Advances in Industrial and Production Engineering

As today’s business environment is highly competitive and dynamic, APM is seen as a winning project management methodology to be adopted, especially in the IT business. However, with the popularization of APM, new challenges emerge. This study identifies the barriers in APM implementation and obtains relative rankings for their importance using the input of industry experts and Multi-Criteria Decision-Making (MCDM) techniques. Following the thorough review of the literature and five individual interview sessions, a total of 13 barriers of APM were identified and categorized under 4 main themes (High-level Organizational, project planning, team operation, and Quality Management). The final weights of the 13 APM barriers were obtained with the application of a recently introduced MCDM technique called Best–Worst Method (BWM). The findings from this study reveal that the top five sub-barriers are in order of priority: the lack of adequate and accurate description of tasks, the unclear time requirements and project schedule, the lack of effective communication/knowledge sharing, the lack of clarity in team roles and task ownership and the lack of understanding of agile project management principles. This reflects the fact that barriers are spread out into a wide range of factors, highly dependent on the inherent features of APM as well as on human communication and social skills.

The automotive service sector is one of the most crucial parts of the Indian automotive landscape. Besides creating jobs for the economy, it contributes to the overall success of the automotive industry. The field of increasing the operational efficiency of the automotive service center remains relatively unexplored, as there are not many studies in this area. Many Kaizen improvement initiatives have been proposed. This work involves modeling of automotive service center using IBM ILOG CPLEX mixed integer optimization. This includes layout optimization, increased use of On-board diagnostics (OBD) reader, resource optimization, etc. This research draws parallels between automotive service centers and job shop production. The optimization of service center has been carried out like a job shop production.

In spite of the potentially enormous benefits, small and medium Enterprises (SMEs) are falling behind in digital transformation. Emerging technologies provide a variety of applications that can help these companies to enhance their performance and get around the size-related obstacles that stand in the way of conducting business. SMEs play a major role in the economy as they generate jobs on a larger scale. In the era of industry 4.0, SMEs are also required to upgrade their supply chain with the integration of digital technologies, e.g., Cloud Computing, IoT, and Artificial Intelligence to meet global competition. During the pandemic COVID-19 suddenly a gap is created to keep business running smoothly due to supply chain disruption and the SMEs are most affected due to pandemic. A need for the integration of technology with the supply chain is created to make the supply chain agile and sustainable. This paper studies the challenges in order to adopt the digital transformation in Indian SMEs.

Small cardamom is one of the most popular and expensive spices in India. Two top constraints as judged in the year 2019 were labour shortage during production and price fluctuations during the marketing of this crop. This work is an attempt to forecast the price of small cardamom by using its price data from May 2015 to December 2019. It is evident from the data that there is no seasonality in the crop price data during that period. So, Sen’s slope estimator and Mann–Kendall tests are employed to estimate the price trend, and it is found that there is an increasing trend with a magnitude of 0.429. Thus, ARIMA (Autoregressive Integrated Moving Average) model is used to predict the price of the crop for the 2020 period, where it is applied different combinations of (p, d, q) values based on ACF (Auto-Correlation Function) and PACF (Partial Autocorrelation Function) plots. By using standard criteria such as RMSE (Root Mean Square Error), MAPE (Mean Absolute Percentage Error), and MAD (Mean Absolute Deviation), the accuracy of the selected models was assessed. The ARIMA (3,1,3) model performed better in forecasting the prices for small cardamom in southern India. COVID-19 (2019–2020) had a significant impact on the price of small cardamom in southern India, where the price has more fluctuations with a variance of 639,147.93 compared to the forecasted price variance of 65,199.97.

In 2015, the UN resolution aimed to adopt seventeen Sustainable Development Goals (SDGs) to tackle climate change, inequality, and poverty. The timeline of SDGs implementation was to be spread over the next 15 years and intended primarily to check the pollution echelons and rapid degradation of resources due to massive industrialization. However, numerous barriers impede sustainability adoption in supply chains. This paper identifies and assesses the environmental barriers to Nickeling SMEs in India. In this study, the barriers are evaluated, and their interrelations are studied to develop a hierarchical solution that would help in the productive adoption of SDGs from an Indian perspective. The instrument used for this purpose is Interpretive Structural Modelling (ISM) which is entirely adjustable for identifying and establishing relationships among various barriers. The present research work considers nine obstacles that act as an impediment. These nine barriers are further plotted on their driving and dependence powers and categorized into four zones: Autonomous, Dependent, Linkage, and Independent. The analysis helps assess and prioritize the environmental barriers to sustainability implementation in the Nickeling SMEs from an Indian perspective.

The implementation of industry 4.0 is influenced by numerous factors. These factors identify so that some problems can be rectified during the initial phase of adoption of industry 4.0. There are various aspects to consider that affect the application of industry 4.0 which has their positive and negative impact on it. Hence in this research the elements influencing industry 4.0 implementation are identified and ranked them using expert opinion and the Fuzzy AHP method. Total fifteen factors were identified and results showed that the measures of cyber security have the highest weight 0.303 and ranked one. Similarly, the first three ranking factors have a cumulative weight of 60%. The aim of undertaking such study was to facilitate the industry professional and company management to identify the areas of concern that may impede or accelerate the adoption of industry 4.0.

Sustainability has grabbed attention in recent times. Customers’ awareness and demand of sustainable products have turned their focus to the manufacturing sector. Adoption of sustainable practices in manufacturing is a challenging task due its environmental and socio-economical balancing nature. In the current scenario, manufacturing small and medium enterprises (SMEs) need special attention on the strategic and planning front. Processes and practices of SMEs need significant changes as per sustainability criteria. In the current paper authors have studied the environmental sustainability of practices of Indian SMEs. For collecting information on sustainable practices of manufacturing organizations authors have conducted an online survey and a series of personal interviews. A designed survey format was circulated among all organizations for collecting observations on the current and future state of environmental sustainability of practices. Based on responses to the survey and personnel interviews authors have calculated the index of environmental sustainability of manufacturing practices of organizations that participated in the survey. The finding of study highlights the importance of environmental sustainability and future action plan for Indian manufacturing SMEs in the same direction.

During the times of pandemic, every sector tried to get through the rough journey especially the manufacturing industries which not only had to get along with the change in demands but also suffered a great loss of their human resources which makes us clearly understand the importance of achieving the goal of social sustainability. Exercising on enablers can be the new ray of hope where we can start fresh to stabilize and mend the loose ends in the industrial sector. The author of the study aims to investigate the detailed characteristics of the social pillar in sustainable manufacturing, highlighting the enablers which can act as either critical or driving in nature for enterprises, especially after the period of post-pandemic recovery. The main discussion is about an underrated pillar of social sustainability with an approach named as fuzzy DEMATEL method which we are using in the following present research for the valuation and evaluation of the major enablers for industries. In the following study, we aim to find the most effective enabling factors for the social aspect of sustainable manufacturing.

Sustainable manufacturing practices have been part of the futuristic approaches of manufacturing organizations for many years. Out of three aspects of sustainability economic, environmental, and social, social sustainability needs urgent attention in the pandemic era, especially in small and medium enterprises (SMEs). Understanding the uncertainty of pandemics and the contributions of SMEs in the manufacturing sector, the authors of the current paper observed the need for a holistic study on the social sustainability assessment of digital practices in Indian SMEs. In this study, the authors have identified and analyzed the indicators of social sustainability with reference to digital practices in the Indian SMEs in the current era of digitalization and the COVID-19 pandemic. The sustainability mathematical model and Analytic Hierarchy Process have been used for prioritization and efficient selection of sustainability factors. The weights for the social sustainability factors were calculated by a series of personal interviews of experts, and a questionnaire-based survey of the Indian SMEs. An experienced team was organized to collect data and analyze the impact of various elements on one another. The questionnaire was distributed to 30 specialists to collect responses (20 from Industry and 10 from academia). A total of 22 experts (14 from industry and 8 from academics) responded to the questionnaire. In the framework of the Indian situation, a mathematical model was developed for evaluating the social sustainability index. From the results, the authors noticed that Indian SMEs are less concerned about social sustainable practices as they are economically weak and are under extreme pressure from stakeholders. The social sustainability issue of 47.42% justifies the findings of this research.

Ever since its inception in 2011, Industry 4.0 has garnered much attraction from a research point of view. Presented for the first time by the German Federal Government, I4.0 was introduced to improve the country’s industrial capability through digital manufacturing techniques. I4.0 looks to digitize, automate and optimize the manufacturing processes, adding value to the products and amenities offered and providing tremendous growth opportunities for the organizations. I4.0 uses the latest techniques like the Industrial internet of things, smart manufacturing, cloud manufacturing, etc., to increase productivity and ensure higher level of control over the product’s lifecycle. These new and upcoming technologies have had an enormous impact globally, and it has become essential for MSMEs (Micro Small and Medium Enterprises) to use them to their benefit. For organizations like these, with limited resources, Artificial Intelligence (AI) technologies can act as the tool needed to ensure their survival and grow further. This study will help MSMEs achieve their goals by providing them with a list of critical success factors for implementing I4.0, researched and reported according to the PRISMA Approach.

In the current era, all organizations understand the significance of sustainability as it is a critical role player in the community’s well-being and longevity. The major focus is on adopting sustainable manufacturing practices, including aspects of the environment, social, and economy. In the current research paper authors have studied the social sustainability of practices of SMEs under the current era of COVID. For collecting information on the social sustainability practices of organizations authors have conducted an online survey and a series of personal interviews with organizations. A designed survey format was circulated among all organizations for collecting observations on the current and future state of social sustainability of practices. Based on responses to the survey and personnel interviews authors have calculated the social sustainability index (SSI) of practices of organizations that participated in the survey. The finding of the study highlights the importance of sustainability that enterprises are continuously trying to contribute toward social sustainability by being more productive to society. Though 25–300% of the effort toward employment aspect is needed to solve issues, especially in manufacturing, construction and retail sector. The healthcare and utilities sector needs to put more effort of 17–20% into managing their business practices and policies. The results also signify that to contribute toward sustainability, enterprises require to extend their works and services toward society and their respective customer by 90% as different parts of society require mends after the catastrophic effects of the COVID-19.

In the last few decades, there is massive growth in the electrical and electronic industry as a result of these more products are getting obsolete, hence, there is rapid growth in the electrical and electronic waste, demand of electronic product growing enormously resulting more amount of e-waste. Remanufacturing is a method for transforming End of use product (EOU) or End-of-life product (EOL) to the given standards or specifications of the original product, remanufacturing is at an emerging stage in India. In this study, author aims to detect the critical factors, which will affect e-waste remanufacturing and find out the logical relationship between them by using interpretative structural modeling (ISM). Ten factors have been taken using of literature survey and experts in the respective field. Further, these ten factors are divided into six levels based on the logical relationship among them or by using ISM model, top levels show strong dependence power, and the bottom level shows strong driving power. The result shows that government support (GS), timing of return (TR) and marketing strategies (MS) are the major critical factors in the hierarchy and shows strong driving power in remanufacturing, market access (MA), product design (PD), collection strategies (CS), return intension (RI), consumption attitude (CA), assured warranty (AW) and workforce and technology (WT) shows strong dependence power as they are in top levels of ISM model. Government support (GS) are at level six of ISM model shows strong driving power it is one of the critical factor, which strongly effects the remanufacturing of e-waste, government, have to come onward and offer subsidy to both manufacturer and consumer to encourage remanufacturing. This study promotes the manufacturer for sustainable e-waste remanufacturing in India.

In the prospect of improving supply chain delivery planning, this paper introduces an improved vehicle routing model using the Ant colony optimization approach. Based on literature surveys, many related articles on VRP (vehicle routing problem) models proved suitable results exclusively in the hunt for an optimal delivery tour. The use of Ant colony optimization aid to ameliorate the supply chain delivery process by finding the best shortest route tour for consumers’ packet delivery to all locations. Ant colony optimization is an AI (Artificial Intelligence) technique that is based on a Metaheuristics approach with the main goal of effectively piloting through plausible road path selection using multi-Ant agents and appreciably scaling down the time aspect in the search of a fast and efficient delivery tour mission. Ant colony approach enables efficiency for the best routing selection process and time-saving while maximizing profit. For this study, a typical scenario of a supply chain delivery planning problem is assumed to illustrate the application of Ant colony optimization technique, and an efficient routing path selection is found after the computation of the model for both scenarios (from depart to return), and the total distance covered is minimal. Furthermore, graphical representations will be showcased for the problem scenario, the theoretical analysis and model formulation of Ant colony optimization are also explained, and a step-by-step Ant colony algorithm steps wise approach are described, and also other illustrations are well exhibited in below sections.

There are key parameters that influence the performance of deep drawing operation in terms of thickness variation of the drawn object, wrinkles, and energy required. The most significant parameters are blank holding force (BHF), friction, punch edge radius (rp), die ingress edge radius (rd), and punch force. Design of experiment (DOE) is designed using three levels of each of the three factors; BHF, punch radius, and die radius. The blank thickness is fixed as per the specifications of flat pot product (cooking vessel). The wrinkling is predominately controlled by the holding force against the die. Thickness variation is inevitable but must lie within an acceptable range. Simulation of deep drawing is performed on Aluminum 1050 material (blank dia. 391 mm and thickness 0.61 mm). The design of the experiment (DOE) has been used as an optimization tool to find out the workable process parameters; BHF, rp, and rd. The feasible and optimal values of process parameters (rp, rd, and BHF) have also been obtained based on minimum energy criteria. Computer-aided Engineering (CAE) modeling of flat pot (circular cup) is performed using FEM software ABAQUS 6.14 and DEFORM 3D. Based on the minimum energy criteria using Taguchi method, it is found that (BHF = 80 KN, rp = 10 mm, and rd = 8 mm) yield the minimum energy for performing optimal deep drawing. The energy required for the product under consideration is 2270 N-m.

In the current scenario a constant rise of cybercrime has been observed in the cyberspace. There has not only been an increase in the number of cyber criminals in the past few years but also an increase in the level of technology and methods used to commit such malicious attacks in the cyberspace. This paper discusses algorithms used in the supervised domain of machine learning such as regression algorithms especially linear regression, polynomial regression, logistic regression as well as other algorithms in the supervised domain of machine learning like Naive bayes, K-nearest neighbors (KNN), Support vector machines (SVM), Decision Trees, Random Forests and other algorithms have been discussed. It can be used for identification, classification, and optimization of malware control techniques of cybersecurity systems and also identify the particular type of algorithm in which it is best suited for dealing. The kinds of malware as well as the drawbacks of certain kinds of algorithms in certain situations and how they can be improved or made more efficient have also been discussed.

Economic sustainability is one of three aspects of sustainability and focusses on resources conservation in long run. Three basic concepts of recovery, reuse, and recycle are used while implementing economic sustainability. Economic sustainability involves making decisions in the most balanced and fiscally sound manner. Practices of economic sustainability stabilize the economy from uncertainties. Operations become resilient, buoyant, and strong in an uncertain environment. In this research paper authors have identified and analyzed the enablers of economic sustainability. Few of enablers identified are international issues and globalization, energy management, innovative product design, latest manufacturing facilities, competitive manufacturing strategies, continuous evaluation of performance in supply chain, latest flexible management and policies, local and global sources of finance, demand certainty, ethical and integrity of business, and entrepreneurship growth. For analysis of enablers, authors have used DEMATEL techniques. Findings of the study will help policymakers in strategically managing the challenges of the future.

Green building rating systems developed in the last few decades has stressed on the evaluation of the buildings on sustainability from starting of construction till life cycle ends. There is a set of indicators and processes to evaluate the buildings. However, sustainability analysis of operational building is a challenge. Aim of the current study is to examine the operational educational institution building on sustainability dimensions. In this research paper, the authors have studied the design of an educational institution building for implementing sustainability practices of lighting, water, and energy consumption. The sustainable evaluation of buildings has both tangible and intangible benefits. The unmistakable advantages are the decrease in water and energy utilization. Theoretical advantages incorporate well-being and prosperity of youngsters, improved air quality, and magnificent day lighting. The case school building follows international standards of building design and implemented several sustainable techniques. Working in a similar direction authors have calculated the whole load of the building in kWh and its EPI (Energy Performance Index). The authors also proposed to set up the roof-mounted solar power plant and do the calculation for same. The total load in building is found to be 295,457 kWh (EPI: 32.5). Cost of installation of the rooftop solar power plant is estimated as Rs. 5,07,00,000.00. From further analysis, the authors observed that the cost of installation of rooftop solar power plant can be recovered in approximately 7 years taking into account the current electricity tariff issued by DERC (Delhi Electricity Regulatory Commission).

The several techniques that are used in stock analysis and market forecasts are nowadays based on machine learning (ML). They rely solely on previous market trends for a particular stock and mainly aim to predict the future pricing by analyzing a certain pre-specified patterns in the dataset. Although this approach provides better and accurate results, it sometimes fails to consider some of the other variables like political events, rumors, public sentiments, and some other psychological events that may eventually affect the market. In this paper, analysis and implementation are performed to predict stock and market analysis. It may use not only in-depth technical analysis but also basic research on a given stock. Variable inputs, which include political issues, ongoing events, etc., are included in the technique that determines the predicted value. This proposed work’s analysis can be used to develop a more accurate ML method that combines mathematical value input and emotion analysis to achieve perfect ally prediction in market trends.

Over the last few decades, economic change, sustainable product demands, governmental policy towards climate change and market influence have led to a surge in the level of competition. To remain in the market, industries should encounter the demands of customers to achieve the most excellent satisfaction among customers. Primarily, micro-, small and medium enterprises (MSMEs) face these problems as they have restricted resources. There is only one way to enhance productivity by optimizing accessible resources by minimizing waste and defects in products and operations to stay in this huge competition. That is why Lean Six Sigma (LSS) is evolving as a probable approach that can easily modify operations. LSS is a comprehensive strategy that can reduce waste and variation in processes to improve the productivity of an organization. But numerous organizations are facing challenges in executing this approach effectively. Thus, the key aim of current research is to recognize the critical success factors (CSFs) of LSS in the context of MSMEs. Furthermore, the Interpretive Structural Modeling (ISM) technique has been used to establish a relationship between LSS CSFs. The study reveals CFS 13 (Linking the LSS principles with business strategies), CSF 12 (Organizational profitability), CSF 11(Matrices & Data) and CSF 10 (Communication) are the utmost CSFs for the execution of LSS approach in MSMEs. The present work encourages industrial managers and academic researchers to successfully execute this approach in their organization by focusing on CSFs before execution.

Medical device manufacturing industries, being highly technology-driven, have witnessed rapid growth and remained highly competitive. In India, medical device industries have started embarking on their footprints and have abundant scope for improvement. Six sigma though being one of the widely used process improvement methodologies, a little evidence of application is available in the medical device field. This article presents the implementation of Six Sigma methodology to improve the equivalent capability of surgical screw manufacturing process. DMAIC (Define–Measure–Analyze–Improve–Control) approach was used to reduce rejection of surgical screws. CTQ (Critical to Quality) characteristics were selected during the initial phase and related data was collected during the subsequent phase. The causes of rejection were identified and illustrated using the Ishikawa diagram. FMEA was carried out to identify, prioritize, and eliminate the causes of failures. Statistical process control (SPC) was used to monitor and control the process. The rejection level was reduced from 33,000 DPMO (Defects per Million Opportunities) to 7000 DPMO (78.78% improvement) which resulted in the improvement of process capability from 1.2 to 1.38 and 80.56% saving in cost of poor quality (COPQ) leading ultimately leading to improved quality, profitability, and productivity. Equivalent process capability enhanced from 0.71 to 0.88 and process sigma level improved from 3.3 to 3.92. This paper demonstrates application of DMAIC to reduce rejection of a single product but can be generalized for any surgical/medical product. This article will act as lighthouse for medical device industries and researchers working with an attribute type of data to improve the performance of various processes.

Lean is a tool to eliminate industrial waste and improve the manufacturing process to enhance productivity. But the implementation of lean manufacturing involves intensive planning, proper understanding of methodology, efficient inter-departmental communication, technical infrastructure, and resources. The aim of this research paper is to identify the various barriers to the implementation of LM. Based on the literature review various factors such as the poor involvement of management, lack of interest and empowerment of shop floor workers, lack of financial resources, Challenge of updating skills without proper incentive, hesitation of change, and cultural and linguistic barriers hinder the adoption of lean manufacturing. Lean principles and tools are implemented in various sectors like process industries, automobile sector, construction, IT, food processing, etc. The Indian manufacturing industry, key barriers faced and identifies the adverse impact such as management-related barriers, human resource barriers, resource-related barriers, market barriers, and so on. Even now, many Indian MSMEs are using Lean as an improvement tool instead of adopting it as an organizational culture. If implemented on full scale as a culture in the organization, it can reap a very significant improvement in organizational performance. So, based on the literature reviews, this paper provides an insight into the practical barriers to the implementation of lean manufacturing.

This paper gives a detailed study for the use of cloudlet-based mobile cloud computing model and gives an insight into the advantages associated with the cloudlet-based mobile cloud computing model. It also shines a light on various applications and methods of integration of these cloudlets with the new upcoming technologies. A comparison has been done between the traditional cloud model and the cloudlet-based model showing the reduction in latency and increased efficiency while using cloudlets. The results show that the use of cloudlet-based cloud computing model performed better than the traditional cloud computing model, having reduced latency, faster download speed, and a more efficient system.

Smart grid provides new capabilities for the improvement of electrical grid control and operation. To enforce this capability smart meters, phasor measurement units (PMU), supervisory control and data acquisition (SCADA), and wide-area monitoring systems (WAMS) are installed in smart grid. These systems generate a huge amount of data every millisecond. The challenge of storing massive data and analyzing it in real time creates the inevitability of big data analytics in smart grid applications. Also, the integration of smart grid architecture with the existing analytics model is the major challenge. In this article, we have studied the sources of big data in smart grid PMU and have proposed an analytical model which is highly scalable and flexible for accommodating different types of data analytics and handling of massive data. The model is outclassed from the conventional data processing frameworks in the perspective of underlying architecture and control on iterative model execution for accurate detection of events or anomalies inside a smart grid and the classification of the event to take necessary decision. The proposed architecture is a big data-enabled and feedback-controlled model which seamlessly integrates data analytics and machine learning with the smart grid architecture. The execution of the model is controlled by a feedback loop considering the reference of event threshold which builds the novelty of the model.

In a number of disciplines and applications embedded systems now play an essential role, such as network embedded systems, real-time embedded systems that provide for vital mission areas with time limits, stand-alone systems with network routers, etc. A superb processor implementation built to suit consumers’ demanding requirements. In addition, there is a broad deployment of sensor networks to give enhanced capabilities, but a specific operating system must be given in order to control such types of embedded devices. This paper introduces several software infrastructures capable of supporting such embedded systems. Embedded systems are also used in the medical, transportation, and wireless sensor network fields, specifically in medical imaging, vital signs, car electric vehicles, and Wi-Fi modules. it should be evolved and able to function on a LAN or a wide-ranging shared network. It must also be ensured that it offers the same user environment facilities on the basis of their demands. Precision and precision are vital as the environment changes. Incorporated operating systems for a certain purpose are designed.

Hard turning characteristically refers to a machining process of hard steel having a hardness greater than 45 HRC. Nowadays, hard steel turning attracted considerable attention among manufacturers of automotive, ball bearings, die, and gear industries. Hard turning offers several challenges with respect to the selection of cutting tools with enhanced tool life and high-accuracy machining. However, to overcome these challenges, the present research implemented an environment-friendly air–water mix spray cooling system for machining hardened D2 grade steel (56 HRC) using an uncoated carbide tool. Air–water mix spray cooling is a novel cooling strategy and is rarely utilized in hard turning tests. Further, the artificial neural network (ANN) modeling was executed to forecast the values of workpiece surface roughness (Ra) and chip-tool interface temperature (T). Three ANN architectures 3-6-1, 3-7-1, and 3-8-1 have been trained by using Levenberg–Marquardt (L-M) algorithm, and performance was computed based on the lowest mean square error. The prediction statics (R-square results) revealed that the 3-7-1 ANN architectures exhibited superior accuracy for predicting the Ra and T in comparison to other architectures (3-6-1 and 3-8-1). Further, for each structure, the average error between experimental and model-predicted results was computed and compared. The least average error of Ra (1.961%) and T (0.698%) was noticed for 3-7-1 architectures. However, ANN 3-7-1 architecture is the best suited for the prediction of responses in hard turning compare to other considered ANN architectures.

Green product design is influenced by numerous factors. These factors are identified so that a green product can be designed and manufactured keeping in mind that the environment is not harmed. Due to environmental concerns, organizations are giving more emphasis to designing green products. However, several factors affect the adoption of green product design and their realisation through manufacturing. Hence the present study identified the factors affecting green product design and ranked them using expert opinions and the Fuzzy AHP method. A total of nineteen factors were identified and the results showed that implementing 6 R concepts (i.e., reduce, reuse, recover, recycle, remanufacturing, and redesign) has the highest weight of 0.279 and ranked one. Similarly, the first three ranking factors have a cumulative weight of 56.2%. The aim of undertaking such a study was to facilitate the industry professionals, and company management to identify the areas of concern that may impede or accelerate the green product design adoption.

Setup time or changeover time in a machine is the time required to change the setup required to run the next batch of production. The changeover time can range anywhere between few minutes to few hours and due to this changeover, the total time required for production reduces in a shift. This necessitates that the setup or changeover time has to be significantly reduced. Single Method Exchange of Die (SMED) concept is widely used in such situations where drastic reduction in changeover time is expected. This work proposes the application of SMED to reduce the changeover time in a winding machine of the capacitor line. Two machines—Low Voltage and High Voltage machines are considered for the study. The setup activities that take place over these machines are observed and classified as internal and external activities depending on whether the activity can be carried out even during the operation of the machine, or it could be separately carried out and made ready for immediate changeover. Presently, the changeover time for Low Voltage machines is 24.82 min and that of High Voltage machine is 12.67 min. With the application of SMED, the changeover time is reduced to 17.49 min on Low Voltage machine and 8.35 min on the high voltage machine. This paper also indicates the reduction in downtime costs on these machines that could be realized with the application of SMED concept.

Titanium and its alloys are broadly used these days in airplanes, marines, for military purposes and in various other fields. The use of titanium alloys in such a large quantity and in various fields is only feasible due to their light weight, high strength, and ability to resist high temperatures. The machining of titanium is very difficult due to its greater hardness. Therefore, it is very important to optimize its machining process through optimizing the input variable parameters. The present experimental research study aims to find the best set of machining input parameters including gap voltage (Vg), pulse on time (Ton), and peak current (Ip) that gives the best results while performing experiments. In this work, the work piece chosen is titanium grade five alloy (Ti6Al4V), and the electrode is copper. Surface roughness (Ra) and material removal rate (MRR) are the performance parameters analyzed in this work. Experimentation was done based on the Taguchi L9 matrix and the TOPSIS method was utilized to determine what must be the optimum combination of machining parameters. The optimum condition (Ip3Vg2Ton3) was found, i.e., Ip = 14 A, Vg = 50 V, and Ton = 400 μs.

Tool wear is a key factor that contributes to the productivity and cost of the machining process. Premature failure of a tool can cause unexpected machine down-time and material loss. Also, tool wear affects dimensional accuracy and surface finish produced on the workpiece during machining operations. With the view-point of monitoring the tool wear, machining experiments were performed on AISI 4140 steel using cemented carbide insert on the CNC turning center. Three different prediction algorithms based on learning techniques using the deep learning toolbox of MATLAB were utilized. Parameters of the design and training process for the neural networks have been optimized. The proposed neural network approach resulted in an accuracy of around 98%. Therefore, the proposed approach can be utilized to predict the amount of tool wear with higher accuracy resulting in enhanced productivity and part quality.

In metal cutting industries, machining is one of the most basic operation. A higher amount of heat is produced in the machining interfaces due to the shear deformation of the workpiece material. Usually, a conventional cutting fluid is applied to reduce the heat generated. However, these cutting fluids lead to a negative effect on the health of the operator and natural resources. Solid lubricants that are able to withstand high pressures and temperatures can be used to improve the machining performance. Therefore, an effort has been made to examine the applicability of minimum quantity lubrication (MQL) applied with castor oil and solid lubricant during machining of AISI 4140 steel. Comparison of findings revealed significant improvements in surface quality, reduced energy consumption, and a reduction in the tool wear with solid lubricants assisted MQL when compared to the dry turning. It is attributed to enhanced tribological conditions in the machining zone with the presence of MoS2 in the cutting fluid.

In this study, metal removal rate (MRR) and tool wear rate (TWR) has been optimized simultaneously during electric discharge drilling (EDD) of Titanium alloy. Two process variables such as discharge current and duty cycle have been considered for controlling the output responses. Experiments for the study have been conducted using the copper tubular electrode with deionized water as dielectric fluid. The responses have been optimized by using the Taguchi-based grey relational analysis (GRA) and Principal component analysis. Finally, the analysis of variance (ANOVA) has been carried out to find out the significance of parameters. The application of approach predicts the optimum level of input parameters and the experimental results obtained at optimum level confirms the optimization of the process.

India has 42.5 million small and medium-sized enterprises (SMEs). 45% of all goods are produced by 95% of all industries. Small and medium-sized enterprises (SMEs) account for over half of all exports, contributing to global growth and fiscal significance. Industrial revolution 4.0 can potentially improve SME efficiency and future decisions in support of technological innovation, but it could also present new obstacles. Our goal was to examine the impact of digitalization on small and medium-sized enterprises in India’s industrial sector. SME survival and growth policies were discussed, and numerous digital technologies were identified as possible aids and discussed.

In order to prevent a company from placing orders too frequently or having an excess of inventory on hand, a number of EOQ models have evolved throughout time to ensure that the right amount of inventory is ordered in every batch. EOQ models are required for inventory management, which is the evaluation of the ordering, storing, and usage of a company’s inventory. This paper focuses on the development of an EOQ model for a specific class of goods, namely growing items, when the supplier extends a trade credit policy to the buyer. The weight of each item increases at a consistent rate, and the growth function of the items is assumed to be a linear growth function. Prior to a specific numerical model that is illustrated with the help of numerical examples, a broad scientific model has been proposed. Sensitive research is offered to evaluate the impact of the model’s major variables while taking its decision variables and objective function into account.

Through this study, the supply chain model i.e., seller-buyer model is investigated using carbon emissions costs, i.e., the projected maximum cost incurred by a sector. This study examines the supply chain model in which the buyer imposes unit price and unit marketing expenditure, controls the item's demand. The setup costs, order costs, and expenses are related to the carbon emissions of manufacturing and transportation activities in a two-tiered coordinated supply chain. A supply chain model is developed using a non-cooperative game theoretical method to support the interaction and democracy of the supply chain's buyers and sellers. The Stackelberg game strategy, in which the leader is one of the participants and the other has the role of the follower, and he is employed in the non-cooperative method. Finally, numerical examples, including sensitivity analysis, are shown to highlight the significance of the theory offered in the work and this shows in the Seller-Stackelberg model, both the buyer and the seller profit when the α parameter changes, whereas in the Buyer-Stackelberg model, both the buyer and the seller profit when the β parameter changes.

A design and development of swing grinding and gate cutting fixtures has been carried out to improve productivity. The 3–2-1 design technique was used to design the fixture. A case study is taken for Ballast 113, Adapter 117, and Spacer block components. The fixture allows the components to hold forces and optimize the design for machine operation and process function ability. The performance of the proposed fixture for Ballast, Adapter, and spacer block is calculated in terms of cycle time, production quantity per shift, and grinding cost per shift. The cycle time in Ballast, Adapter, and Spacer block is reduced by about 41%, 14%, and 44%, respectively. The Production quantity per shift in Ballast, Adapter, and Spacer block is increased by 70%, 15%, and 54%, respectively, after employing the designed fixture. The grinding cost per shift in Ballast, Adapter, and Spacer block is increased by 41%, 145%, and 37%, respectively, sing the designed fixture. The Annual ballast, adapter, and Spacer block are about near Rs 114,240/- was saved with these fixtures by using designed fixtures.

Through the application of the best material removal method, AFM and centrifugal force were utilized in this study to improve the uniformity of the work piece's surface and therefore improve the overall quality of the work piece. In response to the centrifugal force applied to the cylindrical workpiece, the abrasive particles created have a centrifugal orientation with respect to the axis of rotation that is typical. Material removal productivity was investigated using a Taguchi L9 orthogonal array, with the Rod Rotational Route Speed, CFG Rod Shape, and No. of Cycles being investigated. Contribution of the rod's rotating speed to material removal is the most important factor to consider, followed by using CFG no. of cycle and rod shape.

This article mainly focuses on estimating and optimizing the solution to decrease the weight of an Injection moulding machine so as to increase its performance and life. It considers all the aspects that can be optimized to lower the weight of the machinery while maintaining its safety factor which need to be theoretically analysed at first with as there are several possibilities to do so thus coming up with the idea of design development and analysis of injection moulding machines. A total of 05 cases were considered to reach final required capacity. The detailed parts of the injection moulding machine were listed out in terms of assemblies and sub-assemblies. Material selection was done for barrel and screw, by analysing various materials using Ashby chart. The potential material was selected with the particular objective for each component. CAD models of the components were developed; material costing and welding costing (as per AWS) of the injection moulding machine was also done. The designed components were then analysed using FEM using selected materials, forces and other boundary conditions. As the result, the weight of the screw was reduced by 52%, the materials for screw and barrel were changed to Nickel Duranickel Alloy 301, annealed and aged and Tungsten Alloy AISI T6 respectively. The overall weight of the machine was predicted to get reduced by 30 to 40%.

In a laser beam, the laser beam burns the upper layer of the area to be engraved. The burnt area is left colorless making it visibly different in the environment. While in laser cutting the laser beam has to penetrate through the surface. This can be achieved by prolonging the beam on a particular area for a long period, the duration is decided on the strength of the material to be cut. This project proposes a method used to build a Computerized Digital Laser Engraver. The uniqueness of this machine is that the user can change the tool whenever he wishes to do so several works such as carvings (Materials—Acrylic, MDF board, Foam sheets, etc.,) and jointly produces a 2D drawing of an object on a sheet of paper. Laser Engraver responds to G Codes can be produced by the software. Machine structure and component operation will be supported by a type of Cartesian. It is a commercially viable and inexpensive machine.