In this study, the authors employed an Interpretative phenomenological analysis (IPA) using “Planned Behavior Theory” to comprehensively assess Dhaka's current waste management issues and propose policy directions for improvement. The study conducted 25 expert interviews with policymakers and stakeholders and followed a thematic analysis using NVivo12 and the Granheim approach. Our findings revealed five main challenges the municipal corporation authority faces in effectively managing household waste: ineffective legislation, lack of cooperation, overpopulation, financial constraints, and community behavior. These challenges hinder the proper operation and handling of household waste, exacerbating the waste management crisis in Dhaka. Based on the findings, the study recommends strengthening legislation and regulations related to waste management, improving cooperation among stakeholders, addressing overpopulation through urban planning and population control measures, allocating sufficient financial resources for waste management infrastructure and facilities, and promoting community engagement and behavioral change towards responsible waste disposal practices. This study contributes to the academic literature on waste management by providing insights into the challenges and potential solutions in the context of a rapidly growing megacity like Dhaka. The findings and policy recommendations may guide policymakers, urban planners, and other stakeholders in developing effective strategies for sustainable waste management in Dhaka.

Supply chain price variability, also known as the "Bullwhip effect in Pricing (BP)," refers to the absorption or amplification of the variability of prices from one stage to another in a supply chain. This article derives analytical conditions that result in BP considering a buyback contract and conducts numerical simulations to gain further insights. For this, a joint price and replenishment setting newsvendor model with a wholesale-Stackelberg game is considered. Two demand types (linear and isoelastic) are analyzed along with uniformly and normally distributed additive and multiplicative uncertainties. The outcome of this research reveals that the main influential factors that affect BP are the structure and error type of the demand functions. Absorption (amplification) in price fluctuations occurs for linear (isoelastic) demand cases. Moreover, the price variances and BP ratios differ under the buyback and wholesale-price-only cases. The overall results help understand the fluctuation of market prices under various conditions.

To reduce carbon emissions and enable sustainable development goals, electric vehicles need to be promoted. As an evolving area of study, significant research gaps exist in the electric vehicle supply chain literature, especially in terms of competition, game structures, decision variables, green investment, etc. This paper considers two competing electric vehicle supply chains with one manufacturer and one retailer under each supply chain. The supply chain members are profit-maximizing in nature. Here, the manufacturers decide on the wholesale price and green level of the product, whereas the retailers or distributors decide on the retail selling price. The market demand is price-sensitive and also positively related to consumers’ green awareness and brand reputation of green products. Nevertheless, the green level comes with a price in the form of green investment for the manufacturer. Therefore, the manufacturer makes a tradeoff between the increased revenue due to greener electric vehicle technologies and the associated green investments. This paper derived the optimal decisions for the supply chain following three game-theoretical approaches. Three game scenarios represent the dynamic power structures of the market. The overall results are presented via four propositions and twelve corollaries, along with proofs. Furthermore, numerical simulations have been conducted to gain further insights into the sensitivities of the optimal actions with respect to self-price, competitor price, green level, and green investment. The overall study may assist electric vehicle businesses in having a better understanding of optimal pricing and green investment strategies under a variety of market scenarios.

Background: Additive manufacturing (AM) applications in producing spare parts are increasing day by day. AM is bridging the digital and physical world as a 3D computer-aided manufacturing (CAM) method. The usage of AM has made the supply chain of the aviation spare parts industry simpler, more effective, and efficient. Methods: This paper demonstrates the impacts of AM on the supply chain of the aircraft spare parts industry following a systematic literature review. Hence, centralized and decentralized structures of AM supply chains have been evaluated. Additionally, the attention has been oriented towards the supply chain with AM technologies and industry 4.0, which can support maintenance tasks and the production of spare parts in the aerospace industry. Results: This review article summarizes the interconnection of the industry findings on spare parts. It evaluates the potentiality and capability of AM in conceptualizing the overall supply chain. Moreover, MROs can adopt the proposed framework technologies to assist decision-makers in deciding whether the logistics hub with AM facilities is centralized or decentralized. Conclusions: Finally, this review provides an overall view to make critical decisions on the supply chain design of spare parts driven by new and disruptive technologies of industry 4.0. The next-generation supply chain may replace the logistics barriers by reducing waste and improving capability and sustainability by implementing AM technologies.

COVID-19 induced lockdowns have made it extremely difficult for the poorer section of the community to arrange sustenance items like food and medicines due to the lack of cash flow coupled with the closure of markets. Although multiple organisations took initiatives to help this vulnerable cohort by distributing essential items, they ended up either oversupplying or undersupplying at different regions due to the lack of a reference framework. Hence, the study develops a model to aid the national relief distribution process during a pandemic. This study considers the capacitated plant location model and applies the linear programming tool to formulate and solve the model. The model assigns a target and service zones, to relief organisations based on their capacity and proximity and avoid redundant relief goods to easily accessible areas. The model can be used by government, private, and non-profit alike to distribute relief during any similar future events as well.

PurposeGlobally, a myriad of floating workers is in grave jeopardy due to the ceasing of employment opportunities that resulted from the mobility restriction during the Covid-19. Despite the global concern, developing countries have been suffering disproportionately due to the dominance of informal workers in their labour market, posing the necessity to campaign for the immediate protection of this vulnerable population. This paper analyses various dimensions of the vulnerability of urban floating workers in the context of Covid-19 in Bangladesh. In reference to International Labour Organization's (ILO) “Decent Work” concept, this paper endeavours to examine floating workers' vulnerability using the insider-outsider framework in context to Covid-19 pandemic.Design/methodology/approachThe study was conducted in two phases. In the first phase, data were collected before the pandemic to assess the vulnerability of the informal floating workers. Later, we extended the study to the second phase during the Covid-19 pandemic to understand how pandemic affects the lives and livelihood of floating workers. In phase one, data were collected from a sample of 342 floating workers and analysed based on job security, wages, working environment, psychological wellbeing and education to understand the vulnerability of floating workers. In phase two, 20 in-depth qualitative interviews were conducted, followed by thematic analysis to explore how the pandemic affects the existing vulnerability of floating workers.FindingsVarious social protection schemes were analysed to evaluate their effectiveness in reducing the vulnerability of floating workers facing socio-economic crises. The study has found that the pandemic has multiplied the existing vulnerability of the floating workers on many fronts that include job losses, food crisis, shelter insecurity, education, social, physical and mental wellbeing. In response to the pandemic, the Government stimulus packages and Non-government Covid-19 initiatives lack the appropriate system, magnitude, and focus on protecting the floating workers in Bangladesh.Practical implicationsThis paper outlines various short-term interventions and long-term policy prescriptions to safeguard floating workers' lives and livelihood from the ongoing Corona pandemic and unforeseen uncertainties.Originality/valueThis paper is the first of its kind that aims at understanding the vulnerability of this significant workforce in Bangladesh, taking the whole picture of Government and Non-government initiatives during Covid-19.

Background: Retail chains aim to maintain a competitive advantage by ensuring product availability and fulfilling customer demand on-time. However, inefficient scheduling and vehicle routing from the distribution center may cause delivery delays and, thus, stock-outs on the store shelves. Therefore, optimization of vehicle routing can play a vital role in fulfilling customer demand. Methods: In this research, a case study is formulated for a chain of retail stores in Dhaka City, Bangladesh. Orders from various stores are combined, grouped, and scheduled for Region-1 and Region-2 of Dhaka City. The 'vehicle routing add-on' feature of Google Sheets is used for scheduling and navigation. An android application, Intelligent Route Optimizer, is developed using the shortest path first algorithm based on the Dijkstra algorithm. The vehicle navigation scheme is programmed to change the direction according to the shortest possible path in the google map generated by the intelligent routing optimizer. Results: With the application, the improvement of optimization results is evident from the reductions of traveled distance (8.1% and 12.2%) and time (20.2% and 15.0%) in Region-1 and Region-2, respectively. Conclusions: A smartphone-based application is developed to improve the distribution plan. It can be utilized for an intelligent vehicle routing system to respond to real-time traffic; hence, the overall replenishment process will be improved.

Purpose Many research findings demonstrate the benefits of lean manufacturing implementation. However, the impact of lean manufacturing on organizational performance in developing countries like Bangladesh remains unexplored. The purpose of this paper is to investigate the impact of lean manufacturing system (LMS) on the organizational performance of the apparel industry in Bangladesh. Design/methodology/approach Empirical data were collected from 227 apparel manufacturing firms using a close-ended structured questionnaire. The causal relationships between the independent and dependent variables are examined by structural equation modeling using AMOS 20.0 software. Findings The results reveal significant evidence that the implementation of LMS has a direct impact on organizational performance in terms of operational and business performance. Practical implications The findings of this study will create a substantial interest among the practitioners of the apparel industry to implement LMS. This study will also explore the opportunities to develop lean implementation framework and identify the benefits that will enhance the competitive advantages. Originality/value This paper explores the causal relationships and argues based on the empirical data in the context of the apparel industry in Bangladesh.

Bullwhip effect in Pricing (BP) is the amplification or absorption of price fluctuation across the supply chain. This paper aims to derive conditions for BP under a revenue-sharing contract. For this purpose, we consider both a deterministic model and stochastic newsvendor models with additive and multiplicative uncertainties assuming both the linear and isoelastic demand forms. Contrasting the results with a no-contract case, this paper shows that the cost-pass-through and the BP ratio increase with the increase of the revenue-share percentage. Numeric simulations also showed different phases of price-variation for the linear and the isoelastic demand forms.

Bullwhip effect in pricing (BP) refers to the amplified variability of prices in a supply chain. This paper analyzes the occurrence of BP under three game scenarios (e.g. a simultaneous, a wholesale-leading, and a retail-leading) for three types of demand functions (e.g. a log-concave, an isoelastic, and a negative exponential). Cost pass-throughs and BP ratios are calculated analytically for an N-stage supply chain, and then the price fluctuations in various supply chain game structures are illustrated through simulations. The results indicate that in the case of optimal markup pricing games, the occurrence of BP depends on the demand functions. This study also shows that, BP occurs in varying magnitudes for different types of games. Finally, a relation between price variations and corresponding markup profits are also discussed.

A "reverse bullwhip effect in pricing (RBP)" occurs when an amplification of price variability takes place moving from the upstream suppliers to the downstream customers in a supply chain. In this study, we investigate RBP conditions for supply chains where joint replenishment and pricing decisions are made. Commencing with a single-stage supply chain in which a retailer faces a random and price-sensitive demand, we extend the results to a multi-stage supply chain using a leader-follower game theoretical framework. We discuss RBP conditions for supply chains where newsvendor and continuous review inventory policies are employed, and present numerical examples for commonly used demand functions.

Analytic Hierarchy Process (AHP) has been widely used in varieties of decision making processes among several alternatives, where data on pair-wise comparisons are aggregated and the degree of importance of each alternative is quantified. The process of assigning importance or priorities against the alternatives has inherent limitations, which lead to higher possibility of inconsistency. This paper focuses on two basic limitations of the AHP, first one is its inconsistency generated from huge comparisons in judgment matrix and the second one is the use of ‘ranking weightages’ given by AHP. To eliminate these limitations, this research paper recommends to calculate relative importance among alternatives from the ratings assigned from “Likert scale” to form a “suggestion matrix” with zero percent CR before judgment matrix which gives privilege to decision makers to change relative importance within the range of CR. This process intensifies the effectiveness of AHP by reducing time consumption through optimizing inconsistency.

Biomass transportation suffers from higher transportation costs and insufficient competition in terms of supply chain providers. The transportation network optimization is somewhat absent in biomass transportation and hence this sector is still subsidized in many states. Biomass transportation industry can be benefitted from utilizing intermodal facilities and various transportation modes. Availability of several modes introduces flexibility of routing and intermodal facility enables using several modes throughout the routes. Transportation network consisting intermodal facility connected with various modes along with various shipment options (direct shipment, transshipment, cross docking, consolidation etc.) among different stages of biomass transportation offer more alternative choices for consideration. Some of the alternative choices induce less cost with prolonged service time, some choices provide expedite delivery with higher cost, some choices maintain similar service level and on time delivery at a reasonable or moderate cost. Proper analysis of various routing options should be done in order to choose the most efficient one. In most of the cases, minimization of time leads toward a costly transportation, on the other hand cost saving routes requires more time to ship the order. This research endeavors to develop a methodology so that both of the objectives (i.e. minimization of transportation time and minimization of transportation cost) can be satisfied together to find out an optimal transportation solution. This paper considers the supply chain of cellulosic bio-diesel. This supply chain consists of harvesters, hub or storage facilities, preprocessing facilities and bio-refinery plant. Several modes of transportations and intermodal facilities are available in the supply chain. There are direct shipment and transshipment options too. This paper tried to develop a generic mathematical model to find out the optimal solution for both minimization of transportation cost and time. In this paper, excel solver is used to calculate the numbers of unit loads to be transported through various routes. Finally, it compares three solutions, out of which, one gives the minimum cost, another gives the minimum time and last one is the modification of second solution that costs less than the second one. The modification is done by choosing alternative cost saving routes without affecting the critical time. The recommended solution considers the trade-off between time and cost. It is expected, that this research will assist the decision maker of biomass industry to make smart choice in route and mode selection process.