Syed Mithun Ali

A heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm

The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.

Coordinating a supply chain system for production, pricing and service strategies with disruptions

This paper investigates the coordination problem of a supply chain system composed of one supplier and one retailer. To coordinate, we apply revenue sharing contracts in the context of supply chain disruptions management. Herein, we consider disruptions at two factors namely demand and service sensitivity coefficient and propose a responsive pricing, service level, production and contract decisions model. Our results reveal that the proposed coordination mechanisms could lead to the supply chain system of interest achieving around 80% to 90% efficiency while satisfying win-win positions of the partners. In addition, this work illustrates that the coordinated supply chain produces more profit to the retailer. Our findings also indicate the original contracts for the non-disrupted supply chain system show some level of robustness to the scenarios that show a small increase of the market scale and service sensitivity coefficient. More specifically, the original contracts work fine as long as the increment of markets scale is less than 30% of the market base. However, for most of the cases, the production, pricing, service strategies, and contracts policies need to be adjusted to tackle the disruptions. We show the usefulness of our work by providing some numerical examples.

Development of a CNC interpolation scheme for CNC controller based on Runge-Kutta method

The parametric interpolators of modern CNC machines use Taylors series approximation to generate successive parameter values for the calculation of x, y, z coordinates of tool positions. In order to achieve greater accuracy, higher order derivatives are required at every sampling period which complicates the calculation for contours represented by NURBS curve. In addition, this method calculates the chordal error in a given segment through estimation of the curvature neglecting a fraction of the error. In order to avoid calculating higher derivatives and make the calculations simpler, this paper proposes the classical fourth-order Runge-Kutta (RK) method for the determination of successive tool positions requiring the calculation of the first derivatives only. Furthermore, a method of estimating the chordal error on the average value of parameters at the end points of a given curve segment is proposed here that does not require the calculation of curvature at every segment. Finally, a variable feedrate interpolation scheme is designed combining the RK method of parameter calculation and the proposed method of chordal error calculation. Results show that reduced chordal error and feedrate fluctuations are achievable with the proposed interpolator compared to the conventional interpolator based on Taylors approximation with higher order terms.

Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh

Abstract Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.

An application of Pareto analysis and cause-and-effect diagram (CED) to examine stoppage losses: a textile case from Bangladesh

Abstract Spinning industries are facing challenges of improving productivity in the competitive market nowadays. Ring spinning, the most widely used yarn manufacturing process for short staple spinning, uses several types of machinery from blow room to ring frame for producing yarns from cotton fibers. An enterprise can improve utilization of resources by identifying unwanted machine stoppage and taking corrective actions at different points in the production cycle. This study focuses on the major six stoppage losses that are used to calculate Overall Equipment Efficiency (OEE) of ring frame section. The Pareto analysis reveals that idling and minor stoppage and breakdown losses are responsible 89.3% of total stoppage losses. According to cause-and-effect analysis, root causes for the stoppage losses are: high doffing time, high traveler changing time, broken end of yarn due to piles generation through the front roller, power failure and change in Draft Change Pinion (DCP) due to breakage of teeth of the gear during starting of machine by operators before lowering of ring rail and change of Twist Change Pinion (TCP) due to the displacement of TCP gear shaft. Finally, few recommendations are made to reduce stoppage losses and to increase the productivity of the ring frame section.

Optimal ordering policies in a multi-sourcing supply chain with supply and demand disruptions-a CVaR approach

In this study, we propose a conditional value at risk (CVaR) model for supply chain disruptions planning of a multi-agent, multi-product supply chain subject to supply and demand disruptions. Our focus is on building and comparing ordering policies under CVaR and expected cost criteria. The proposed formulation is illustrated through some numerical instances. The results present that the CVaR model shows a considerable difference in response policies compared to the expected cost model. Ordering quantities in response to supply and demand disruption are lower in the CVaR model than the expected cost model. In many instances, it is also seen that ordering quantities in response to disruptions tend to become lower when a decision maker becomes more risk-averse. It is expected that the proposed CVaR model would outperform to optimise the supply chain of an organisation, in particular, for the purpose of reducing the risk of high cost.

Investigation on workplace environment and safety - a case study in Rahimafrooz Batteries Ltd.

The importance of workplace environment and safety are getting increasing attention among researchers for decades. Each company needs to develop safety programs, procedures, policies and plans for their specific workplace. This can include a wide range of ideas, depending on what type of workplace environment and safety issues are of concern for the organisation. Workplace hazards and safety concerns need to be identified along with ways to handle them effectively to ensure a safe workplace and thus maintaining productivity of the organisation. Workplace environment and safety directly or indirectly influence the quality of products and productivity of an organisation to a great extent. This paper addresses some issues on workplace environment and safety in a battery manufacturing company named Rahimafrooz Batteries Ltd. (RBL) and some recommendations have been suggested to handle those issues in an efficient and effective manner.

A quantitative model for disruptions mitigation in a supply chain considering random capacities and disruptions at supplier and retailer

Abstract Effective response and recovery from disruptions are vital to achieving the supply chain objectives. This study aims to formulate a quantitative model for mitigating disruptions in a supply chain. An inventory model has been developed for a manufacturer with one supplier and one retailer by considering random capacity of the supplier and random availability of both the supplier and the retailer assuming zero delivery lead time. Backorders are allowed and it has two parts – unit dependent and both unit and time-dependent. This study suggests an optimal order quantity and a reordering point so that the average cost per cycle gets minimized. A genetic algorithm is used to solve the proposed inventory model. The applicability of the proposed model has been tested using a numerical example. Finally, sensitivity analysis is performed to examine the robustness of the model.

Decision modeling for evaluating risks in pharmaceutical supply chains

Managing risks is becoming a highly focused activity in the health service sector. In particular, due to the complex nature of processes in the pharmaceutical industry, several risks have been associated to its supply chains. The purpose of this paper is to identify and analyze the risks occurring in the supply chains of the pharmaceutical industry and propose a decision model, based on the Analytical Hierarchy Process (AHP) method, for evaluating risks in pharmaceutical supply chains (PSCs).,The proposed model was developed based on the Delphi method and AHP techniques. The Delphi method helped to select the relevant risks associated to PSCs. A total of 16 sub risks within four main risks were identified through an extensive review of the literature and by conducting a further investigation with experts from five pharmaceutical companies in Bangladesh. AHP contributed to the analysis of the risks and determination of their priorities.,The results of the study indicated that supply-related risks such as fluctuation in imports arrival, lack of information sharing, key supplier failure and non-availability of materials should be prioritized over operational, financial and demand-related risks.,This work is one of the initial contributions in the literature that focused on identifying and evaluating PSC risks in the context of Bangladesh. This research work can assist practitioners and industrial managers in the pharmaceutical industry in taking proactive action to minimize its supply chain risks. To the end, the authors performed a sensitivity analysis test, which gives an understanding of the stability of ranking of risks.