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The purpose of this paper is to design and develop a balanced scorecard (BSC)-based index for quantifying the suitability of suppliers to mature to the next level of collaboration with the buyer company.

A BSC-based decision framework was developed using factors and factor indicators affecting collaboration with the supplier, using exploratory research. The proposed decision framework was implemented as a real-world case study in an Indian manufacturing organisation by using the Analytic Hierarchy Process. An 11-step methodology was developed to quantify supplier suitability to mature to the next level of collaboration. This is referred to as the “supplier collaborative performance index” (SCPI).

The proposed decision-making framework helps quantify the extent of collaboration with each supplier. It serves as an index by using the perspectives that are significant for the strategic performance of the buyer company, the factors that affect the collaboration and their specific factor indicators. The initial results of the implementation of the case study were found to be useful in judging supplier suitability in order to mature in their relationship.

A comprehensive BSC-based framework for enhancing relationships with suppliers, SCPI will be instrumental in deciding, managing and improving the level of collaboration with suppliers in manufacturing companies, depending on priorities.

This approach provides a single index to establish the supplier’s suitability to mature to the next level of collaboration with a buyer company. The higher the value of the Collaboration Index for a supplier, the better is the chance to move to the next level of maturity.

The purpose of this paper is to consider a nonlinear problem of minimizing the cost of providing reliable systems. The authors assume that the system consists of several components in series, and for each such component, the cost of the component increases exponentially with its reliability.

In order to solve this nonlinear optimization problem, the authors propose two approaches. The first approach is based on the concept of adjusting the reliability of a pair of components to minimize the cost of the system. The authors call this procedure as reliability adjustment routine (RAR). Proofs of optimality and convergence for the proposed model are also provided. The second approach solves the problem by using a Lagrangian multiplier. A procedure is developed to obtain the maximum step size to achieve the desired optimal solution in minimum iterations. Proposed approaches are efficient and give exact solutions.

Proposed methods enable a decision maker to allocate reliability to the components in series while minimizing the total cost of the system. The developed procedures are illustrated using a numerical example. Although an exponential relationship between the component cost and reliability is assumed, this can be extended to various other nonlinear distributions.

This cost optimization problem, subject to system component reliability values, assumes the near practical nonlinear pattern of cost vs reliability. Such problems are complex to solve. The authors provide a unique approach called RAR to solve such convoluted problems. The authors also provide an approach to solve such problems by using a Lagrangian multiplier method. Various proofs have been worked out to substantiate the work.

The purpose of this paper is to improve the understanding of the role of the bottlenecks in the dynamic software development supply chains. The paper examines the effects of the task priorities in the software development and investigates the possible strategies to manage them effectively.

In this paper, a software development supply chain has been simulated. This includes modeling of the various sizes of software requirement, different priorities, variations in development times, quality defects, etc. The model assumes a fixed set of resources of various skills. The model is studied for the bottlenecks, throughput, work in progress (WIP), etc. under various work preemption scenarios.

The results indicate that job priorities impact the bottleneck formulation, throughput and WIP of the software development. The work interruption policies to accommodate priority jobs adversely impact the throughput. Selective introduction of interruptions by leaving the bottlenecks from interruptions helps balancing the throughput and priorities.

The impact of the learning curve and knowledge acquisition time needed by the resources to restart the interrupted work has not been considered in this paper, which can be a future area of research.

The paper helps the practicing managers evaluate the dynamics of the bottlenecks with various task management approaches and comprehend the possible tradeoffs between priority and throughout.

The paper looks at software development from a perspective of workflow dynamics. This is a pioneer effort, as it utilizes simulation and modeling approach in understanding the software supply chains better.

The purpose of this paper is to propose an approach to evaluate “on time” performance for a class of Indian railways (IR). This approach is build-up on the theme of six sigma level computation for continues data. The arrival data obtained from a class of IR called “Rajdhani Express” exhibited a unique characteristic: neither did the data followed a distribution nor could it be transformed to fit into a distribution. In this work, the authors present an approach to evaluate on time performance of IR, given such “unruly” data.

An attempt is made to develop a lucid approach, given an unruly data. Initially, the authors plot a histogram using Scott’s method. Later, the authors use Taguchi’s quality loss function to assign weights to each of the bins in histogram. Weights to each of the bins are assigned based on the predefined rules. Finally, sigma level is computed by using weighted defect per million opportunities (DPMO) approach. In this paper, the authors discuss the proposed algorithm, an illustrative example with an emphasis on a class of IR.

Given the unique characteristic (unruly data) of arrival data of IR, the proposed methodology helps in quantifying it is on time performance. This method extends the conventional DPMO approach of computing the sigma level. The proposed method is validated using various data sets of different distributions. Further, this approach can be generalized and applied to data set of any distribution. Since distribution of the data is not the pre-requisite, the proposed approach can be applied to compare (and benchmark) data sets of different distributions. This methodology, thus, paves path to develop newer approaches in quantifying and benchmarking the sigma levels.

In this manuscript, the authors present a case of Rajdhani Express trains, a class of IR. A practicing manager can use this approach to compare the performance of various classes of railways and benchmark their performance. Such an approach, with suitable modifications (if any) can be applied to evaluate performance in various service industries.

Usually, if the data are unruly, the sigma level is computed by using ad-hoc methods that may provide compromising solutions and/or inaccurate results. The developed methodology proposes a unique approach to quantify sigma level, given such an unruly nature of the data. This approach thus fills the long needed gap in addressing such situations. This approach can be applied in various similar situations. A case of IR is presented.

The purpose of this paper is to propose a performance evaluation framework using an integrated approach of stochastic frontier analysis (SFA) and technique of order preference with similarity to ideal solution (TOPSIS) called efficiency ranking method using SFA and TOPSIS (ERM-ST) specifically in the banking sector where service excellence is of prime importance for business growth.

The proposed approach ERM-ST measures the performance of a DMU in the SFA framework by considering multiple outputs and multiple inputs. It is a non-parametric tool which does not need any prior model assumptions which enhances its applicability in real-life business scenarios. Moreover, the efficiency score obtained using the proposed model ERM-ST lies between 0 and 1, unlike in case of super efficiency data envelopment analysis (DEA) which may go well above 1.

The proposed framework is evaluated for its applicability using two various data sets and is further used to evaluate the performance of a group of 26 public sector banks in India. The results obtained by the proposed method ERM-ST are compared with those obtained by super efficiency DEA using Friedman’s test.

The proposed approach ERM-ST is developed to evaluate the performance of a service unit with multiple outputs and multiple inputs in the SFA framework.

Understanding the significance of supply chain, demands need of multiple criteria for its performance evaluation. The aim of this paper is to propose an approach to evaluate the performance of supply chain using multiple criteria.

A multi‐criteria decision making tool, like analytic hierarchy process is used to develop an eight step methodology for performance evaluation. The proposed methodology is also elucidated with an illustration and a case from Indian chemical company.

Supply chain performance number is computed, suggesting the present performance status of the supply chain. The methodology also helps rank the various links according to its performance. The analysis leads in computation of supply chain performance number (SCPN): the value lies between 0 and 1.

The methodology can be suitably modified to accommodate any number of criteria and supply links, given the structure of the supply chain.

A case from an Indian chemical industry is presented in this paper. The findings of which, are encouraging and have benefited the practicing mangers in various ways.

This paper presents a unique approach for supply chain performance evaluation considering multiple criteria, with a flexibility to modify and analyze using the available data sets.

The purpose of this paper is to present a tie-breaking procedure for computing performance efficiencies to improve benchmarking and performance evaluation process in a business situation.

The authors propose a unified approach based on data envelopment analysis (DEA) and technique for order of preference by similarity to ideal solution (TOPSIS), to overcome the difficulty of unique ranking in the prevalent benchmarking and performance evaluation processes such as DEA, Super efficiency DEA model, etc., under constant return to scale (CRS) assumption. This model is called as efficiency ranking method using DEA and TOPSIS (ERM-DT). In order to check the consistency of the approach, various input-output combinations (to calculate the efficiencies) have been illustrated. Further, the authors present a case of an Indian Bank to illustrate an application of the proposed approach.

The proposed approach, ERM-DT enables assign a unique rank to decision making units and provides a tie breaking procedure. Results obtained using the proposed approach are statistically compared with those obtained from the CRS DEA approach and super efficiency DEA approach using Friedman’s test.

The proposed model provides an efficiency ranking method based on a score obtained by considering the minimum distance from the best value and maximum distance from the worst value. The proposed methodology is capable of handling negative data and undesirable output variables. This approach is unit invariant and makes the calculations simple. The authors present an application to compute the efficiency of various branches of an Indian bank. The authors hope the proposed method can enhance the decision-making ability of the management in complex situations.

The authors propose an integrated DEA and TOPSIS framework for better benchmarking and performance evaluation. This approach provides a tie-breaking procedure for the efficiencies computed using CRS DEA approach. Ranks are assigned based on score obtained by considering the distance from the worst and the best solution. The proposed approach can be used with non-positive data points and undesirable output variables.

This study builds up the case for dependency considerations in system analyses by explaining “dependency” as a concept, and proposes a methodology to predict the dependency for the components and the systems. Many a times, dependency concept is neglected in system studies in order to simplify the analysis that leads to the “satisficing” results. For systems where such negligence may cause significant deviations, the dependency has to be accounted for. The proposed methodology applies the concepts of analytic hierarchy process (AHP), directed graph, and matrices at different stages of analyses and results into an intra‐dependency index for the system. This index could help the analyst select the less dependent system from amongst the choices available without compromising on the minimum expectations of the system. The index would also help understand the level of the dependencies in the system. In this paper, the methodology is developed and described using examples, to explain the application of functional dependency and design dependency. This suggests the wide coverage and application of the proposed methodology.

This paper aims to design a hybrid model of knowledge-based performance management system (KBPMS) for facilitating Lean Six-Sigma (L6s) application to increase contractor productivity without compromising human safety in Indonesian upstream oil field operations that manage ageing and life extension (ALE) facilities.

The research design applies a pragmatic paradigm by employing action research strategy with qualitative-quantitative methodology involving 385 of 1,533 workers. The KBPMS-L6s conceptual framework is developed and enriched with the Analytical Hierarchy Process (AHP) to prioritize fit-for-purpose Key Performance Indicators. The application of L6s with Human Performance Modes analysis is used to provide a statistical baseline approach for pre-assessment of the contractor’s organizational capabilities. A comprehensive literature review is given for the main pillars of the contextual framework.

The KBPMS-L6s concept has given an improved hierarchy for strategic and operational levels to achieve a performance benchmark to manage ALE facilities in Indonesian upstream oil field operations. To increase quality management practices in managing ALE facilities, the L6s application requires an assessment of the organizational capability of contractors and an analysis of Human Performance Modes (HPM) to identify levels of construction workers’ productivity based on human competency and safety awareness that have never been done in this field.

The action research will only focus on the contractors’ productivity and safety performances that are managed by infrastructure maintenance programs for managing integrity of ALE facilities in Indonesian upstream of oil field operations. Future research could go toward validating this approach in other sectors.

This paper discusses the implications of developing the hybrid KBPMS- L6s enriched with AHP methodology and the application of HPM analysis to achieve a 14% reduction in inefficient working time, a 28% reduction in supervision costs, a 15% reduction in schedule completion delays, and a 78% reduction in safety incident rates of Total Recordable Incident Rate (TRIR), Days Away Restricted or Job Transfer (DART) and Motor Vehicle Crash (MVC), as evidence of achieving fit-for-purpose KPIs with safer, better, faster, and at lower costs.

This paper does not discuss social implications

This paper successfully demonstrates a novel use of Knowledge-Based system with the integration AHP and HPM analysis to develop a hybrid KBPMS-L6s concept that successfully increases contractor productivity without compromising human safety performance while implementing ALE facility infrastructure maintenance program in upstream oil field operations.

Performance appraisal is one of the most critical and indispensable human resource practices for organisations. However, it generates dissatisfaction among employees as it is often viewed as complex and ineffective. The purpose of this paper is to present a new performance management system that integrates multi-criteria decision analysis (MCDA) methods – the analytic network process (ANP) and PROMETHEE – with the visual techniques of the GAIA plane and the stacked bar chart. MCDA methods allow a structured and consistent evaluation integrating qualitative and quantitative criteria.

The authors developed a structured and transparent performance management system. It is based on the MCDA methods PROMETHEE and ANP. It also incorporates the visual techniques: GAIA and stacked bar chart. Feedback for trainings and developments can precisely be formulated.

Visual techniques permit clear identification and quantification, for each employee, of the areas that need improvement through training and development, which contributes to the resource-based view of organisations. A real case study has been portrayed to show the added value of the MCDA methods and the visual techniques in employee performance management.

The paper describes a new employee performance system adopted in an organisation. The multi-criteria analysis transparently combines qualitative and quantitative decision criteria into a holistic and transparent evaluation. The visual techniques permit us to gain a deep insight into the employees’ skills profile and capture fine details where individuals perform or underperform.