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The purpose of this paper is to present and test a new model for competitive capabilities. Traditionally, a cumulative model has been viewed as having one sequence of building competitive capabilities in a firm in support of market needs, including quality, delivery, cost efficiency and flexibility. Although appealing as a conceptual model, empirical testing has not been able to fully support the cumulative model. This paper acknowledges the need for a hybrid approach to managing capability progression. It brings together the literature on trade‐offs, cumulative capabilities, and order winners and qualifiers.

A new hybrid approach for modelling competitive capabilities is tested empirically using data from the high performance manufacturing (HPM) study, round 3, including three industries and seven countries – a total of 211 plants.

The hybrid model shows significantly better fit with the data from the sample than the cumulative models suggested by previous literature. Empirical support is found for the traditional perception that a high level of quality is a prerequisite for a high level of delivery performance. However, cost efficiency and flexibility do not exhibit a cumulative pattern. Instead, the results show that they are developed in parallel. The findings suggest that a balance between cost efficiency and flexibility is built upon high levels of quality and delivery performance.

Since we limit the empirical investigation to three industries and seven countries, it would be interesting to extend the testing of this model to more industries and countries. This research shows that combining perspectives and insights from different research streams – in this case, trade‐off theory and the concepts of cumulative capabilities, and order winners and qualifiers – can be fruitful.

The results of this paper provides managers with guidelines concerning the configuration of competitive capabilities. First, a qualifying level of quality needs to be attained, followed by a qualifying level of delivery. Then, a balance between potential order winners, i.e. cost efficiency and flexibility, needs to be attained.

This paper presents a new approach to modelling competitive capabilities that synthesises previous research streams and perspectives from cumulative capabilities, contesting capabilities (trade‐offs), and order winners and qualifiers.

Cumulative capability models are dominating frameworks explaining how manufacturing organizations gain their performance capabilities, such as quality, delivery, flexibility and cost. When innovation capabilities are excluded from the framework, the models are incapable of explaining how companies sustain substantive capabilities in a changing environment. Responding to this gap, the purpose of this paper is to propose and test a “sand cone” cumulative capability model that includes the innovation competitive performance alongside the competitive performance of quality, delivery flexibility and cost.

Two competing cumulative models were proposed. The extended cumulative capability model hypothesizes the development of innovation in sequence with other competitive performance dimensions. The affected with innovation cumulative model hypothesizes innovation performance as a predecessor of other performance dimensions. The models were tested using a multimethod approach on a representative sample of 500 manufacturing companies. An analysis of correlations among competitive performance, frequencies of plants following prescribed sequences, fit statistics of covariance-based structural equation modeling and analysis of strength and statistical significance of path coefficients enabled us to select a model that best represents the collected data.

The findings reveal that innovation competitive performance operates as a predecessor of quality, delivery, flexibility and cost and is developed in relation to these performance dimensions. The modified model also provides a theoretical explanation of how innovation performance helps to sustain reliable production systems that can perform consistently over time within a tolerable range of quality, delivery, flexibility and cost performance.

The results are significant for practitioners, especially for companies that are operating in volatile environments because the results provide insight on how to develop innovation competitive performance in relation to quality, delivery, flexibility and cost performance.

This study extends the cumulative capability models with innovation competitive performance. It advances the contingency approach on cumulative capability models.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

The purpose of this paper is to examine whether modular product design is an appropriate practice to improve manufacturers’ flexibility performance and cost performance as well as to evaluate whether combined effects of modular product design and delivery performance on flexibility performance and cost performance exist.

Structural equation modeling with moderating effects is used. Moderating effects allow an evaluation whether combined effects of modular product design and delivery performance exist. For the analysis, data from the international high-performance manufacturing survey are used.

Analysis results show a positive relationship between modular product design and cost performance, but do not show a significant moderating effect. Thus, no combined effect of modular product design and delivery performance exists in the data at hand.

A potential limitation of this study is the cross-sectional nature of the analysis. In order to test for causal relationships or chronological sequences, longitudinal data are deemed more suitable.

The findings make improvement processes more predictable and help managers to overcome traditional trade-off situations, especially in terms of flexibility performance and cost performance. Manufacturers are still neglecting the implementation of complementary methods for achieving an increase in flexibility while maintaining efficiency.

This paper complements prior research on the effect of improvement practices on operational performance dimensions. It also takes an alternative approach to examine whether a beneficial implementation sequence of improvement practices can be assumed.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

Much of the research literature in job shop scheduling deals with pure job shop environments. However, currently most processes involve a hybrid of both the job shop and a flow shop with a combination of flexible and conventional machine tools. Presents a study of such a job shop under varying conditions and performance criteria. Argues that for scheduling in this environment, certain combinations of scheduling rules should be utilized under different arrival rates and for different job types. A simulation model is developed using a hypothetical hybrid job shop to study the performance of rule combinations with variations in arrival rates and processing times. The performance criteria used are flowtime as a measure of work‐in‐process inventory, tardiness for JIT, and throughput for completed items inventory. It was found that rule combination performance varied with the performance criteria. Furthermore, it was found that the combinations were sensitive to arrival rates and processing times. Concludes, from the insights gained in the study, that the rule combination to be implemented should depend on the performance objective and the arrival rate/processing time condition of the hybrid job shop.

The aim of this study is to provide a holistic analysis of all possible maritime business logistics processes related to import and export shipments in a fuzzy environment through a case study of a maritime logistics company based on the as-is and to-be models within business process management (BPM).

The analyses considered the following perspectives: (i) in the stage of the process identification, the definition of the problem was carried out; (ii) in the stage of the process discovery, ocean department was divided into ocean export/import operation departments; ocean export/import operation were divided into freight collect/prepaid operation processes; ocean export/import logistics activity groups were broken down into sub-activities for freight collect/prepaid operation; the logistics activity groups and their sub-activities were defined; each sub-activity as either operation or documentation process group was classified; the durations of sub-activities were evaluated by decision-makers (DMs) as fuzzy sets (FSs); the monthly total jobs activities were estimated by DMs as FSs; the applied to monthly jobs activities of total shipments were estimated by DMs as FSs; the durations of each sub-activities were aggregated; the duration of the logistics activity groups and the sub-activities for per job were calculated; the cumulative workload of logistics activity groups and sub-activities were calculated; the duration of sub-activities for per job as operation or documentation departments were calculated, (iii) in the stage of the process analysis, cumulative ocean export/import workload as operation or documentation for freight collect/prepaid were calculated; duration of activity groups and sub-activities for per job as operation or documentation were calculated; cumulative workload activity groups and sub-activities as operation or documentation were calculated, (iv) in the stage of the process redesign, cumulative workload, process cycle time as operation and documentation group and required labor force were calculated; the process cycle time of the theoretical, the as-is model and the to-be model were calculated: (i) the theoretical minimum process cycle time without resource were calculated by the critical path method (CPM), (ii) the process cycle time of the as-is model perspective with the 1 person resource constraint and (iii) the process cycle time of the to-be model perspective with the 2-person resource constraint were calculated by the resource constrained project scheduling problem (RCPSP) method.

The methodology for analyzing the ocean department operation process was successfully implemented in a real-life case study. It is observed that the results of the to-be model can be applicable for the company. The BPM-proposed methodology is applicable for the maritime logistics industry in the present study; however, it can be applied to other companies in maritime logistics as well as other industries.

This study contributes to research using BPM methodology in maritime logistics. This is the first study the logistics process analyses were carried out in terms of including all operation processes for a company. All processes were analyzed by using BPM methodology in maritime logistics. This study demonstrated the application of the BPM as-is and to-be models to maritime logistics. The as-is and the to-be models of the BPM methodology were applied in maritime logistics.

This methodology applied in this study can enable organizations operating in the time-urgent maritime logistics sector to manage their logistics processes more efficiently, increase customer satisfaction, reduce the risks of customer loss due to poor operational performance and increase profits in the long term. Through the use of these methodologies utilizing FSs, the CPM and the RCPSP methods, this study is expected to make contributions to the BPM literature and provide original insights into the field. Furthermore, this study will undertake a comprehensive analysis of maritime logistics with respect to BPM to deliver noteworthy contributions to the maritime logistics literature and provide original perspectives into the field.

Many indicators attempt to measure the social performance of a company from different perspectives. Grounded in stakeholder theory, this paper aims to propose capitalising the economic value distributed annually to society over a period of time, hereafter called a firm’s cumulative contribution to society (CCS). This can be done by including everything that stakeholders value; for example, payments of taxes, remuneration of employees, payments to suppliers and creditors, donations, dividends, research and development expenses and efforts to improve the environment.

First, this paper makes a methodological proposal about how to calculate the CCS and discusses potentials and shortcomings. Then, a set of hypotheses are formulated about the firm characteristics and country attributes that make the most positive contribution to society such as business models, financial performance, a country’s human development, income equality and the extent of its shadow economy. The authors also argue that a company that originally contributes to society will continue to do so because of the structural inertia faced by organisations. The hypotheses were validated with an empirical study conducted with a sample of 9,276 new-born European companies.

The most significant contributors to society are large, profitable companies, which are leveraged but solvent, with high asset turnover and high-profit margins and which are productive and pay high wages. Unfortunately, this win-win situation describes a small percentage of the explained variance, which can explain why social and financial performance sometimes do not go hand-in-hand. The paper identifies features of other types of companies that contribute to society, suggesting criteria for socially responsible investors. Country development favours the cumulative contribution that firms make to society.

Most accounting systems do not collect all the information necessary to calculate a refined version of the indicator such as percentage of purchases from local suppliers, percentage of salaries for executives and disabled employees and percentage of financing from socially responsible financial entities. The authors encourage modification of the accounting systems to include those aspects.

This paper identifies several types of companies that contribute the most to society from a modest set of financial indicators. Socially responsible investors can estimate their contribution to society, devising new investment criteria.

The paper identifies several types of companies that contribute the most to society from a modest set of financial indicators. Socially responsible investors can estimate their contribution to society, devising new investment criteria.

The paper makes two contributions, one methodological and the other empirical. By applying a financial methodology, the authors propose to capitalise the contributions of a company over a period of time. The empirical study identifies both firm and country characteristics that explain CCS.

The purpose of this paper is to develop a tool, “The Excellence Grid,” to categorize attributes on the basis of their ability to impact customer perception of “excellence” in service compared to perception of “good” service. In addition, provide a three dimensional (3D) model for excellence-performance analysis, which can aid managers in formalizing the strategies for building perceptions of excellence about the service.

The positive zone of performance is analyzed through a two-function modeling technique of ordinal logistic regression (OLR) with the non-proportional odds to categorize attributes on grid. Tool is applied to two case studies to validate and establish the asymmetric impact of attributes on perceptions of “good service” and “excellent service.”

Similar to the Kano model for impact of attributes on positive and negative performances, findings from cases confirm the asymmetric impact of attributes on the positive zone of performance and establish “Excellence Grid” as a means to categorize attributes as drivers of excellence.

The “Excellence Grid” tool is expected to empower managers to focus on strategies directed toward the goal of “service excellence” and recommends that managers should not only strive for process improvement, but also sharpen the external communication of service excellence.

The “Excellence Grid” and the “3D Excellence-Performance model,” proposed in this research, are expected to enrich the body of knowledge on operational tools to achieve service excellence. Using parameter estimates of the two-function model of OLR for service quality has not yet been reported in open literature.