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The present study aimed to assess performance of Industry 4.0 (I4.0) in case organization by considering potential performance measures and analysis using scoring approach.

50 performance measures grouped into five dimensions namely manufacturing management, manufacturing economics, manufacturing strategy, manufacturing technology and workforce were considered for the analysis. The study had been done with relevance to automotive component manufacturing organization. Further, questionnaire for each performance measure was developed to gather expert inputs regarding different performance aspects of I4.0 in case organization. Reliability of the expert responses towards questionnaire was assessed by computing Cronbach's alpha (a) using Statistical Package for the Social Sciences (SPSS) software.

Findings of the study revealed overall I4.0 performance index (OIPI) of 0.71, i.e. 71% signifying improvement scope of 29% pertaining to I4.0 adoption. Gap analysis was performed across dimensions and performance measures to realize the weaker areas. Gap analysis revealed workforce dimension with highest gap and manufacturing management with lowest gap. The gaps that obstruct performance of I4.0 are being recognized and proposals for improvement were provided to the industrial practitioners. Based on further analysis, dimensions and performance measures found to be weaker.

The study helped industrial practitioners and managers to create the foundation for evaluating performance of I4.0-focused organization. Industry practitioners can employ the study to understand different performance measures with respect to different dimensions and realize the significance of I4.0 adoption.

The identification of performance dimensions and measures for I4.0 performance measurement and assessment using scoring approach is the original contribution of the authors.

Equipment performance helps the manufacturing sector achieve operational and financial improvements despite process variations. However, the literature lacks a clear index or metric to quantify the monetary advantages of enhanced equipment performance. Thus, the paper presents two innovative monetary performance measures to estimate the financial advantages of enhancing equipment performance by isolating the effect of manufacturing fluctuations such as product mix price, direct and indirect characteristics, and cost changes.

The research provides two measures, ISB (Improvement Saving Benefits) and IEB (Improvement Earning Benefits), to assess equipment performance improvements. The effectiveness of the metrics is validated through a three stages approach, namely (1) experts' binary opinion, (2) sample, and (3) actual cases. The relevant data may be collected through accounting systems, purpose-built software, or electronic spreadsheets.

The findings suggest that both measures provide an effective cost–benefit analysis of equipment performance enhancement. The measure ISB indicates savings from performance increases when equipment capacity is greater than product demand. IEB is utilised when equipment capacity is less than product demand. Both measurements may replace the unitary cost variation, which is subject to manufacturing changes.

Manufacturing businesses may utilise the ISB and IEB metrics to conduct a systematic analysis of equipment performance and to appreciate the financial savings perspective in order to emphasise profitability in the short and long term.

The study introduces two novel financial equipment performance improvement indicators that distinguish the effects of manufacturing variations. Manufacturing variations cause cost advantages from operational improvements to be misrepresented. There is currently no approach for manufacturing organisations to calculate the financial advantages of enhancing equipment performance while isolating production irregularities.

This research studies the development of the evolving dynamic system model and explores the important elements or factors and what detailed attributes are the main influences model in achieving the success of a business, industry and management. It also identifies the real and major differences between static and dynamic business management models and the detailed factors that influence them. Later, this research investigates the benefits/advantages and limitations/disadvantages of some research studies. The studies conducted in this research put more emphasis on the capabilities of system dynamics (SD) in modeling and the ability to measure, analyse and capture problems in business, industry, manufacturing etc.

The research presented in this work is a qualitative research based on a literature review. Publicly available research publications and reports have been used to create a research foundation, identify the research gaps and develop new analyses from the comparative studies. As the literature review progressed, the scope of the literature search was further narrowed down to the development of SD models. Often, references to certain selected literature have been examined to find other relevant literature. To do so, a supporting tool (that connects related articles) provided by Google Scholar, Scopus, and particular journals has been used.

The dynamic business and management model is very different from the static business model in complexity, formality, flexibility, capturing, relationships, advantages, innovation model, new goals, updated information, perspective and problem-solving abilities. The initial approach of a static system was applied in the canvas business model, but further developments can be continued with a dynamic system approach.

Based on this study, which shows that businesses are developing more towards digitalisation, wanting the ability to keep up with the era that is moving so fast and the desire to increase profits, an instrument is needed that can help describe the difficulties of the needs and developments of the future world. This instrument, or tool of SD, is also expected to assist in drawing future models and in building a business with complex variables that can be predicted from the beginning.

This study will contribute to the SD study for many business incubator research studies. Many practical in business incubator management to have a benefit how to achieve the business performance management (BPM) in SD review.

The significant differences between static and dynamics to be used for business research and strategic performance management. This comparative study analyses some SD models from many authors worldwide. Their goals behind their strategic business models and encounter for their respective progress.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

A quantitative theoretical model is proposed to measure how productivity performance can be affected by strategic decisions related to specific competitive priorities.

This study proposes the Primary Transformation Model (PTM) and an equation to measure cause-and-effect relationships between productivity and competitive priorities.

The interdependence between productivity and competitive priorities was studied using the PTM and the proposed model indicates that strategies that improve external performance also impact internal productivity. It was also observed that the compatibility between competitive priorities depends on the initial manufacturing conditions and the implementation method adopted.

The proposed model is theoretical and, as such, is an abstraction of reality and does not consider all possible aspects. It consists of a novel approach that still requires further empirical testing. The PTM provides insights about the trade-offs between productivity and strategic objectives, as well, contributes to the ongoing research on manufacturing strategy and can be further developed in future studies.

The main practical implication is to allow companies to relate their strategic decisions to their productivity performance.

This research also contributes to societal issues by enabling firms to better align strategic objectives and operations, which ultimately allows offering products more suited to the needs of customers, thus making better use of the required resources and favoring economic growth.

The model proposed allows objective assessment of actions aiming at operational efficiency and effectiveness, in addition to providing insights into cause-and-effect relationships between productivity and competitive priorities. The model can also be used in empirical investigations on manufacturing strategy.

The purpose of this study was to improve the overall equipment effectiveness (OEE) of the production process of the shredder operation of ABC company, an industrial waste management company which supplies pre-processed industrial waste as alternative fuel to a cement plant.

This case study investigated all possible availability and performance losses that caused the shredder system’s OEE and various problem-solving techniques, such as root cause analysis and Pareto analysis, were used to find the root cause of the reduced OEE.

After analysing this case study, three significant loss factors were identified from all the availability and performance losses, which caused the shredder system’s OEE losses. Practical solutions were found for the effect of those loss factors to improve the machine’s OEE and productivity.

This case study has been concentrated on only analysing of losses and improvement of OEE in the production process and not about cost analysis between loss and improvements.

This paper shows how to improve the OEE of a production process through various problem-solving techniques by identifying its losses and how to achieve the best solutions for those losses in a practical manner.

This study aims to determine whether and how objectives and key results (OKRs) can be used to solve performance measurement issues encountered by organizations.

A total of 204 staff members from 26 Thai organizations that adopted OKRs were interviewed. Five senior executives and five operational staff members with experience using OKRs were selected from each organization. Content analysis was also performed.

OKRs facilitate the acceptance of performance indicators and help solve issues of alignment between indicators and organizational strategies as well as improper target setting.

The results have limited generalizability because of the qualitative approach undertaken in the study. Further research can test whether the results hold true if OKRs are used for longer than six months.

The results of this study can be used to help managers and employees set challenging targets, utilize their competencies and find a sense of relatedness, which can lead to organizational success.

This study is one of the first to thoroughly investigate the use of OKRs by adopting the self-determination theory (SDT) as the main theoretical framework.

This paper aims to present a simple and innovative fuzzy methodology-based lean performance measurement system (L-PMS) for an Indian automotive supply chain. The paper also enlightens the influence of coronavirus disease 2019 (COVID-19) on supply chains and the practical implications of the unprecedented disruptions on the performance measurement systems.

The L-PMS is divided into three phases. In the first phase, the key performance indicator (KPI) list, as deemed fit by the organization, is prepared using literature and suggestions from the case organization. The list contains 61 KPIs measuring 24 performance dimensions in seven functional areas of the supply chain. In the second phase, the KPI performance data (actual, best and worst) are collected using the enterprise resource planning (ERP) system. In the last phase, the leanness score of the case organization is calculated at four levels – KPI, dimension, functional area and overall organization.

The overall leanness score of the case organization is 60%. The case organization uses KPIs from all seven functional areas, but it needs to improve the number of KPIs in administration and supplier management functional areas. The case organization uses only quantitative KPIs. However, the performance dimensions at the middle level are adequate. The leanness level of the case organization in different areas is highly variable (ranges from 45% to 91%).

The major limitation of the study is that the case study is done at a single organization.

The managers at the different levels of the hierarchy can use the lean performance measurement score to leverage the better performing areas/dimensions/KPIs and improve poor performing areas/dimensions/KPIs. The lean performance measurement at functional area level can help leadership to give responsibility to different people for the improvement of leanness with respect to different dimensions/functional areas. The disruptive impact of COVID-19 should clearly be understood by the managers to make appropriate decisions based on the severity as measured at different levels.

According to the authors' best knowledge, this is the first lean performance measurement application at the four hierarchical levels (KPI, performance dimension, functional area and overall organization).

The purpose of this study is to discuss the construction of a structural measurement model utilizing structural equation modelling (SEM) to confirm the link between Industry 4.0 technologies, sustainable manufacturing practices and organizational sustainable performance. Relationship among the paradigm has yet to be fully investigated, necessitating a more conceptual and empirical examination on what impact they have on organizational sustainable performance when used together.

Industry 4.0 and sustainable production practices aim to progress a company's business competitiveness, forming sustainable development that benefits manufacturing companies. The aim of the study is to analyze the relationship between constructs that lead to operational excellence in firms that use Industry 4.0 technologies and sustainable manufacturing techniques. Experts from diverse automotive industries, who are applying both Industry 4.0 and sustainable manufacturing practices, provided data for the study.

Statistical estimations (hypotheses) are created to substantiate the measurement model that has been developed. The structural model was analysed, and the findings were discussed. The statistical estimate is either approved or rejected based on the findings. According to the conclusions of this study, strong link exists between Industry 4.0 technologies and sustainable manufacturing practices that affect organizational sustainable performance environmentally, economically and socially.

The research was conducted in the framework of automobile component manufacturing companies in India. The outcomes of the study are practically feasible.

The authors' novel contribution is the construction of a structural model with Industry 4.0 technologies and sustainable manufacturing practices into account.

COVID-19 is a global event affecting supply chain operations and human health. With COVID-19, many issues in business models, business processes and supply chains, especially in the manufacturing industry, have had to change. The ability to analyze supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19. Therefore, it aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains.

In this study, a new circular-SCOR model is proposed, and 17 supply chain performance measurement criteria are prioritized for a manufacturing company in the context of circular economy principles during COVID-19 by using stepwise weight assessment ratio analysis and analytical hierarchy process method, separately.

As a result, for both methods, in the case study discussed, the demand fulfillment rate is determined as the most prominent criterion in line with the circular economy principles in the COVID-19 period in manufacturing supply chains.

It is expected that this study will contribute to managers and policy makers as it addresses the “new normal” that started after COVID-19 and the criteria to be considered in supply chain performance measurement and emphasizes the need to adopt circular supply chains, especially in manufacturing industries.