PurposeThis paper discusses and integrates the concept of complexity in the performance measurement and management (PMM) theory by providing a comprehensive framework to design and evaluate the overall coherence alignment of an indicators hierarchy in unstable and changing environment.Design/methodology/approachAn original, comprehensive and dynamic framework has been proposed and then applied on a sample case of a large-scale retail trade (LSRT) company, starting from relevant frameworks and criteria in the scientific literature.FindingsThis research shows that organizational changes may significantly impact the coherence alignment of an organization's indicators hierarchy. In addition, it finds that even though the alignment at the operational level is obtained, its effectiveness should be evaluated in relation to the organization's strategic orientation. Indeed, without assessing the strategic alignment of an indicators system, an aligned hierarchy at the operational level could lead to ineffective results.Research limitations/implicationsThis paper focuses on the topic of measuring the coherence inside an indicators hierarchy, which seems not to be addressed in the literature. Thus, it opens a new research stream, integrating the studies on performance indicators with an essential element that often causes flawed performance measures in organizations.Practical implicationsOrganizations could adopt this framework to design effective PMM systems and maintain them in light of the organizational changes.Originality/valueThis study introduces different metrics to evaluate the coherence and alignment of an indicators system, being one of the few research studies to address this topic in the context of complex and changing environments.

This article presents a comprehensive framework which identifies the relevant factors that can influence the standard adoption process, along with insights for performing a qualitative perspective analysis on the possible market diffusion of a specific standard under development or under review. This article also shows an example of framework application to the ISO 22400 standard, evidencing organizational and managerial implications on the standard adoption process.

The initial adoption phase of new production technologies is the period between the first production run or technology reconfiguration and the achievement of a stable target output. This time frame is generally characterized by productivity unsteadiness, quality performance variability, and unexpected machine failures together with increasing production volumes due to the process setup and instability, which inevitably affects production output. In this context, human performance represents an additional source of variability and process instability that is dependent on the workers’ productivity, learning curve and related training activities. Hence, to effectively assess the ramp-up phase of new production technologies, an appropriate evaluation of human performance is required. This paper proposes a comprehensive framework and criteria to perform a consistent assessment of the initial adoption phase of new production technologies by introducing two OEE measurement methodologies that distinguish between human performance, process configuration and technical features of the production technology. The proposed framework is then applied to and validated by a case study concerning the introduction of a semi-automatic packaging machine in a primary multinational company in the logistics industry. This case study shows the difference between the two OEE measures, along with the values interpretation and useful insights for achieving a stable production output.

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PurposeThis paper discusses the concept, definition and usage of Key Activity Indicators (KAIs) and their integration within a Performance Measurement and Management system (PMM).Design/methodology/approachThe actual definition and application areas of the KAIs are determined through a systematic literature review. Successively, a thorough definition of Key Activity Indicators is provided, along with a set of criteria for their deployment. Lastly, a case involving a Large Scale Retail Trade (LSRT) company is reported to report an example for guiding KAIs adoption.FindingsThis research shows that the scientific background concerning KAIs is still not mature. Moreover, the paper defines the role of KAIs for measuring operational activities and their possible connection with Key Performance Indicators (KPIs).Research limitations/implicationsAlthough KAIs have been introduced and discussed in the scientific literature; there is no evidence of criteria to deploy these indicators, leaving organizations without any guidance for their operational implementation.Practical implicationsFrom an academic standpoint, the study provides an overview of the usage of KAIs within the present scientific contributions, showing the advancements of this research field. From an industrial standpoint, the research proposes a set of criteria for the organizational deployment of KAIs.Originality/valueThe study investigates the concept of KAIs that, besides being originally conceived within World Class Manufacturing (WCM), has not received much attention in the scientific literature.

This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM.

Purpose: This technical note provides the mathematical demonstration for obtaining the optimal aspect ratio for a rectangular storage area with a lateral receive/ship dock, representing the standard configuration of modern distribution centers and logistic warehouses. The proposed aspect ratio is the one that minimizes the travel times of operators, keeping the common assumption of a storage area having a uniform access probability.Design/methodology/approach: To obtain the optimal aspect ratio of the storage area we model the entry point of the uniformly distributed dock with a random variable with a continuous uniform distribution, and we consequently evaluate the average travel path of the operator as a function of the latter. Successively, we estimate and minimize the average roundtrip length of the operator, leading to the optimal aspect ratio of the storage area.Findings: We find that the optimal aspect ratio between the warehouse width (U) and length (V) equals 1.5. The obtained result shows that the operators’ travel times are minimized with a storage area where .Research limitations/implications: Warehouses with a dock on one side now represent modern distribution centers' standard configuration. However, no optimal aspect ratio for the storage area has been discussed. For this reason, the paper fills this lack of scientific literature in the warehouse optimization research field by providing indications on how to design this class of warehouses.Practical implications: Distribution managers may find here guidance for defining a proper design of logistics centers and evaluating the operators' actual travel times to perform a roundtrip within the storage area.Originality/value: Traditional warehouse shape optimization models assume a single input/output point to the storage area. To our knowledge, no formal demonstration has been proposed for a warehouse with a dock on one entire side.

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