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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.

Collaborative networked organisations (CNO) are a means of ensuring longevity and business continuity in the face of a global crisis such as COVID-19. This paper aims to present a multi-criteria decision-making method for sustainable partner selection based on the three sustainability pillars and risk.

A combined analytic hierarchy process (AHP) and fuzzy AHP (F-AHP) with Technique for Order of Preference by Similarity to Ideal Solution approach is the methodology used to evaluate and rank potential partners based on known conditions and predicted conditions at a future time based on uncertainty to support sustainable partner selection.

It is integral to include risk criteria as an addition to the three sustainability pillars: economic, environmental and social, to build a robust and sustainable CNO. One must combine the AHP and F-AHP weightings to ensure the most appropriate sustainable partner selection for the current as well as predicted future period.

The approach proposed in this paper is intended to support existing CNO, as well as individual firms wanting to create a CNO, to build a more robust and sustainable partner selection process in the context of a force majeure such as COVID-19.

This paper presents a novel approach to the partner selection process for a sustainable CNO under current known conditions and future uncertain conditions, highlighting the risk of a force majeure occurring such as COVID-19.

The purpose of this paper is to examine the relationship between acquisitions and inventory performance. Specifically, it analyzes the inventory performance (inventory level) of acquirers and their targets pre- and post-acquisition.

Using several business databases, a sample of 270 horizontal acquisitions by US firms between 1996 and 2004 is subject to multivariate analysis. Various robustness tests are applied to validate the results.

Three main results are found. First, the acquirer’s inventory performance is normally better than its target’s prior to the acquisition, consistent with acquirers taking over less efficient firms rather than cherry picking the more efficient ones. Second, inventory performance improves over time in the post-acquisition period in those cases where the acquirer is more efficient than the target. Third, inventory performance deteriorates over time in the post-acquisition period in those cases where the acquirer is less efficient than the target. The results are consistent with acquisitions being associated with both efficiency gains and efficiency losses due to (in)efficiency transfers from acquirers to targets.

From the management point of view, the study delivers the strongest message to companies that have substantial inventories and for whom efficient inventory management is vital to overall performance. Managers who are unaware of the potential consequences of acquisitions on inventory performance destroy value.

This research complements past research by showing that in spite of their synergetic potential, reducing inventory receives only limited attention in acquisitions.

To improve supply chain performance, companies are now exploring new pathways including industry-wide data sharing initiatives along complex supply chains. The purpose of this paper is to stimulate research in this field by describing the benefits, obstacles and the governance required for supply chain data sharing initiatives.

Based on publicly available information complemented by interviews with practitioners, the authors describe how companies are establishing ambitious data sharing infrastructure and initiatives along their supply chains.

The authors describe how data sharing along supply chains is becoming increasingly important for many companies and how the automotive sector is working towards establishing a digital infrastructure for data sharing that could support a wide range of use cases. The article emphasises the importance of studying the governance of data ecosystems using new theoretical approaches. Finally, the authors suggest three areas for future research on data ecosystems, including their governance, the learning dynamics that will drive their adoption and their relationship with broader system-level changes.

This paper is the first, to the authors’ knowledge, that depicts how industry-wide data-sharing initiatives are expected to have an impact on supply chain performance. The authors highlight factors that affect the development and implementation of these initiatives along supply chains.

Demand forecasting models in companies are often a mix of quantitative models and qualitative methods. As there are so many existing forecasting approaches, many forecasters have difficulty in deciding on which model to select as they may perform “best” in a specific error measure, and not in another. Currently, there is no approach that evaluates different model classes and several interdependent error measures simultaneously, making forecasting model selection particularly difficult when error measures yield conflicting results.

This paper proposes a novel procedure of multi-criteria evaluation of demand forecasting models, simultaneously considering several error measures and their interdependencies based on a two-stage multi-criteria decision-making approach. Analytical Network Process combined with the Technique for Order of Preference by Similarity to Ideal Solution (ANP-TOPSIS) is developed, evaluated and validated through an implementation case of a plastic bag manufacturer.

The results show that the approach identifies the best forecasting model when considering many error measures, even in the presence of conflicting error measures. Furthermore, considering the interdependence between error measures is essential to determine their relative importance for the final ranking calculation.

The paper's contribution is a novel multi-criteria approach to evaluate multiclass demand forecasting models and select the best model, considering several interdependent error measures simultaneously, which is lacking in the literature. The work helps structuring decision making in forecasting and avoiding the selection of inappropriate or “worse” forecasting model.

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 examine how companies should off‐shore complex product related tasks to low‐cost countries, without jeopardizing their competitive advantage and intellectual property, while building solid and sustainable business in the sourcing country.

The underlying case concerns a multinational, globally operating engineering company delivering complex system products used as part of industrial and social infrastructure, and its entry to off‐shoring and how it has evolved from a green field operation to sizeable value center over the past six years.

The case provides support to the fact that companies understanding that building permanent, knowledge‐based and proprietary presence with full product management responsibility in lower labor cost countries will be more responsive in serving customers, cost efficient in maintaining old infrastructure products and in delivering new ones in the future. Further, complex product business companies focusing on long term and knowledge based legacy building in emerging economies will develop, not only more robust global business platform for themselves, but they will also contribute to the sustainable development of the global economy.

The paper presents unique descriptive data on the overall outsourcing strategy of a global engineering company and how one of its off‐shoring units has evolved over time since its inception.

This paper aims to systematically document drivers of change and the implications they will have on international supply chain management in the coming two decades.

This study was commissioned by the World Customs Organization (WCO) at the end of June 2006. Because of increased trade volumes, emerging complex supply networks and heightened security concerns, the WCO saw the need to assess future trends and drivers in supply chain management. The Delphi method was applied to identify a set of foreseeable drivers of change and to assess their predicted impact on global supply chain management in the coming ten to 20 years. Based on a literature review of 150 recent publications and interviews among 33 industry, academic and customs experts, a survey was designed and conducted to collect current and potential change drivers in global supply chains. These drivers were compiled and prioritized by an eclectic team of 12 specialists.

The main results of the study are strongly connected to strategic and operational supply chain planning for the next ten to 20 years. They are related to increased off‐shoring of operations through truly global manufacturing, characterized by its intercontinental supply of materials; increased product complexity with shorter product life cycles; increased importance of business‐to‐government networking for operational and security efficiency; introduction of new supply chain services integrating financial, physical and information flows leading to further consolidation in the logistics markets; and the overall increase in risks and vulnerabilities in international supply chains.

This paper provides a 360 degree view of the future of international supply chain management and the challenges companies will face to compete in the twenty‐first century business environment.

The paper shows that it is possible to reconstruct the dynamical attractors of demand at different levels of the supply chain by using time series duplication and techniques for normalisation. The objective of reconstructing dynamical attractors is to learn more about the long‐term dynamical behaviour of supply chains. Typical patterns that can be encountered through phase space reconstruction are discussed. Based on the analysis of real life supply chains first results are presented on how attractors can be used to better understand the dynamical behaviour of supply chains. The cases show that clear attractors can be identified for consumer and retailer demand. When this demand is compared with supply the phase space analysis becomes an effective tool for identifying distortion in the supply chain. The paper concludes by presenting two examples on how a better understanding of demand attractors have been used to improve operational and tactical planning.

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.