Performance Measurement Metrics Research Articles

The Influence of Lean Practices and Leadership on Business Excellence: Malaysian E&E Manufacturing Companies

The objectives of this study are to adopt the well-known business excellence model (BEM), Baldrige excellence framework 2019-2020 criteria and examine in Malaysian Electrical and Electronics (E&E) manufacturing companies. We have identified those elements in BEM like leadership, customers, measurement, analysis, knowledge management (MAKM), strategy, and operations that influence on business results. This study also aims to assess the Lean practices influence on the operations and results of business excellence (BE). The theoretical framework is guided by BEM and Leadership theory. A stratified random sampling was employed to choose the sample from the four sub-sectors of E&E industry. A total of 500 questionnaires was circulated in Peninsular Malaysia with return usable 156 questionnaire utilized for data analysis. The outcome from the structural analysis suggested that positive relationships between strategy, operations, and Lean practices on achievement of BE. The limitations from present study suggest a future research may focus on other industries, Industry 4.0 companies and also ascertain what are the leadership behaviours more favourable to achieve BE. The conclusions of this study extend the knowledge by assisting theory building efforts especially in the operations management domain and BE management. The practical implication is adoption of Baldrige excellence framework and Lean practices will enhance infrastructural decision in fields of business strategy such as human resource policies, best practices, benchmarking and quality management. This study contributes to the methodology perspective by covering both economic and non-economic performance measure metrics.

Design, Simulation, Fabrication, and Characterization of an Electrothermal Tip-Tilt-Piston Large Angle Micromirror for High Fill Factor Segmented Optical Arrays.

Micro-electromechanical system (MEMS) micromirrors have been in development for many years, but the ability to steer beams to angles larger than 20° remains a challenging endeavor. This paper details a MEMS micromirror device capable of achieving large motion for both tip/tilt angles and piston motion. The device consists of an electrothermal actuation assembly fabricated from a carefully patterned multilayer thin-film stack (SiO2/Al/SiO2) that is epoxy bonded to a 1 mm2 Au coated micromirror fabricated from an SOI wafer. The actuation assembly consists of four identical actuators, each comprised of a series of beams that use the inherent residual stresses and coefficient of thermal expansion (CTE) mismatches of the selected thin films to enable the large, upward, out-of-plane deflections necessary for large-angle beamsteering. Finite element simulations were performed (COMSOL v5.5) to capture initial elevations and tip/tilt motion displacements and achieved <10% variance in comparison to the experiment. The measured performance metrics of the micromirror include tip/tilt angles of ±23°, piston motion of 127 µm at sub-resonance, and dynamics characterization with observed resonant frequencies at ~145 Hz and ~226 Hz, for tip/tilt and piston motion, respectively. This unique single element design can readily be scaled into a full segmented micromirror array exhibiting an optical fill-factor >85%, making it suitable for optical phased array beam control applications.

Digital supply chain performance metrics: a literature review

PurposeIn the previous decade, a substantial amount of research has been undertaken to measure the digitalized supply chain (DSC) performance. This paper aims to present a systematic literature review on DSC performance measurement metrics to apprehend current practices, recognize gaps and advocate future research itineraries.Design/methodology/approachTo guarantee a replicable, rigorous and transparent research process, the authors used a systematic literature review methodology to synthesize the research. A combination of 25 keywords was used to obtain 248 scientific studies in the first step. The balance scorecard (BSC) model was used to categorize 299 gathered performance metrics into 4 BSC perspectives.FindingsThe review highlighted the need for qualitative performance measuring metrics for DSC. During the review, only one study was identified that primarily focused on developing performance metrics for DSC. Additionally, the review identified that metrics related to internal and financial perspectives received the most attention while the “growth and learning” perspective received the least attention. The review also identified that external partners, such as distributors and suppliers, were virtually ignored in previous literature.Originality/valueAlthough numerous literature reviews have been conducted in the past on the performance measuring metrics for supply chain management, no literature review aiming to synthesize the measuring metrics for DSC has yet been undertaken.

Intelligent feature selection using particle swarm optimization algorithm with a decision tree for DDoS attack detection

The explosive development of information technology is increasingly rising cyber-attacks. Distributed denial of service (DDoS) attack is a malicious threat to the modern cyber-security world, which causes performance disruption to the network servers. It is a pernicious type of attack that can forward a large amount of traffic to damage one or all target’s resources simultaneously and prevents authenticated users from accessing network services. The paper aims to select the least number of relevant DDoS attack detection features by designing an intelligent wrapper feature selection model that utilizes a binary-particle swarm optimization algorithm with a decision tree classifier. In this paper, the Binary-particle swarm optimization algorithm is used to resolve discrete optimization problems such as feature selection and decision tree classifier as a performance evaluator to evaluate the wrapper model’s accuracy using the selected features from the network traffic flows. The model’s intelligence is indicated by selecting 19 convenient features out of 76 features of the dataset. The experiments were accomplished on a large DDoS dataset. The optimal selected features were evaluated with different machine learning algorithms by performance measurement metrics regarding the accuracy, Recall, Precision, and F1-score to detect DDoS attacks. The proposed model showed a high accuracy rate by decision tree classifier 99.52%, random forest 96.94%, and multi-layer perceptron 90.06 %. Also, the paper compares the outcome of the proposed model with previous feature selection models in terms of performance measurement metrics. This outcome will be useful for improving DDoS attack detection systems based on machine learning algorithms. It is also probably applied to other research topics such as DDoS attack detection in the cloud environment and DDoS attack mitigation systems.

Performance measurement in smart city governance: a case study of an Egyptian city council

PurposeThis study seeks to explore the powerful role(s) of institutionalised performance measurement systems or metrics in smart city governance in a politically and militarily sensitive developing country.Design/methodology/approachThis study extends the application and contribution of a multi-level institutional framework to previous management accounting literature on the potential relationship between performance measurement and smart city governance. The value of utilising a multi-level framework is to broaden and deepen theoretical analyses about this relationship to include the effect of political pressure from the military regime at the macro level on the institutionalisation of a performance measurement system at the micro-organisational level. Taking the New Cairo city council smart electricity networks project (Egypt) as an interpretive qualitative single-case study, data collection methods included semi-structured interviews, direct observations and documentary readings.FindingsPerformance measurement systems or metrics, especially in politically and militarily sensitive smart cities, constitutes a process of cascading (macro-micro) institutionalisation that is closely linked to sustainable developments taking place in the wider arena of urban policies. Going a step further, accounting-based performance metrics, arising from political and military pressures towards public-private collaborations, contribute to smart city management and accountability (governance). Institutionalised measurement systems or performance metrics play a powerful accounting role(s) in shaping and reshaping political decisions and military actions in the city council.Originality/valueTheoretically, this study goes beyond the cascading institutionalisation process by arguing for the powerful role(s) of institutionalised accounting and performance measurement systems in smart city decision-making and governance. Empirically, it enriches previous literature with a case study of a developing Arab Spring country, characterised by an emerging economy, political sensitivity and military engagement, rather than developed and more stable countries that have been thoroughly investigated. It is also among the first politically engaged accounting case studies to highlight public-private collaborations as a recent reform in public sector governance and accountability.

Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment

In addition to the focus of research selected as a Final Project material, the selection of lecturers as student's supervisor becomes very important. The lecturer's competence related to the focus of student research and the supervising style of lecturers is also very influential on the final results. Measurement of style appropriateness between students' learning styles and supervising lecturers' styles can benchmark the quality of the final project's implementation, especially higher education institutions. This study has applied fuzzy-based assessment to build objective perceptions of students' learning characteristics and lecturers' characteristics (Visual (V), Auditory (A), Kinesthetic (K)) as supervisors through questionnaire processing that has designed in such away. Hence, it is suitable for this study. The measuring technique of the percentage of overlapping areas under the curves and the correlation test between a pair of curves have been used as performance measurement metrics. In general, the study results indicate a significant level of coverage adequacy for all research variables regarding existing conditions. It means that the process of Final Project activities in terms of students' and lecturers' learning characteristics as supervisors and their distribution is at a reasonable level (88.38%). It has also been shown by the results of the correlation test of the appropriateness of choice, both supervisors selected by students (0.8657) and students chosen by lecturers (0.9897) who are at a very significant level of similarity. Correlation tests conducted for similarities between students' and lecturers' learning characteristics as supervisors show almost no significant correlation between them (0.4064).

Hyperledger Fabric Blockchain for Securing the Edge Internet of Things.

Providing security and privacy to the Internet of Things (IoT) networks while achieving it with minimum performance requirements is an open research challenge. Blockchain technology, as a distributed and decentralized ledger, is a potential solution to tackle the limitations of the current peer-to-peer IoT networks. This paper presents the development of an integrated IoT system implementing the permissioned blockchain Hyperledger Fabric (HLF) to secure the edge computing devices by employing a local authentication process. In addition, the proposed model provides traceability for the data generated by the IoT devices. The presented solution also addresses the IoT systems’ scalability challenges, the processing power and storage issues of the IoT edge devices in the blockchain network. A set of built-in queries is leveraged by smart-contracts technology to define the rules and conditions. The paper validates the performance of the proposed model with practical implementation by measuring performance metrics such as transaction throughput and latency, resource consumption, and network use. The results show that the proposed platform with the HLF implementation is promising for the security of resource-constrained IoT devices and is scalable for deployment in various IoT scenarios.

Addressing sustainability or following political climate Rhetoric? Anatomy of government Agency’s performance management

This paper examines how environmental sustainability is addressed, acted on, measured and communicated by the performance management system of a government agency. The Finnish Transport Safety Agency (Trafi) is the researched object, the activities and services of which cover a wide range of environmental sustainability goals set at different levels. The goals originate from international agreements, national strategies, performance agreements between the agency and the governing ministry, as well as from in-agency strategies and managerial planning. The results of the case research suggest that, instead of balanced and holistic addressing the different environmental sustainability dimensions, the agency’s portfolio of activities focused on climate change. More extensive sustainability goals did not trickle down to action or performance measurement metrics, but topical political discussions and pressures had narrowed the scope down. The analysis concludes with suggestions on how the existing performance management systems could be improved in the future.

Rate normalization for sweat metabolomics biomarker discovery

As the demand for real-time exercise performance feedback increases, excreted sweat has become a biosource of interest for continuous human performance assessment. For sweat to truly fulfill this requirement, analyte concentrations must be normalized to adequately assess day-to-day differences within and among individuals. In this manuscript, data are presented highlighting the use of accurate localized sweat rate as a means for ion and global metabolomic data normalization. The results illustrate large sweat rate variability among individuals over the course of two distinct exercises protocols. Furthermore, the data show sweat rate is not symmetrical at similar locations among right and left forearms of individuals (p = 0.0007). Sweat ion conductivity analysis suggest overall sweat rate normalization reduces variability collectively among ion values and participants with principal component analysis showing 77.8% of variation in the data set attributable to sweat rate normalization. Global metabolomic analysis of sweat illustrated overall rate normalization increases the variability among test subjects with 72.7% of the variation explained by sweat rate normalization. Finally, overall rate normalized metabolomic features of sweat significantly correlated (ρ ≥ 0.7, ρ ≤ −0.7) with measured performance metrics of the individual, establishing the potential for sweat to be used as a biosource for performance monitoring. Collectively, these data illustrate the importance of accurate localized sweat rate determination, for analyte data normalization, in support for the use of sweat in biomarker discovery efforts to predict human performance.

Optimized gene selection and classification of cancer from microarray gene expression data using deep learning

Cancer is the major leading reason of death around the world. However, the early identification and prediction of a cancer type is very critical for patient’s health. Recently, microarray gene expression data was utilized for efficient and early diagnosis of cancer. Previous work shows that microarray data has two major issues which are high dimensionality and small sample size. Several researchers have analyzed and evaluated the cancer classification problem using different statistical and machine learning-based approaches but there are still some issues with these approaches that make cancer classification a nontrivial task. Such as, the inability of certain machine learning algorithms to use unstructured data has limited their utility in the cancer classification process. Convolutional neural networks are proven to very suitable to analyze variety of unstructured data. This ability allowed the deep learning algorithms to play a vibrant part in early detection of cancer through data classification. In this research, a hybrid deep learning model based on Laplacian Score-Convolutional Neural Network (LS-CNN) is employed for the classification of given cancer’s data. The performance of the proposed system was evaluated on 10 different benchmark datasets using various performance measurement metrics such as accuracy and confusion matrix. The experimental results conclude that proposed LS-CNN model outperformed compared to traditional machine learning and recently used deep learning approaches.

Performance analysis of melanoma classifier using electrical modeling technique.

An efficient and novel modeling approach is proposed in this paper for identifying proteins or genes involved in melanoma skin cancer. Two types of classifiers are modeled, based on the chemical structure and hydropathy property of amino acids. These classifiers are further implemented using NI LabVIEW-based hardware kit to observe the real-time response for proper diagnosis. The phase responses, pole-zero diagrams, and transient responses are examined to screen out the genes related to melanoma from healthy genes. The performance of the proposed classifier is measured using various performance measurement metrics in terms of accuracy, sensitivity, specificity, etc. The classifier is experimented along with a color code scheme on skin genes and illustrates the superiority in comparison with traditional methods by achieving 94% of classification accuracy with 96% of sensitivity.Graphical abstract An equivalent electrical model is developed for designing melanoma classifier. Initially, each amino acid is modeled using the RC passive circuit depending on their physicochemical structure and hydropathy nature, to form a gene structure model. The melanoma-related genes are detected by phase, transient, and color code analysis.

Modeling continuous security: A conceptual model for automated DevSecOps using open-source software over cloud (ADOC)

Agile software development methodology and DevOps, together, have helped the business to achieve agility and velocity in delivering time-to-market applications and services. Open-source software (OSS) and cloud technologies are taking up business innovation and DevOps at new heights. However, in the quest of agility and velocity, user data security and privacy assurance often get lower priority as they are perceived as a time-consuming activity requiring specialized people, process, and technology. We see this problem being addressed by integrating security in DevOps processes. Security for DevOps has been institutionalized as DevSecOps with practical considerations for a given business context. In this work, we proposed a conceptual security model, ADOC, to facilitate adopting DevSecOps for the business processes capitalizing OSS over the cloud. This work contributes towards the following to integrate continuous security in application and service delivery: (i) A continuous security conceptual framework proposal based on the requirements elicited from the analysis of challenges in adopting DevSecOps using OSS over the cloud. (ii) An integrationist security model, ADOC, based on the proposed continuous security conceptual framework, integrating development, security, and operation activities through automation of security controls using OSS over the cloud. (iii) A set of inter-working OSS tools for automation of the proposed security controls in ADOC workflow and practices. (iv) A set of metrics for performance measurement of the ADOC model. (v) Mapping of the solutions for the analyzed challenges using the proposed security controls, followed by a use case scenario to adopt the ADOC workflow and continuous practices. The ADOC transforms security being adhoc compliance-oriented activities into continuous assurance-oriented activities by codifying security controls into an automated delivery workflow. Its practical adoption enables businesses to deliver time-to-market security ready applications and services with accelerated velocity and sustainable agility in a cost-effective way.

A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection

Due to the launch of new applications the behavior of internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely, Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Nonlinear Principal Component Analysis (NLPCA) have been studied and analyzed. Secondly, performance of four classifiers, namely, Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naïve Bayes (NB) has been studied for the actual and reduced datasets. In addition, novel performance measurement metrics, Classification Difference Measure (CDM), Specificity Difference Measure (SPDM), Sensitivity Difference Measure (SNDM), and F1 Difference Measure (F1DM) have been defined and used to compare the outcomes on actual and reduced datasets. Comparisons have been done using new Coburg Intrusion Detection Data Set (CIDDS-2017) dataset as well widely referred NSL-KDD dataset. Successful results were achieved for Decision Tree with 99.0 percent and 99.8 percent accuracy on CIDDS and NSLKDD datasets respectively.

Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements

Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving comparative parameters independent of the datasets. Measure invariance properties make metrics more or less informative, particularly on unbalanced, monomodal, or multimodal negative class datasets and for real or simulated datasets. Although several studies applied ML to detect and classify TEs, there are no works evaluating performance metrics in TE tasks. Here, we analyzed 26 different metrics utilized in binary, multiclass, and hierarchical classifications, through bibliographic sources, and their invariance properties. Then, we corroborated our findings utilizing freely available TE datasets and commonly used ML algorithms. Based on our analysis, the most suitable metrics for TE tasks must be stable, even using highly unbalanced datasets, multimodal negative class, and training datasets with errors or outliers. Based on these parameters, we conclude that the F1-score and the area under the precision-recall curve are the most informative metrics since they are calculated based on other metrics, providing insight into the development of an ML application.

DSDV Extension to Enhance the Performance of Ad Hoc Networks in High Diverse-Velocity Environments

&lt;p class="0abstractCxSpFirst"&gt;Mobile Ad-Hoc Networks (MANETs) are characterized as decentralized control networks. The mobile nodes route and forward data based on their routing information without the need for routing devices. In this type of networks, nodes move in an unstructured environment where some nodes are still fixed, others are moving in a constant velocity, and others move with diverse velocities; and thus, they need special protocols to keep track of network changes and velocity changes among the nodes. Destination Sequenced Distance-Vector (DSDV) routing protocol is one of the most popular proactive routing protocols for wireless networks. This protocol has a good performance in general, but with high speed nodes and congested networks its performance degrades quickly.&lt;/p&gt;&lt;p class="0abstractCxSpLast"&gt;In this paper we propose an extension to the DSDV (we call it Diverse-Velocity DSDV) to address this problem. The main idea is to modify the protocol to include node speed, determine update intervals and the duration of settling time. To evaluate the performance of the new protocol, we have carried a number of simulation scenarios using the Network Simulator tool (NS-3) and measured relevant parameters such as: packet delivery ratio, throughput, end-to-end delay, and routing overhead. We have compared our results with the original DSDV and some of its new variants. The new protocol has demonstrated a noticeable improvement of performance in all scenarios, and the measured performance metrics outperform the others except the average delay where the performance of the new protocol was modest.&lt;/p&gt;

Performance management in hybrid organisations: A study in social housing

The nature and role of social housing across the European States is growing more diverse every decade. From the wide range of social housing as a percentage of total housing stock, the many different housing options provided, to the vast array of housing allocation mechanisms and differing levels of equity delivered by such mechanisms, social housing across Europe presents an increasingly complex social challenge. As such a one-size-fits-all solution to these challenges is unlikely to present itself, and researchers are therefore forced to focus on the specifics of a region or state—this is the case with this study.Within the UK, Housing Associations (HAs) have played a fundamental role within successive government social housing policies for at least three decades. However, through a succession of legislative changes, welfare reform and the deregulation of their non-profit social role, HAs have been fundamentally challenged, and are now exposed to competition from private registered providers.This study poses the overarching research question; what role does performance management play in the transition to a competitive hybridised social housing sector? Exploring this question, the paper analyses the effect of this transition through institutional isomorphism and considers specifically how a sample of English HAs sense uncertainty within the social housing sector and respond to the coercive, normative, and mimetic isomorphic pressures at play.This study finds the primary mechanism by which HAs appear to sense and respond to external changes within the sector is through their strategic performance measurement systems and metrics. Social, political, and competitive changes in the sector are ‘sensed’ as a misalignment within the existing strategic performance metrics, exerting isomorphic pressures on the organisational governance team to respond by realigning the performance metrics with the sensed changes. In this way, we posit that strategic performance measurement is linked to and plays a much more pivotal role, in the hybridisation of third-sector organisations than previously reported within the literature.Examples of such realignment include the increased use of benchmarking performance as a normative practice, and the replication of perceived best practice from the private sector to remain competitive and sustainable. Such changes reflect on an increasingly hybridised position for HAs, which now focus on improving business-critical activities, rather than simply producing performance data as a means of demonstrating good governance and compliance to their regulatory body.

Measuring Mental Effort for Creating Mobile Data Collection Applications.

To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials.

Performance measurement metrics in TPM: A contextual view to training and development

In today’s global competitive manufacturing environment for survival of any firm it is necessary to provide quality goods to its customers at competitive prices. It is possible only if the firm is running at its highest level of productivity. That is why the firms are focused on productivity improvement tasks. The Total Productive Maintenance (TPM) is a key tool to improve productivity exercising its various pillars. Whole hearted implementation of training and development pillar of Total Productive Maintenance is of utmost importance in improving skills of the operators and other employee of the company. Measuring performance is vital for its improvement. The objective of this paper is to identify the performance measurement metrics to measure and verify effect of the pillar. A conceptual framework has also been presented that outlines the selection and effective application of performance measurement metrics aligning manufacturing performance and maintenance objective through effective training and skill development.

Embedded Architecture Composed of Cognitive Agents and ROS for Programming Intelligent Robots

Abstract This paper proposes and evaluates an embedded architecture aimed to promote the utilization of cognitive agents in cooperation with the Robotic Operating System (ROS), serving as an alternative for programming intelligent robots. It promotes the programming abstraction level in two directions. The first direction regards using cognitive agents facilities for programming the robots intelligence, consisting of its perceptions and related actions. The second direction exploits the facilities of using ROS layers for programming the robot interaction with its sensors and actuators. The paper reports experiments of using agents to command simulated UAVs while measuring performance metrics that allowed us to evaluate the benefits of the proposed architecture.

Parametric Modelling and Multi-Objective Optimization of Electro Discharge Machining Process Parameters for Sustainable Production

Electro Discharge Machining (EDM) can be an element of a sustainable manufacturing system. In the present study, the sustainability implications of EDM of special-purpose steels are investigated. The machining quality (minimum surface roughness), productivity (material removal rate) improvement and cost (electrode wear rate) minimization are considered. The influence and correlation of the three most important machining parameters including pulse on time, current and pulse off time have been investigated on sustainable production. Empirical models have been established based on response surface methodology for material removal rate, electrode wear rate and surface roughness. The investigation, validation and deeper insights of developed models have been performed using ANOVA, validation experiments and microstructure analysis respectively. Pulse on time and current both appeared as the prominent process parameters having a significant influence on all three measured performance metrics. Multi-objective optimization has been performed in order to achieve sustainability by establishing a compromise between minimum quality, minimum cost and maximum productivity. Sustainability contour plots have been developed to select suitable desirability. The sustainability results indicated that a high level of 75.5% sustainable desirability can be achieved for AISI L3 tool steel. The developed models can be practiced on the shop floor practically to attain a certain desirability appropriate for particular machine limits.