Search results

The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly.

The proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn.

The proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective.

This paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

In the field of knitwear, dimensional stability is assumed as a critical problem that affects the quality and salability of a product. Although much work has been done in this area with a focus on the factors affecting fabric shrinkage, however, there is a lack of work on knitwears with respect to their dimensional stability. The purpose of this paper is to investigate the impact of stitching parameters and wash types on the dimensional properties of knitwear.

The crew-neck t-shirts were prepared by using pique knitted fabrics. Different sewing and finishing parameters were used that include stitch density, stitch type, stitching thread and wash type. The critical measurements of the selected garment are taken as output variables which are body width, sleeve length, body length and across shoulder. After laundering process, shrinkage percentage was calculated by using before-wash and after-wash measurements.

This study shows that the stitching parameters affect significantly on knitwear’s shrinkage. Thus, when patterns are being developed for the cutting of fabric, expected shrinkage, known as residual shrinkage, must be considered to avoid unexpected changes in garment shape.

This research will be useful for knitwear manufacturing industry.

The purpose of this research is to identify major barriers to the implementation of reverse logistics (RL). Also, the study addresses best practices among reuse, remanufacture, recycling, refurbishment and repair as alternatives for RL processes.

This study targets supply chain management experts for their opinions regarding the identification of critical barriers and alternatives for RL implementation. Their opinions were extracted through a Web questionnaire based on 14 criteria with 5 alternatives. The tools of multi-criteria decision-making are used for analysis, i.e. fuzzy VIKOR and fuzzy TOPSIS.

The results indicate that lack of recognition of competitive advantage to be gained through RL practice is the most critical barrier to RL implementation. The least barrier or major facilitator for RL is “supportive initiative for end-of-life products.” The top-ranked alternative in this study is reuse followed by remanufacturing. The least important alternative is “repair” in the case of Pakistan. These alternatives are ranked based on “q values” derived through fuzzy VIKOR.

The results of this study can only be generalized for the manufacturing sector of Pakistan during the period of the study.

The findings of this study will assist managers in deploying the best practices concerning RL.

Fuzzy VIKOR and fuzzy TOPSIS have not been applied to RL alternatives in previous research.

The learning outcomes are as follows: what tasks are to be done by medical representatives in pharmaceutical industry? This study also highlights various competencies required to do effective selling in this industry; analyzes and discusses different unethical practices going on in the market; explains why ethical norms are necessary in sales context when sales targets are already achievable with unethical means; and develops and comments on strategies Flori Pharmaceutical can make to overcome on these unethical issues. What should be the response of Dahar to the email of Naveed khan? What course of action should be taken by Dahar in the deceitful reporting case of Mohsin Ali?

Flori is considered a leading and growing multinational organization in the highly competitive environment of Pakistan pharmaceutical industry with over 40 years of experience. The company aims to command a leading position in developing new health-care products as it offers a wide range of diabetic, cardiovascular, respiratory and vitamin products based on quality as a result of high research and examination. Recently, an email to Bilal Dahar on March 2017 from Flori’s star sales person Naveed Khan has forced management to take some strong decisions regarding ethical norms and values to be adopted by medical representatives of Flori pharmaceuticals. The email highlighted the issues related to sales pressure which are leading toward unethical sales practices. Dahar just not have to maintain Flori’s ethical code of conduct but he and his team also has to work hard to achieve more than 26% growth rate in sales revenue as compared to last year. Dahar knew that the highly competitive environment of pharmaceutical industry has led most of the stake holders to indulge in unethical behavior to achieve their individual targets. He knew that this is dangerous in long term for the multinational organizations such as Flori pharmaceuticals as if the similar behavior continues, the sales culture and values of the organization would be on stake. He also has to decide what decision to be taken against deceitful reporting issue of one of the top-performer territory managers, who was key person in helping Flori to close the sales year 2016 with the revenue of Rs. 6.4bn, a 26% growth over the last year. The case is rich enough to provide a platform regarding management of several ethical challenges in pharmaceutical selling and developing strategies based on them.

BBA, MBA final year.

Teaching Notes are available for educators only.

CSS 8: Marketing.

This work highlights the optimization of the consumed amount of sewing thread required to make up a pair of jeans using three different metaheuristic methods; particular swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) techniques. Indeed, using metaheuristic optimization techniques enable industrialists to reach the lowest sewing thread quantities in terms of bobbins per garments. Besides, the compared results of this research can obviously prove the impact of each input parameter on the optimization of the sewing thread consumption per pair of jeans.

To assess objectively the sewing thread consumption, the optimized sewing conditions such as thread composition, needle size and fabric composition are investigated and discussed. Hence, a Taguchi design was elaborated to evaluate and optimize objectively the linear model consumption. Thanks to its principal characteristics and popularity, denim fabric is selected to analyze objectively the effects of studied input parameters. In addition, having workers with same skills and qualifications to repeat each time the same sewing process will involve having the same sewing thread consumption values. This can occur in some levels such as end of sewing, the number of machine failures, the kind of failure and its complexity, the competency of the mechanic and his way to repair failure, the loss of thread caused by threading and its frequency. Seam repetition due to operator lack of skill will obviously affect clothing appearance and hence quality decision. Interesting findings and significant relationship between input parameters and the amount of sewing thread consumption are established.

According to the comparative results obtained using metaheuristic methods, the PSO and ACO technique gives the lowest values of the consumption within the best combination of input parameters. The results show the accuracy of the applied metaheuristic methods to optimize the consumed amount needed to sew a pair of jeans with a notable superiority of both PSO and ACO methods compared to experimental ones. However, compared to GA method, ACO and PSO algorithms remained the most accurate techniques allowing industrials to minimize the consumed thread used to sew jeans. They can also widely optimize and predict the consumed thread in the investigated experimental design of interest. Consequently, compared to experimental results and regarding the low error values obtained, it may be concluded that the metaheuristic methods can optimize and evaluate both studied input and output parameters accurately.

This study is most useful for denim industrial applications, which makes it possible to anticipate, calculate and minimize the high consumption of sewing threads. This paper has not only practical implications for clothing appearance and quality but also for reduction in thread wastage occurring during shop floor conditions like machine running, thread breakage, repairs, etc. (Kawabata and Niwa, 1991). Unless the used sewing machine is equipped within a thread trimmer improvement in garment seam appearance cannot be achieved. By comparing and analyzing the operating activities of the regular lock stitch 301 machine with and without a thread trimmer, a difference in time processing can be grasped (Magazine JUKI Corporation, 2008). Time consumed in trimming by a lockstitch machine without a thread trimmer equals 3.1 s compared to 2.6 s by a thread trimming one. Hence, the reduction rate in the time processing equals 16.30%. This paper aimed to implement the optimal consumption (thread waste outstanding number of trials). Unless highly skilled workers are selected and well-motivated, the previous recommended changes will not be applied. The saved cost of the sewing thread reduction can be used to buy a better quality of fabric and/or thread. However, these factors are not always the same as they can vary according to customer's requirements because thread consumption is never a standard for sewn product categories such as trousers, shirts and footwear (Khedher and Jaouachi, 2015).

Until now, there is no work dealing with the investigation of the metaheuristic optimization of the consumed thread per pair of jeans to minimize accurately the amount of sewing thread as well as the sewing thread wastage. Even though these techniques of optimization are currently in full development due to some advantages such as generality and possible application to a large class of combinatorial and constrained assignment problems, efficiency for many problems in providing good quality approximate solutions for a large number of classical optimization problems and large-scale real applications, etc., are not applied yet to decrease sewing thread consumption. Some recent published works used statistical techniques (Taguchi, factorial, etc.), to evaluate approximate consumptions; conversely, other geometrical and mathematical approaches, considering some assumptions, used stitch geometry and remained insufficient to give the industrialists an implemented application generating the exact value of the consumed amount of sewing thread. Generally, in the clothing field 10–15% of sewing thread wastage should be added to the experimental approximate consumption value. Moreover, all investigations are focused on the approximative evaluations and theoretical modeling of sewing thread consumption as function of some input parameters. Practically, the obtained results are successfully applied and the ACO method gives the most accurate results. On the other hand, in the point of view of industrialists the applied metaheuristic methods (based on algorithms) used to decrease the amount of consumed thread remained an easy and fruitful solution that can allow them to control the number of sewing thread bobbin per garments.

This study aims to investigate the role of moderated mediation between innovation in service quality and customer loyalty in the hospitality industry for potential entrepreneurs in the hospitality industry.

The authors have collected data from customers of the hospitality industry of significant cities of Pakistan using 362 structured questionnaires. Structural equation modelling was used to find out moderated mediation between innovation in service quality and customer loyalty in the hospitality industry for potential entrepreneurs in the hospitality industry.

The results showed that the role of customer satisfaction as a mediator and the role of perceived price fairness as a moderator between service quality and customer loyalty is statistically significant. Customers are influenced by price, and it leads to their satisfaction in the hospitality industry.

The results of this study can be used by managers of the restaurant industry to enhance customer loyalty by focusing on customer satisfaction and perceived price.

The evidence documented in this paper is first known to measure the role of a mediator and moderator between service quality and customer loyalty for potential entrepreneurs. This paper will add to the literature of service quality in the hospitality industry concerning the role of customer satisfaction and perceived price fairness for future and potential entrepreneurs.

Earned Value Management (EVM) is widely used as a project performance measurement and forecasting technique. Nonetheless, it has not been fully explored in Pakistani construction industry; where conventional progress reporting methodology (CPRM) is being followed having certain confines. It reports only the financial progress of a project, expresses feeble association between the duration and cost of activities, and forecasts flawed schedule and completion cost. This research implements EVM on under-construction building projects in Pakistan, and compares its upshots with the projects' actual records and with the outcomes of CPRM.

To assess the implementation of EVM on building projects, a set of specific criteria was established. Work Breakdown Structure, Organization Breakdown Structure and Control Points were established. The study has compared the EVM metrics with CPRM outcomes on three under-study building projects, and has deliberated on their mutual differences as well as their relationship with actual cost and schedule performance. Monthly figures of actual spending and completed activities were periodically recorded and compared with planned values for status indication. The graphs were generated to observe the correlation between the results of EVM and CPRM. The data was then extrapolated to forecast the schedule and cost values at completion.

The study discovered that trends of EVM in quantifying the project's cost and schedule performance were strongly correlated and were closer to the actual progress. It has also verified the EVM's soundness in forecasting the cost and schedule, required for project's completion. Contrarily, CPRM metrics could not precisely visualize the current and future, cost and schedule performance.

The case study concludes that EVM's incorporation in progress reporting regime can revolutionize the assessment procedures in Pakistan by rightly indicating the project's current status as well as visualizing the future performance. The study's methodology can also be extrapolated in other countries having similar work environment and economic conditions.

The study aims at providing a reliable system of real-time monitoring for underground mine and tunnels which detects any structural change in the network and reconfigures it for resuming the data delivery process. In high stress environments, e.g. underground mines and tunnels, real-time activity monitoring is an emerging issue. Wireless sensor networks (WSNs) play a key role in ensuring the safety of people working in underground mines and tunnels. WSN not only provide real-time monitoring of underground environment but also detects any structural change in the network itself.

In this paper, results of empirical implementation of a re-configurable WSN, capable of self-healing approach, reconfigure the network connectivity upon failure or addition of nodes in the system. An open-source radio-frequency identification standard for WSN, named as DASH7, is used for practical implementation. The proposed system is capable of determining cluster breakage by sudden disruptions caused by roof falls, explosions and node failures, sensor coverage hole, node re-addition to the network and distress priority signal generation by the miner.

The proposed platform contributes to re-attain network state for establishing a communication link with fusion center in terms of: instant and accurate detection of collapse holes, acceptable error rate, time to re-attain network state, rapid distress signal propagation and low deployment cost. This platform is deployed in four different environments of anechoic chamber, hallway, outdoor and underground mine environment, to test the aforementioned scenarios using DASH7-compatible Bitsense Sensor Motes operating at 433 MHz. The effectiveness of the proposed approach has been experimentally validated for the single and multiple adjacent and disjoint node failures in all the four environments.

The number of monitoring systems was implemented for safety assurance in high stress environments before, but the novelty of our platform is long range, cost effectiveness, quick response to any structural change in the network, rapid and accurate data delivery using WSN operated on DASH7 protocol stack.