Browsing by Author "Verma, Abhishek"

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Analysis of categorical incident data and design for safety interventions using axiomatic design framework
(Springer, 2020-03) Verma, Abhishek
Although analysing categorical data from incident investigation reports provides meaningful associations amongst causal factors of incidents, however, to date, no studies considered these associations in designing actionable interventions for safety improvement. We propose a methodology using descriptive analytics and axiomatic design framework. In this study, we have analysed injury, and ‘property-damage’ data, collected for 45 months from a large integrated steel plant. The data are analysed using the contingency table, Cramer’s V, Phi coefficients (ϕ) and Fisher’s exact test. The ‘wire-making division’ is the most injury-prone. Unsafe acts done by fellow workers are significantly causing injuries in ‘support services’, maintenance and ‘steel-making’. The property-damage cases are mostly reported in ‘steel-making division’, and caused by material-handling, crane-dashing, toxic-chemical, hot-metal and process-related incidents. It is also found that SOP inadequacy and non-compliance are significantly associated with ‘property-damage’ incidents. The key interventions from axiomatic design are as follows. For process-related incidents, regular inspection and maintenance of safety-critical equipment should be done. Safety-critical instrument and alarms can also be used to monitor safe operating limits of processes. Unsafe acts by fellow workers are the result of lack of coordination and communication. So, the management should identify and provide the types of safety training necessary to improve the same. The material-handling related problems can be handled through improved staff competency and communication. To address the SOP related issues, operating procedures should be reviewed, revised and communicated regularly.
Business intelligence and data analytics
(Springer, 2025) Verma, Abhishek
This book is a collection of the high-quality research articles presented at the International Conference on Business Intelligence and Data Analytics (BIDA 2024), organized by RV Institute of Management (RVIM), Bengaluru, India, during April 2024. The book covers state-of-the-art research articles from the researchers and practitioners working in the field of business intelligence, data analytics, decision support systems, data warehousing and data mining, big data analytics, predictive and prescriptive analytics, and machine learning for business applications and their real-world applications.
Business intelligence and data analytics: Proceedings of BIDA 2024
(Springer, 2025) Verma, Abhishek
This book is a collection of the high-quality research articles presented at the International Conference on Business Intelligence and Data Analytics (BIDA 2024), organized by RV Institute of Management (RVIM), Bengaluru, India, during April 2024. The book covers state-of-the-art research articles from the researchers and practitioners working in the field of business intelligence, data analytics, decision support systems, data warehousing and data mining, big data analytics, predictive and prescriptive analytics, and machine learning for business applications and their real-world applications.
A data analytic-based logistics modelling framework for E-commerce enterprise
(Taylor & Francis, 2022-01) Verma, Abhishek
Data-driven approaches have noteworthy significance in managing and improving logistics in E-commerce enterprises. This study focuses on the development of an integrated framework to analyse the Brazilian E-Commerce enterprise public dataset. From the analysis, it is found that sellers of Ibitinga city of SP state had the most count of late deliveries where 42 sellers are under-performing in terms of estimated delivery time. Locations of customers and sellers were spotted on a map to get a geographical representation. The proposed framework may help E-Commerce enterprise owners and retail merchants to make better decisions related to sales and E-Commerce enterprise logistics.
Data-driven mapping between proactive and reactive measures of occupational safety performance
(Springer, 2017-10) Verma, Abhishek
This study aims to analyse the incident investigation reports logged after the occurrence of events from an integrated steel plant and map it with proactive safety data. From the narrative text describing the event, this study has attempted to unfold the hazards and safety factors present at the workplace. Text document clustering with expectation maximization algorithm (EM) has been used to group the different events and find key phrases from them. These key phrases are considered as the root causes of the reported events. This study shows how the mapping of the safety factors from both proactive safety data and incident reports can help in the improvement of safety performance as well as better allocation of resources. The study points out specific areas to the management where improvements are needed. The mapping also indicates the areas of improvement made by the constant effort of safety practitioners.
Decision support system for safety improvement: an approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering
(Elsevier, 2019-02) Verma, Abhishek
An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.
Forecasting occupational safety performance and mining text-based association rules for incident occurrences
(Elsevier, 2023-03) Verma, Abhishek
Occupational incidents are a major concern in steel industries due to the complex nature of job activities. Forecasting incidents caused by various activities and determining the root cause might aid in implementing appropriate interventions. Thus, the purpose of this study is to investigate the future trend and identify the pattern of contributing factors of incident occurrences. The study focuses on an integrated steel plant where different steel-making-related operations are carried out in separate units. The incident data of 45 months is used. Initially, a unit-wise trend of incidents (e.g., injury, near-miss and property damage) is forecasted using the autoregressive integrated moving average (ARIMA) model to determine the near-future incident trends and to identify the most incident-prone unit of the plant. The model is validated using six-month holdout data, and the predicted number of incidents is compared with the actual counts. The ARIMA model indicates that the safety performance of the iron making unit is found to be underperforming. In the second phase, meaningful association rules are extracted from text data using the apriori algorithm for the underperforming unit to discover the incident-causing factors. Results from text mining-based association mining suggest that bike and car-related incidents are the leading causes of injury. Similarly, gas leakage, slag spillage, and coke-oven door malfunctioning are causing near-miss incidents. The majority of property damage incidents are reported due to derailment, loading/ unloading and dashing of the dumper vehicle. Effective implementation of the study’s specified rules can aid plant administration in formulating policies to improve safety performance by designing focused interventions.
From blocks to books: learnifytech's blockchain journey
(The Case Centre, 2024) Verma, Abhishek
LearnifyTech (a hypothetical name due to name disclosure issue) is a pivotal player in India's education sector, making significant progress and encountering hurdles that reflect the changing world of educational technology. This case study offers a comprehensive examination of LearnifyTech's progression, the present condition of the education sector in India, the core principles of blockchain technology, and the predicament encountered by Prakhar Patel, the main character in our case study. Case study concisely overview the education sector's current condition and ongoing difficulties. The education system in India is extensive and varied, with many young people, widespread use of digital technology, and changing regulations. This creates a challenging environment for LearnifyTech to operate in. LearnifyTech was established to transform the methods of delivering and accessing education. It has quickly become known for its dedication to excellence, incorporation of technology, and focus on the needs of learners. LearnifyTech is considering using blockchain technology in its educational system, whereas Prakhar faces complex hurdles and essential considerations. In addition, he is contemplating the ramifications for LearnifyTech's future. This decision-making scenario is set against the interplay between technology, organizational strategy, and educational impact.
Healthcare unchained: the blockchain battle at ArogyaManav
(Sage, 2025-01) Verma, Abhishek
Dr. Ananya Sharma, medical director of ArogyaManav Hospitals, faces challenges with patient data security and medication tracking within the existing evolving healthcare landscape. The fragmented nature of medical records, data privacy concerns, and ethical dilemmas highlight some of the problems Dr. Ananya Sharma and her chief technology officers face. The case invites students to put themselves in the situation of the medical director of ArogyaManav Hospital, who is facing a dilemma in choosing between the established trustworthiness of blockchain and the potential transformative advantages of the internet-of-things application (IOTA). The decision carries implications for patient data management and the hospital’s ability to provide quality treatment.
Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports
(Elsevier, 2014-12) Verma, Abhishek
The aim of this paper is to find out the patterns of incidents in a steel plant in India. Occupational incidents occur in steel plant mainly in form of injury, near miss, and property damage or in combination. Different factors are responsible for such incidents to occur. An incident investigation scheme is proposed. Association rule mining approach is used to discover cause-and-affect patterns (rules) using 843 incidents. Thirty-five meaningful association rules are extracted using three criteria, support (S), confidence (C) and lift (L). For example, the results show that unsafe acts done by others are more frequent in injury cases (S = 4.86%, C = 78.8%, L = 2.3). Similarly, one of the SOP (standard operating procedures) related rule: ‘SOP required, available, adequate but not complied’ led to property damage (S = 11.03%, C = 49.2%, L = 1.525). Another useful rule ‘SOP required, available but inadequate, followed’ led to near miss (S = 1.66%, C = 38.89%, L = 1.163). It is also found that for slip, trip and fall incidents, workers working alone (S = 3.91%, C = 76.74%, L = 2.239) or in a group (S = 3.20%, C = 75.00%, L = 2.188) does not make much difference. The findings pinpoint the areas of improvement such as inadequate SOPs, non-compliance of SOPs, training, and slip, trip and fall prevention to minimize incidents.
An integrated TRIZ coupled safety function deployment and capital budgeting methodology for occupational safety improvement: a case of manufacturing industry
(Elsevier, 2022-09) Verma, Abhishek
In this study, a novel scheme is proposed for occupational safety improvement by leveraging the concepts of Virtual Reality (VR), Safety Function Deployment (SFD), TRIZ (theory of inventive problem solving) and capital budgeting approach. This integrated approach helped in identifying safety interventions, which added a new dimension to the safety intervention design in the operational study at the workplace. By observing the effectiveness of the immersive safety training in identifying the accident path elements such as hazards and initiating mechanisms, a three-dimensional (3D) VR environment is created for safety training of Electric Overhead Travelling (EOT) crane operators of the studied manufacturing industry. This study is carried out in two phases based on accident path elements identified before and after safety training in the VR platform. Three House of Safety (HoSs) are used in this study to establish a relationship between tasks and hazards, hazards and initiating mechanism, and initiating mechanism and safety interventions. Priority weight of interventions in the last HoS is fed as input to the capital budgeting methodology for the selection of an optimal number of interventions for safety improvement. 0–1 multi-dimensional knapsack model is used in capital budgeting considering safety budget and cost of each intervention. We have introduced Z- number approach in capital budgeting methodology to characterize the reliability of experts’ opinion considered in this process. A 15% improvement in safety performance is observed after safety training. Further, it is observed that technology-based interventions (laser scanner, smart helmet, smart jacket, Radio Frequency Identification (RFID) to monitor Personal Protective Equipment (PPE), immersive safety training) are having more weightage than traditional safety interventions after safety training.
Mining safety rules for derailments in a steel plant using correspondence analysis
(Elsevier, 2014-10) Verma, Abhishek
In this study, we have analyzed a steel plant’s derailment data using correspondence analysis. The primary purpose of this analysis is to find out associations of categories of factors contributing to the derailments which ultimately lead to the development of meaningful rules for preventing derailments. 348 derailment incidents collected over a period of 42 months were analyzed considering 4 factors namely, shift of working, location, cause of derailment and department responsible. Descriptive statistics show that by shift of working there is not much difference in the occurrence of derailments. But from location, cause of derailment and responsibility (departments) points of view, ‘raw material line’, ‘manual operations’ and ‘production (raw material)’ accounted for 50%, 60% and 48.28% of derailments, respectively. From correspondence analysis, it is found that ‘level of movements’, ‘level of human involvement’, ‘management of wagons’, and ‘criticality of movements’ are the hidden root causes of derailments in the plant studied. In order to improve the safety of in-plant rail transport of the plant studied, the plant management should (i) collect and analyze derailment data related to ‘level of movements’ and ‘human involvement’, (ii) adopt collaborative maintenance of wagons as external agencies are also involved in rail transport, and (iii) practice risk based maintenance of the in-plant rail transportation systems.
Prediction of occupational incidents using proactive and reactive data: a data mining approach
(Springer, 2017-10) Verma, Abhishek
Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. As an extension of the existing works, the present study has used the underused proactive data coupled with reactive data to establish the predictive models so that the information inherent in both data sets could be better utilized. The main aim of the study is to predict the incident outcomes using mixed data set comprising reactive and proactive data together. Two decision tree classifiers, i.e. classification and regression tree (CART) and C5.0, have been implemented with tenfold cross validation. Furthermore, the ensemble technique, namely adaptive boosting has been implemented to increase the classification accuracy. Results show that boosted C5.0 produces higher accuracy than others for the prediction task. Furthermore, the rules obtained produce the insight of the incidents. The limitation of the present study includes the use of less amount of data and the requirement of experts’ domain knowledge for a large span of time. Future scope of the study includes the proper feature selection for preparation of the mixed data set and building the better classification algorithm for better prediction of occurrence of accidents. The present work sets out the potential use of both types of data sources together.
A preliminary analysis of incident investigation reports of an integrated steel plant: some reflection
(Taylor & Francis, 2017-12) Verma, Abhishek
Large integrated steel plants employ an effective safety management system and gather a significant amount of safety-related data. This research intends to explore and visualize the rich database to find out the key factors responsible for the occurrences of incidents. The study was carried out on the data in the form of investigation reports collected from a steel plant in India. The data were processed and analysed using some of the quality management tools like Pareto chart, control chart, Ishikawa diagram, etc. Analyses showed that causes of incidents differ depending on the activities performed in a department. For example, fire/explosion and process-related incidents are more common in the departments associated with coke-making and blast furnace. Similar kind of factors were obtained, and recommendations were provided for their mitigation. Finally, the limitations of the study were discussed, and the scope of the research works was identified.
Prioritization of near-miss incidents using text mining and Bayesian network
(Springer, 2017-07) Verma, Abhishek
Near-Miss incidents can be treated as events to signal the weakness of safety management system (SMS) at the workplace. Analyzing near-misses will provide relevant root causes behind such incidents so that effective safety related interventions can be developed beforehand. Despite having a huge potential towards workplace safety improvements, analysis of near-misses is scant in the literature owing to the fact that near-misses are often reported as text narratives. The aim of this study is therefore to explore text-mining for extraction of root causes of near-misses from the narrative text descriptions of such incidents and to measure their relationships probabilistically. Root causes were extracted by word cloud technique and causal model was constructed using a Bayesian network (BN). Finally, using BN’s inference mechanism, scenarios were evaluated and root causes were listed in a prioritized order. A case study in a steel plant validated the approach and raised concerns for variety of circumstances such as incidents related to collision, slip-trip-fall, and working at height.
Text-document clustering-based cause and effect analysis methodology for steel plant incident data
(Taylor & Francis, 2018-03) Verma, Abhishek
The purpose of this study is to develop a text clustering-based cause and effect analysis methodology for incident data to unfold the root causes behind the incidents. A cause–effect diagram is usually prepared by using experts’ knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. The study also helped in finding out the anomaly in incident reporting.