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Demand forecasting performance is subject to the uncertainty underlying the time series an organisation is dealing with. There are many approaches that may be used to reduce uncertainty and thus to improve forecasting performance. One intuitively appealing such approach is to aggregate demand in lowerfrequency 'time buckets'. The approach under concern is termed to as Temporal Aggregation and in this paper we investigate its impact on forecasting performance. We assume that the non-aggregated demand follows either a moving average process of order one or a first-order autoregressive process and a Single Exponential Smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical Mean Squared Error expressions are derived for the aggregated and non-aggregated demand in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant and the process parameters. Valuable insights are offered to practitioners and the paper closes with an agenda for further research in this area.

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International audienceIn a turbulent world, global competition and the uncertainty of markets have led organizations and technology to evolve exponentially, surpassing the most imaginary scenarios predicted at the beginning of the digital manufacturing era, in the 1980's. Business paradigms have changed from a standalone vision into complex and collaborative ecosystems where enterprises break down organizational barriers to improve synergies with others and become more competitive. In this context, paired with networking and enterprise integration, enterprise information systems (EIS) interoperability gained utmost importance, ensuring an increasing productivity and efficiency thanks to a promise of more automated information exchange in networked enterprises scenarios. However, EIS are also becoming more dynamic. Interfaces that are valid today are outdated tomorrow, thus static interoperability enablers and communication software services are no longer the solution for the future. This paper is focused on the challenge of sustaining networked EIS interoperability, and takes up input from solid research initiatives in the areas of knowledge management and model driven development, to propose and discuss several research strategies and technological trends towards next EIS generation

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Even if the research domain related to interoperability has been developed for more than ten years and particularly for the last eight years, the different kinds of interoperability and the different problems to solve need to be consolidated in order to define a real science. Moreover, because of the continuous evolution of enterprises in supply chains, interoperability problems must continuously be considered and solved in order to reach a sustainable interoperability. The objective of this prospective research paper is to discuss how System Theory (ST),applied to System of Systems, is able to support the development of Sustainable Enterprise Interoperability Science Base. After an introduction which reminds the definition of Enterprise Interoperability and the development of this domain in Europe, the system theory conceptsare introduced. Then,the requirements are described to support the determination of the necessary concepts to develop a Science Base for Sustainable Enterprise Interoperability. This part also describes how the concepts of System Theory meet the defined requirements. The fourth part presents a specific approach based on system theory in order to manage the evolution of interoperability in enterprises and to reach sustainable interoperability. Then last part illustrates this workwith a concrete example showing how ST concepts are used in GRAI methodology for instance to represent business process and decision interoperability problems.

Enterprise businesses are more than ever challenged by competitors that frequently refine and tailor their offers to clients. In this context, enterprise information systems (EIS) are especially important because: (1) they remain one of the last levers to increase the performance and competitiveness of the enterprise, (2) we operate in a business world where the product itself has reached a limit of performance and quality due to uniform capacity of industrial tools in a globalized economy and (3) the EIS can increase the product value thanks to additional digital services (built on data associated to the product) in order to meet and fit better client's needs. However, the use of EISs reaches a limit in collaborative environments because enterprises management methods diverge and EISs are mainly inflexible resource packages that are not built with an interoperability objective. Consequently, we need to make EISs interoperable in order to achieve the needed gains competitiveness and performance. This paper contribution can be summarized as follows: (1) it relates existing work and it examines barriers that, at the moment, are preventing further improvements due to current methodological and technological limits, and (2) it proposes a conceptual framework and five challenges that model based approaches must overcome to achieve interoperability between EIS in the near and long term.

To control their enterprises in a complex environment, decision-makers need to measure their enterprise regularly to perpetuate. For that, they use a specific set of performance indicators grouped in a coherent system named performance measurement systems (PMS). Such systems are generally defined and implemented using different methods. As business performance measurement appeared from the 1900s, a large number of approaches developed by researchers and practitioners have appeared since those years until today. They were not designed for the same purpose and on the same basis and each of them has advantages and disadvantages to measure optimally the performance. So, decision-makers have difficulties to choose among these methods the most appropriate to their needs when they want to design and implement their customised PMS. The objective of this paper is to present the main concepts that approaches are based on, to present a state of the art as exhaustive as possible of the approaches and methods themselves and to make a comparison between them in order to allow decision-makers to choose among them the one or a combination of several ones which would efficiently suit to their needs to reach their global objective of PMS design and implementation.