AbstractCollaborative Networked Organisations (CNOs) are increasingly recognised for their ability to harness cooperation and complementary competencies, outperforming individual efforts in pursuing business opportunities. However, the criticality of selecting the right long‐term partner for a CNO has been understated, especially considering the evolving landscape of sustainability perceptions. This research addresses the issue of time inconsistency within the context of sustainable CNO partner selection by employing the Fuzzy Analytical Hierarchical Process with the Technique for Order of Preference by Similarity to Ideal Solution. Time inconsistency refers to a situation where preferences or decisions change over different points in time, leading to inconsistencies in choices or actions. Specifically, the study focuses on a Swiss Manufacturing CNO, examining how the evaluation of potential partners' environmental criteria changes over time. The findings reveal the presence of time inconsistency in environmental criterion evaluation between two time periods. This inconsistency stems from the evolving perception of environmental conditions and the increasing social and governmental pressures surrounding environmental standards. As a consequence, improper partner choices in CNOs can be made, potentially undermining the collaborative's overall sustainability goals. The study sheds light on the importance of considering dynamic sustainability factors in partner selection for CNOs, emphasising the need for a more comprehensive and adaptive approach to secure fruitful and lasting collaborations.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

While digital transformations are taking place, we still have limited understanding as to how ecosystems and supply chains differ from a theoretical standpoint and how they relate practically. We study the evolution of the concept of ecosystems through a systematic review of the literature to describe how the two concepts relate throughout this evolution. We use cases to investigate how ecosystems and supply chains relate practically. We show that non-contractual governance in a supply chain facilitates investment in specific assets by a few key suppliers, while non-contractual governance in an ecosystem facilitates the creation of collective shared assets by many ecosystem participants. We also show that practically supply chains and ecosystems can either compete or complement each other and we present some of the conditions for the emergence of such relations.

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value 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.

As businesses reorganize around platforms, shared digital infrastructures are becoming increasingly important to build competitive advantage. Ecosystems of open software and hardware technologies, known as "technology commons," are increasingly dominating the lower levels of digital infrastructures (i.e., below the user-interface level). To leverage technology commons for platform advantage, businesses need to play the "digital commons ecosystem game." We highlight the motivations and four strategic maneuvers for playing this game, present a five-level strategic roadmap for mastering the game and provide recommendation on who should play it.(1,2)

In recent years, the adoption of open-source digital technologies in AI computing and cloud infrastructures – which we call AI Digital Technology Commons (AI DTCs) – has accelerated the creation and adoption of cloud-based AI applications. This trend is driving infrastructural innovation and the way AI DTC resources can be harnessed for competitive advantage. We document how Google has used strategic and tactical moves in AI DTCs over the past decade to drive AI ecosystem growth while manoeuvring for a position of influence within it, to com-bine infrastructural and product innovation, and harness the resulting AI DTC momentum to drive proprietary advantage by exploiting data network effects. Based on this analysis we propose a three-step process model of infrastructural competitive advantage, which distinguishes between Herding, Productizing, and Colonizing activities in driving and leveraging DTCs for competitive advantage.

ABSTRACTMeasuring the average time that a process takes from start to finish, using observation time windows (OTWs) of different length, is required for numerous operational monitoring and control processes. We refer to this measure as the Mean Lead‐Time (MLT). This study is based on the fact that computing the MLT in an operational context is often misleading for two reasons: (1) the computed value directly depends on the length of the used OTW; and (2) some jobs are usually still running at the end of the OTW, and thus their final lead‐time is not known yet. To overcome these issues, we revisit the definition of the MLT as well as the way to measure it. We develop a method to take these two aspects into account and apply this method to four real‐life cases in different business contexts. Using these practical cases, we show that the proposed methodology makes it possible to compute a standardized measure of the MLT, allowing for a meaningful comparison when different OTW lengths are considered. These new results open the door for an efficient use of the MLT as a commensurable performance indicator and allow near real‐time monitoring and comparison across different process steps, departments, and factories.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

The datasets added include the raw data, ANP weights calculations and TOPSIS ranking calculations for the demonstration case in the article titled: Evaluating demand forecasting models using multi-criteria decision-making approach. The files include a data explanation text file.

Companies are increasingly adopting open source strategies to develop and exploit complex infrastructures and platforms that combine software, hardware and standard interfaces. Such strategies require the development of a vibrant ecosystem of partners that combines the innovation capabilities of hundreds of companies from different industries. Our aim is to help decision makers assess the benefits and challenges associated with creating or joining such ecosystems. We use a case study approach on six major collaborative ecosystems that enable the development of complex, high cost infrastructures and platforms. We characterize their strategy, governance, and their degree of intellectual property (IP) openness. We offer a three-dimensional framework that helps managers characterize such ecosystems. Although all the ecosystems studied aim at scaling up innovative solutions, their strategy, governance and IP openness vary. An upstream strategy aimed at replacing supplier proprietary design with open substitutes requires a democratic governance and an intellectual property policy that maximize the attractiveness of the ecosystem. A downstream strategy aimed at carving a space in new markets requires an autocratic governance and an intellectual property policy that combine attractiveness and value capture opportunities.

PurposeDelivery 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.Design/methodology/approachThe 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.FindingsThe 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.Practical implicationsThis study offers a methodology for quantifying lost sales due to delivery delays and estimating how long the poor delivery performance affects retailers' order behaviour.Originality/valueThe 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.

Accurate demand forecasts are essential to supply chain management. We study the spatial demand variation of seasonal and unseasonal sport goods and demonstrate how demand forecast accuracy can be improved by using geostatistics and linking socio-economic and weather data with order line specific supply chain transactions. We found that the socio-economic features impact the demand of both seasonal and unseasonal products and unseasonal products are impacted more. Weather conditions affect only seasonal products. Cross-validation analyses show that using external information improves demand forecasting accuracy by reducing forecasting error up to 48%. The results can be applied both to the operational demand planning process and to the strategy used when making location-based decisions on supply chain actions, for example, deciding locations for new stores or running marketing campaigns.

High-energy physics studies collisions of particles traveling near the speed of light. For statistically significant results, physicists need to analyze a huge number of such events. One analysis job can take days and process tens of millions of collisions. Today the experiments of the large hadron collider (LHC) create 10 GB of data per second and a future upgrade will cause a ten-fold increase in data. The data analysis requires not only massive hardware but also a lot of electricity. In this article, we discuss energy efficiency in scientific computing and review a set of intermixed approaches we have developed in our Green Big Data project to improve energy efficiency of CERN computing. These approaches include making energy consumption visible to developers and users, architectural improvements, smarter management of computing jobs, and benefits of cloud technologies. The open and innovative environment at CERN is an excellent playground for different energy efficiency ideas which can later find use in mainstream computing.

In seasonal business, manufacturers need to make major supply decisions up to a year before delivering products to retailers. Traditionally, they make those decisions based on sales forecasts that in turn are based on previous season's sales. In our research, we study whether demand forecasts for the upcoming season could be made more accurate by taking into account the weather of the previous sales season. We use a ten-year dataset of winter sports equipment (e.g. skis, boots, and snowboards) sales in Switzerland and Finland, linked with daily meteorological data, for developing and training a generalised additive model (GAM) to predict demand for the next season. Results show a forecasting error reduction of up to 45% when including meteorological data from the past season. In our case, the value of this reduction in the forecasting error corresponds to around 2% of total sales. The results contribute to the theory of stochastic inventory control by showing that taking into account external disturbances, in this case the fluctuations in weather, improves forecasting accuracy in situations where the lag between ordering and demand is around one year.

Purpose - 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. Design/methodology/approach - 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. Findings - 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. Practical implications - 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). Originality/value - 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.

Practical Applications Summary A supply chain or supply network is a multicompany material-flow ecosystem, and operational data of customer companies can provide an indication of supplier companies’ future operations and business performance. In Abnormal Stock Returns Using Supply Chain Momentum and Operational Financials, published in the Winter 2017 issue of The Journal of Portfolio Management, authors Antti Paatela, Elias Noschis, and Ari-Pekka Hameri explain how an understanding of the interrelationships among supply chain partners’ businesses can provide an opportunity to gain abnormal investment returns in the stock market. Diverging from previous research, they focus on whole supply chains instead of single companies, on actual company financial data instead of more speculative stock market prices, and on the special dynamics of supplier–customer links.

Purpose - 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.Design/methodology/approach - 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.Findings - 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.Practical implications - 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.Originality/value - This research complements past research by showing that in spite of their synergetic potential, reducing inventory receives only limited attention in acquisitions.

We track patient flows through various departments in a large university hospital using data collected from over 100,000 visits during a three year period. By linking congestion crisis messages issued by the hospital management to variables describing patient length-of-stay, movements, bed occupancy rates, and labour hours we develop a statistical model to anticipate bottlenecks in the system to show that it is possible to predict congestion two to five days in advance. The developed method shows which variables are the most useful for explaining congestion and other patient flow issues in the case hospital. This advanced warning can be sufficient to avoid the congestion, since hospitals show an inherent capability to stretch their capacity, and vice versa, should it be needed. We compile our results into practical guidelines to complement existing patient flow management systems in hospitals.

By analyzing large data sets on jobs processed in major computing centers, we study how operations management principles apply to these modern day processing plants. We show that Little’s Law on long term performance averages holds to computing centers, i.e. work-in-progress equals throughput rate multiplied by lead time. Contrary to traditional manufacturing principles, the law of variation does not hold to computing centers, as the more variation in job lead times the better the throughput and utilization of the system. We also show that as the utilization of the system increases lead times and work-in-progress increase, which complies with traditional manufacturing. In comparison with current computing center operations these results imply that better allocation of jobs could increase throughput and utilization, while less computing resources are needed, thus increasing the overall efficiency of the center. From a theoretical point of view, a system with close to zero set-up times, as in the case of computing centers, the law of variation does not hold. We observe that the more variation in job lead times and resource usage, the higher the throughput of the system.

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – The study concerns one country and leisure goods, which can limit its generalizability. Practical/implications – 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. Originality/value – 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.