand and supply chain performance in sport goods. The study includes several brands differing in supply chain structure, product variety and seasonality.

Longitudinal data on supply chain transactions and customer weather conditions are analysed. The underlying hypothesis is that changes in weather affect demand, which in turn impacts supply chain performance.

In general, an increase in temperature in winter and spring decreases order volumes in resorts, while for larger customers in urban locations order volumes increase. Further, an increase in volumes of non-seasonal products reduces delays in deliveries, but for seasonal products the effect is opposite. In all, weather affects demand, lower volumes do not generally improve supply chain performance, but larger volumes can make it worse. The analysis shows that the dependence structure between demand and delay is time varying and is affected by weather conditions.

The study concerns one country and leisure goods, which can limit its generalizability.

Well-managed supply chains should prepare for demand fluctuations caused by weather changes. Weekly weather forecasts could be used when planning operations for product families to improve supply chain performance.

The study focuses on supply chain vulnerability in normal weather conditions while most of the existing research studies major events or catastrophes. The results open new opportunities for supply chain managers to reduce weather dependence and improve profitability.

The purpose of this paper is to describe a symbolic-interpretative research on the leadership culture and its subcultures in one unified comprehensive school in Finland.

The research is a phenomenological, qualitative case study. Its methodology is based on triangulation.

The leadership culture of the unified comprehensive school studied in the present research seemed to be based on equality, communality, appreciation, flow of information and humor. Besides examining the general leadership culture of the school, an attempt was made to study the possible subcultures of the school by examining the six subject groups into which the teachers were divided in the school on the basis of the teachers’ education and tasks. These subject groups formed the subgroups of the research. If a subgroup's interpretation of the leadership culture of the school differed significantly from those of the other subgroups, the subgroup was considered to have a subculture of its own. Only one such subculture was found, that of the mathematic teachers. It, too, although being clearly a subculture of its own, included several common features with the main leadership culture of the unified comprehensive school.

The study is the first one in Finnish schools where leadership culture is conceived as a constantly reforming outcome of the meaning and interpretation processes which form themselves through the social structures of the school in connection to the leadership actions at the school.

The purpose of this study is to review the literature on the Delphi method, its characteristics and current applications through an analysis of recent most-cited scientific papers, with an emphasis on three axes, namely, the number of rounds used, stakeholder participation relevance or only academic experts’ participation and the possibility of using indicators or techniques different from those related to descriptive statistics.

In total, 57 papers were initially reviewed, 10 of them with a high citation rate. Then, an analysis was made of papers in Scopus for the period 2015-2018 published in the Technological Forecasting and Social Change Journal and in the Futures and Foresight Journal, which had the characteristic of displaying quartile Q1 or Q2 in Scimago in addition to being in Scopus.

Among the main results, the authors observe the tendency to use fewer rounds, a higher prevalence of stakeholder participation and not only academic experts but also the use of new types of modified Delphi such as real-time spatial Delphi, Delphi group, market Delphi, real-world Delphi and policy Delphi.

Among the conclusions, the possibility of using other indicators or complementary techniques to the descriptive statistics is highlighted such as number of justifications or comments between rounds, coefficients to quantify the competence or degree of expertise of the participants, measures of the perception of the expert on the usefulness of the presented feedback, graphs of the number of arguments according to the number of questions, the Wilcoxon Ranked Pairs Test, the k means, Kolmogorov–Simonov test and the Mann–Whitney U-test.