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Transport industry is one of the leading accident causing industries all around the world. Personal attributes like educational qualification, work experience, marital status, consumption of alcohol, hours of work of bus drivers are known to influence such accidents. In the present study, the mediating effects of Workplace spirituality (WS) among bus drivers between the personal attributes and job performance variables like self-reported accidents, punishments and appraisals are carried out.

A mediation analysis of WS between personal attributes and job performances of bus drivers is conducted in the present study. Since there is scarcity of studies measuring the effects of WS of bus drivers, new scale to measure WS was developed. The study was carried out in the government-owned road transport corporation in the state of Kerala, India. Responses were obtained from 617 male drivers.

Using exploratory factor analysis, four factors were identified, namely Meaningful work, Sense of Community, Mindfulness and Compassion. Confirmatory factory analysis provided good fit. The intercorrelations of personal attributes of drivers (independent variables) between WS factors (mediating variables) and job performance variables (dependent variables) were found out. Mediation analysis showed complete mediation of WS factors between marital status, alcohol consumption, hours of work and job performances like number of self-reported accidents, punishments and appraisals. The WS levels of drivers decrease significantly as working hours per goes beyond the legally allowed working hours.

Psychological attributes like Mindfulness, Sense of community, compassion etc. which are collectively known as WS influence the job performances of employees in other industries. Yet it is not studied in the transport industry. Hence, in the present study, the levels of WS are studied among bus drivers of government-owned road transport corporation in the State of Kerala, India.

Organizational Behavior and Strategic Management.

MBA, Management/Executive development programs.

This case study can be used effectively for understanding the nuances of employee loyalty, especially if there is a cost of employee loyalty. While Anand Finance is happy that its workforce has largely been loyal, the volatile, uncertain, complex and ambiguous times force it to chart new course of action. The newly appointed Business Head, Ashok Singh's challenges compound when he finds that there was not’t a single innovation or best practice adopted over the past three years. Given his mandate to make Anand Finance as the Walmart of financial services, can he aspire to rally the forces behind the new mission? This case study facilitates an interesting discussion on the significance of operational and strategic alignment at organizations in the backdrop of an interesting story of Anand Finance, one of the leading non-banking financial companies (NBFCs) in India. The non-alignment was noticed by Ashok Singh (Singh) who took over as the Business Head of Anand Finance. While the company boasted of long-standing employees, Singh was quick to notice that the company had been paying a cost for employee loyalty. What was the cost of employee loyalty? Singh could also sense that the company was in a state of active inertia. Expected to make Anand Finance Walmart for financial services by 2025, Singh had a big task at hand given the lack of strategic orientation of the employees. What would be the likely course of Singh's actions? As the case study deals with strategic dilemmas related to the organizational culture, it can be suitably used for organizational behavior and strategic management courses. This case study is meant highlight that even if an organization is operationally sound and successful, it cannot afford to be strategically disoriented, as its strengths may prove to be its weaknesses with changing business conditions.

At the end of this case discussion, the participants are expected to know the merits and demerits of employee loyalty and the implications of the same for organizational change; whether employees’ relatively longer stints at companies would contribute to active inertia (as defined by Donald N. Sull in Harvard Business Review article, “Why Good Companies Go Bad”); and the ways to align operational orientation with strategic mindset, especially in the case of employees who rose through the ranks and had been serving the company for relatively longer period.

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From the green supply chain management (GSCM) approach, the purpose of this paper is to propose a green index as a benchmarking tool to assess environmental performance in manufacturing companies. The index involves 10 green practices and 46 performance dimensions, which were identified by way of an exhaustive literature review.

The GSCM index was designed as follows: survey design; experts group selection; relationship identification and green practice weighting (DEMATEL method); and GSCM index construction. The proposed GSCM index was validated at a set of ten Colombian manufacturing companies, which belong to different industrial subsectors.

The structural relationships between both green practices and performance dimensions (cause group vs effect group) were determined. Consequently, a set of green practices and performance dimensions on which managers must focus their environmental efforts (cause group), in order to positively affect the system as a whole, was identified. The environmental performance of a set of manufacturing companies was established.

As a decision support tool, the GSCM index provides a baseline for the deployment of environmental improvement plans. It also permits the comparison of environmental performance between companies from different industrial sectors. Further, it can be used to monitor internal operations and collaboration programs in each echelon of the supply chain.

This publication contributes to the field of environmental performance measurement by introducing a new index, from the GSCM perspective. In contrast with the existing green indicators, the proposed index is the first to consider causal relationships between green practices and between their performance dimensions.

Development of non‐destructive methodology for detection of arc strike, spatter and fusion type of welding defects which may form on steam generator (SG) tubes that are in close proximity to the circumferential shell welds. Such defects, especially fusion‐type defects, are detrimental to the structural integrity of the SG. This paper aims to focus on this problem.

This paper presents a new methodology for non‐destructive detection of arc strike, spatter and fusion type of welding defects. This methodology uses remote field eddy current (RFEC) ultrasonic non‐destructive techniques and K‐means clustering.

Distinctly different RFEC signals have been observed for the three types of defects and this information has been effectively utilized for automated identification of weld fusion which produces two back‐wall echoes in ultrasonic A‐scan signals. The methodology can readily distinguish fusion‐type defect from arc strike and spatter type of defects.

The methodology is unique as there is no standard guideline for non‐destructive evaluation of peripheral tubes after shell welding to detect arc strike, spatter and fusion type of welding defects.

The purpose of this paper is to determine the feasibility of identifying the creep rupture of reactor cladding tubes using acoustic emission technique (AET).

The creep rupture tests were carried out by pressuring stainless steel capsules upto 6 MPa at room temperature and then heating continuously in a furnace upto rupture. The acoustic emission (AE) signals generated during the creep rupture tests were recorded using a 150 kHz resonant sensor and analysed using AE Win software.

When rupture occurs in the pressurized capsule tube representing the cladding tube, AE sensor attached to a waveguide captures the mechanical disturbance from the capsule and these data can be advantageously used to identify the creep rupture event of the cladding tube.

The creep rupture data of fuel clad tube is very important in design and for smooth operation of nuclear reactors without fuel pin failure in reactors.

AE is an advanced non-destructive evaluation technique. This technique has been successfully applied for on-line monitoring of creep rupture of the reactor cladding tube which otherwise could be detected by thermocouple readings only.

It has been widely recognized that agility enables manufacturing systems to respond to dynamic and unpredictable changes in today’s competitive environment. Develops a quantitative analysis framework and a simulation methodology to explore the value of agility in financial terms. Addresses the issues pertaining to the assessment of how an agile system performs in an environment of unanticipated changes, the comparison between two or more systems with different designs and hence different agility levels and the justification of investments in agility. Proposes an exploratory framework for a structured analysis of the various segments of the manufacturing system in which agility at different levels is built‐in through different pathways and links it to a set of aggregate performance measures. Then develops a simulation model that captures dynamic and unanticipated changes in the operating environment and facilitates performance appraisal and investment justification decisions using a quantitative financial metric.

The purpose of this paper is to improve the dye affinity of natural dye of Terminalia chebula retzius (T. chebula) using the dye substrate of leather.

The dyeing conditions such as temperature, concentration of dye, and time are controlled by measuring the dye affinity. The effect of enzymatic post-tanning process on dye affinity is evaluated by using different type of proteases such as flavourzyme, alcalase, and bromelain. The optimum conditions for enzymatic post-tanning process are evaluated depending on different pH, temperature, and concentration of enzyme.

The highest dye affinity was obtained at 50°C using a dye concentration of 200 percent (owf) for 30 min treatment by measuring of K/S values of dyed leather. Distilled water was proved as a better extraction liquid to improve the dye affinity of T. chebula. The K/S values of dyed leather were enhanced after the enzymatic post-tanning process by flavourzyme. Moreover, the fastness properties against the rubbing and dry cleaning of the dyed leather were improved by the enzymatic post-tanning process.

In this paper, the enzymatic post-tanning process is introduced as the method to improve the dye affinity in natural dyeing using Terminalia chebula retzius (T. chebula) applied to leather. The results of the study could be applied for further natural dyeing of leather using various natural dyes.

Flexibility has become one of the most useful and necessary weapons in many of today's competitive markets. For companies in situations considering investments in flexibility, it is necessary to assess carefully exactly what flexibility could benefit the company's operations, and how this flexibility can be achieved. Different manufacturing situations are associated with different levels of uncertainty and variations, and therefore call for different sorts of flexibility. Although flexibility has been argued to be available (to a certain point) without major investments in technology, it can be assumed that flexibility is most likely to come at a price. A company should therefore spend considerable effort on identifying what flexibility would be of benefit to the organisation to enhance their performance, and thereafter assess how to achieve it. This paper provides a comprehensive analysis of variability and uncertainty, and therefore, the need for flexibility within an organisation by examining market and manufacturing process related factors. Each factor is further examined to find out relevant flexibilities and corresponding methods, tools, and techniques to be used by suggesting proposed manufacturing approaches to organisations. The human factor is suggested as an essential flexibility component as well as a key contributor for selecting, developing, improving and implementing flexibilities in order to succeed in markets that are accelerating and becoming more turbulent.

In this article a proposal of a non‐traditional methodology in achieving optimal flexibility with minimal inventory risk will be applied. The non‐traditional methodology will be able to achieve different levels of the production schedule changes with maximum flexibility and minimal inventory risk. The basic principle, implementation methodology as well as its effectiveness and benefits will be discussed. In order to overcome the risk of achieving flexible manufacturing or enhancing supply chain management, this article will address issues on methods to accommodate production sequence changes which include total production quantity change in a short lead time. The study uses the Family Ordering System methodology which has proven capable in solving manufacturing flexibility issues by reducing the total manufacturing lead time. Family Ordering System provides the flexibility of model changes and reduces production line stoppage as a result of part shortage by carrying extra inventory of unique parts.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.