Browsing by Author "Ekren, Banu Y."

Now showing 1 - 13 of 13
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    Additive manufacturing in pharmaceutical supply chain
    (Logistics Research Network and CILT, 2023-09-08) Li, Wenqi; Ekren, Banu Y.; Aktas, Emel
    Purpose: A resilient and efficient pharmaceutical supply chain (PSC) ensures access to essential medicines during pandemics and other emergencies. The COVID-19 pandemic has highlighted the need for continued investment and innovation in this area, and concerted efforts by all stakeholders are necessary to achieve this goal. Additive manufacturing (AM), or 3D printing, can enhance PSC resilience and performance, reduce waste, and improve environmental sustainability. 3D printing can help address drug shortages, patient-specific dosages, and personalised medicine in the pharmaceutical industry. Moreover, 3D printing technology enables local production of drugs and medical devices, reducing transportation costs, carbon footprint, and lead times, transforming how products are designed, produced, and delivered to end-users. This study aims to investigate the multifaceted benefits of 3D printing technology on the PSC, including its potential to streamline processes, increase SC efficiency, enhance responsiveness, and improve sustainability. Additionally, the study seeks to identify the interrelationships between these benefits and how they can contribute to the overall success of the PSC. Research Approach: To achieve this, we comprehensively analyse the potential benefits and shortcomings of 3D printing technology on the PSC by compiling relevant literature and internet sources. Findings and Originality: The study identifies ways in which 3D printing can positively impact the PSC, including simplifying the supply chain (SC) process, localising production, and transitioning from make-to-stock to make-to-order production. These changes can significantly impact inventory levels, increasing SC sustainability, efficiency, responsiveness, and resilience. However, this study also identifies unique shortcomings and future research opportunities associated with implementing 3D printing in the PSC, providing a holistic view of the technology's potential impact. Research Impact: The research highlights the potential of 3D printing to revolutionise the PSC by enabling a more streamlined and sustainable manufacturing process. Practical Impact: The study's findings offer the pharmaceutical industry insights on how to tackle SC shortcomings such as supplier shortages, fluctuating demand, and short response times. As a result, this study offers a valuable resource for both practitioners and researchers who wish to leverage 3D printing technology to enhance the PSC's performance and understand the technology's impact on the PSC.
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    Cost and performance comparison of tier-captive SBS/RS with a novel AVS/RS/ML
    (Taylor and Francis, 2023-04-18) Ekren, Banu Y.; Lerher, Tone; Küçükyaşar, Melis; Jerman, Boris
    This paper introduces a novel autonomous vehicle-based storage and retrieval system that utilizes movable lifts (AVS/RS/ML), proposed as an alternative to the tier-captive shuttle-based storage and retrieval system (SBS/RS). The newly proposed system aims to provide an affordable solution with highly utilised AGVs, that can also perform operations out of warehouse. The performance of this novel system is compared with the equivalent tier-captive SBS/RS warehouse design, where each shuttle is dedicated in a specific tier in that design. The comparison is based on the initial system investments costs, throughput rates, and average utilisation of lifts/MLs in the system. Collision prevention rules are also applied to AVS/RS/ML, and its performance is tested through simulation. The results show that the tier-captive SBS/SR system becomes cost-efficient under high throughput rate requirements, while the AVS/RS/ML technology is preferred for relatively moderate and low process rate requirements. The unit-cost per month performance metric of AVS/RS/ML is less sensitive to an increase in number of tiers in the system, compared to the tier-captive SBS/RS case, indicating that AVS/RS/ML may be promising for high-tier warehouse system designs.
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    Digital twins for decision making in supply chains
    (Springer, 2023-02-06) Kulac, Oray; Ekren, Banu Y.; Toy, A. Ozgur
    This paper studies the utilization of digital twins (DTs) as a decision support tool in supply chains (SCs) by providing a framework. DT is an emerging technology-based modeling approach reflecting a virtual representation of an object or system that can help organizations monitor operations, perform predictive analytics, and improve their processes. For instance, it may provide a digital replica of operations in a factory, communications network, or the flow of goods through an SC system. In this paper, by focusing on SC systems, we explore the critical decisions in SCs and their related data to track, to make the right decisions within DTs. We introduce six main functions in SCs and define frequent decisions that can be taken under those functions. After defining the required decisions, we also identify which data/information would help to make correct decisions within those DTs.
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    E-grocery challenges and a solution approach from multi-objective perspectives
    (Springer, 2022-02-06) Foresti, Laura; Perotti, Sara; Ekren, Banu Y.; Prataviera, Lorenzo Bruno
    This paper provides an overview of the complex structure of the e-grocery industry, highlighting recent trends and challenges including the increasing customers’ expectations. Customers’ satisfaction can be driven by multiple objectives, which can create significant trade-offs. We propose a new approach as a future work for e-grocery businesses to leverage multi-objective perspectives, maximizing product availability and sustainability and minimizing cost. Specifically, we propose an e-grocery store assignment policy while consumers are using apps, which is developed on a real-time data-driven approach from customer ordering behaviors. With the help of data availability and data analytic tools, data-based solutions can foster continuous improvement in businesses. In a simulation study, imitating different demand profiles and online ordering behaviors might help develop a good solution approach for a multi-objective perspective.
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    Enhancing e-grocery order fulfillment: improving product availability, cost, and emissions in last-mile delivery
    (Springer, 2024-01-30) Ekren, Banu Y.; Perotti, Sara; Foresti, Laura; Prataviera, Lorenzo Bruno
    This paper studies e-grocery order fulfillment policies by leveraging both customer and e-grocery-based data. Through the utilization of historical purchase data, product popularity trends, and delivery patterns, allocation strategies are informed to optimize performance metrics such as fill rate, carbon emissions, and cost per order. The study aims to conduct a sensitivity analysis to identify key drivers influencing these performance metrics. The results highlight that fulfillment policies optimized with the utilization of the mentioned data metrics demonstrate superior performance compared to policies not informed by data. These findings underscore the critical role of integrating data-driven models in e-grocery order fulfillment. Based on the outcomes, a grocery allocation policy, considering both proximity and product availability, emerges as promising for simultaneous improvements in several performance metrics. The study recommends that e-grocery companies leverage customer data to design and optimize delivery-oriented policies and strategies. To ensure adaptability to new trends or changes in delivery patterns, continual evaluation and improvement of e-grocery fulfillment policies are emphasized.
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    Identifying the drivers of circular food packaging: a comprehensive review for the current state of the food supply chain to be sustainable and circular
    (MDPI, 2023-07-28) Ada, Erhan; Kazancoglu, Yigit; Lafcı, Çisem; Ekren, Banu Y.; Çimitay Çelik, Cansu
    The resilience of food systems is jeopardized by using food packaging materials that have adverse impacts on the environment, food quality, food safety, shelf-life, food loss, and waste. Therefore, a transition into a more sustainable system can only be possible by adopting circular economy principles and practices that can facilitate the elimination of unsustainable packaging, irresponsible disposal behaviors, and waste management. This paper mainly focuses on circular packaging practices in the existing literature to reveal the drivers of circular food packaging applications. The study also displays the triple combinations of material-sector, material-CE, and sector-CE principles. As a methodology, a systematic literature review (SLR) has been used for this study. Furthermore, this study investigates the literature findings, such as the most frequently mentioned food sector and sub-sector, CE principles, materials adopted for food packaging, and so on. The primary contribution of this study to the body of literature is the synthesis and mapping of the literature as a whole from the perspectives of CE principles, both sector-based and national, and the materials used through circular food packaging, and the attempt to facilitate this transition into a more circular system by outlining the drivers of circular food packaging.
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    Intelligent supply chains through implementation of digital twins
    (Springer, 2022-07-05) Kulaç, Oray; Ekren, Banu Y.; Toy, Özgür
    Data-driven decision-making process can be defined to be the sequential activities of real-time data collection, data analytics, optimization and decision making. Developments in Industry 4.0 technologies have made it possible to realize that new quality decision-making process. When that decision-making process is performed under the simulation model of a system developed on real-time data-based and end-to-end connection manner, to prevent the disruption risks and to improve resilience in a system, then it constitutes a digital twin (DT). A DT is a virtual representation of an object or system that can help organizations monitor operations, perform predictive analytics, and improve processes. For instance, a DT could provide a digital replica of the operations of a factory, communications network, or the flow of goods through a supply chain system. In this work, we focus on DT implementations in supply chain networks. We present state of the art implementation of DTs in supply chains and their prospective utilizations towards creating intelligent supply chains.
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    Micro-fulfilment centres in E-grocery deliveries
    (Springer, 2023-02-06) Ventola, Alessandro; Tinor, Mirko; Perotti, Sara; Ekren, Banu Y.; Reefke, Hendrik
    This paper studies micro-fulfilment centres (MFCs) as a response to rising e-grocery sales and customer expectations from decreased delivery time and cost requests. MFC is a business solution that allows orders to be picked and packed in a hyper-local facility. The study’s aim is to provide an overview of this subject from two research questions: i) how MFCs affect the last-mile delivery challenges? and ii) what design decisions are critical in building MFCs? While we evaluate the advantages and disadvantages of centralised versus decentralised warehousing strategies in the first question, we discuss the critical decisions in designing MFCs in the second question. In that, we discuss location and technology selection decisions as well as other warehousing design criteria. Further, this study provides future research directions at the end of this study.
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    A multi-objective integrated optimisation model for facility location and order allocation problem in a two-level supply chain network
    (Springer, 2022-04-15) Amin-Tahmasbi, Hamzeh; Sadafi, Sina; Ekren, Banu Y.; Kumar, Vikas
    This study proposes a mixed-integer multi-objective integrated mathematical model solving facility location and order allocation optimisation problems simultaneously in a two-echelon supply chain network. The proposed problem is motivated by a factoryless concept and by providing a dynamic decision-making solution under a multi-period time horizon. Within the model, we also determine the optimal replenishment number of production facilities by the multi-objective functions. The multi-objective functions include minimisation of the total cost, rejected and late delivery units and, maximisation of the assessment score of the selected suppliers. The studied dynamic decision model is significant for the cost-efficient management of companies’ supply chain networks. The mixed-integer mathematical model is developed by the LP-metric method and it is solved by the GAMS optimisation software. Due to the NP-hard structure of the problem, for large-scale instances, we utilise the Multi-Objective Particle Swarm Optimisation (MOPSO) and Multi-Objective Vibration Damping Optimisation (MOVDO) heuristic solution approaches. Numerical results show that, for large-scale problems, the MOPSO method performs better in Pareto solutions and decreases run times. However, the MOVDO method performs better regarding the Mean Ideal Distance and the Number of Solutions Cover surface criterion. The developed solution approach by this paper is a generic model which can be applied for any two-level network for simultaneous optimisation of supplier selection, location determination of facilities and their replenishment amounts.
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    An overview for food loss and waste reduction in food supply chains
    (Emerald, 2022-03-28) Ekren, Banu Y.; Kumar, Vikas
    Creating systems designs by considering a balance between economic, environmental and social effects is significant in today’s sustainability concern. Sustainability has become an emerging topic, growing concerns and attention for companies. Especially companies operating on a global scale aim to develop strategies for sustainable supply chain management. When the supply chain is a food chain, that concern increases exponentially and sustainable management of the chain becomes a critical challenge requiring the development and implementation of innovative practices by all stages within value chains. According to the Food and Agriculture Organization (FAO) one-third of all food produced in the world is lost or wasted from farm to fork. This waste has great negative impact on global economy, food availability, as well as environment. In this work, we focus on food loss and waste subject by searching how they could be reduced in throughout a supply chain network. We provide solution ways for the food waste/loss reduction obtained from current works in literature.
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    A reinforcement learning approach for transaction scheduling in a shuttle-based storage and retrieval system
    (Wiley, 2022-03-29) Ekren, Banu Y.; Arslan, Bartu
    With recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle-based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini-loads. With the recent increase in e-commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh-speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e., Q-learning) approach on a newly proposed tier-to-tier SBS/RS design, redesigned from a traditional tier-captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML-based algorithm for transaction selection in that system. The ML-based solution is compared with traditional scheduling approaches: first-in-first-out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, the Q-learning approach performs better than the two static scheduling approaches.
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    Shuttle-based storage and retrieval systems designs from multi-objective perspectives: total investment cost, throughput rate and sustainability
    (MDPI, 2022-12-31) Ekren, Banu Y.; Kaya, Berk; Küçükyaşar, Melis
    This paper studies performance comparison of two shuttle-based storage and retrieval system (SBS/RS) configurations developed on flexible or non-flexible travel policies of shuttles in the system. In the non-flexible SBS/RS, a shuttle is dedicated to a tier so that it cannot travel out of its dedicated aisle and tier. A lifting mechanism is installed in each aisle to provide vertical travel for loads. In flexible SBS/RS, shuttles can travel between tiers by a separate lifting mechanism installed on the other edge point of each aisle. The advantage of that flexible design is that there might be decreased number of shuttles settling in the system compared to the non-flexible design. We simulate the two system configurations and conduct an experimental design for the comparison purpose. Based on the three-performance metrics: total investment cost, throughput rate and energy consumption per transaction, the results show that mainly the flexible system provides better results which might be considered as future system investment for SBS/RS.
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    Transforming challenges into opportunities for Qatar’s food industry: self-sufficiency, sustainability, and global food trade diversification
    (MDPI, 2023-03-25) Al-Abdelmalek, Noora; Kucukvar, Murat; Onat, Nuri C.; Fares, Enas; Ayad, Hiba; Bulak, Muhammet Enis; Ekren, Banu Y.; Kazancoglu, Yigit; Ertogral, Kadir
    Food trade restrictions pose a serious risk for countries that are heavily reliant on food imports, potentially leading to food crises, inequality, and geopolitical conflicts on a global scale. However, such restrictions may also have transformative effects in promoting food supply chain resilience, security, and self-sufficiency. In this study, a novel econometric analysis is presented, utilizing a data-driven analytical model to investigate the impact of a food embargo on the industry, using Qatar as a case study. A structured and automated food trade database is created using Microsoft Management Server Studio and data visualization software is integrated for automated data discovery. By using a global, trade-based sustainability assessment model, which combines the multi-region input-output (MRIO) analysis with transportation mode-based (sea, road, and air) emissions, the carbon footprint of the dairy food production sector could be estimated. The study shows that the trade embargo on Qatar’s food industry can lead to significant reductions in the annual import of food products, promoting self-sufficiency, and reducing the net carbon emissions of the dairy food sector by nearly 40%. This reduction is not only achieved through food supply chain changes, such as transportation modes, but also by restrictions pushing the country to increase domestic production. Overall, the study demonstrates that a trade embargo, with the support of a well-designed national food security strategy, trade/import diversification, and the use of different modes of transportation for food products, can improve the resilience of global supply chains, self-sufficiency, and environmental sustainability.