Showing 225 results for Ai
Tenaw Tegbar Tsega, Thoben Klaus-Dieter, Rao D.k.nageswara, Bereket Haile Woldegiorgis,
Volume 35, Issue 2 (6-2024)
Abstract
Ethiopia has made enormous efforts in the leather industry to gain manufacturing capabilities that can be scaled up to other sectors. Those efforts have resulted in the industry shifting its role from raw material supplier to producer of value-added products for the global supply chain (GSC). However, the industry has faced severe challenges in generating the expected revenue, utilizing capacity, and finally coping with the global competitive environment. Studies reveal that manufacturing firms tackle similar challenges by improving their supply chain performance (SCP). The challenges that appeared in the leather industry of Ethiopia could also be solved by improving its SCP. Nonetheless, there is a lack of study on the basic characteristics and SCP of the industry after it has shifted its role. The main objective of this study is, therefore, to measure the SCP to know where it stands using a bench mark and identify the elements that contribute considerably to the low overall SCP in order to lay the foundation for subsequent improvement. To achieve the research objective, data was collected from primary and secondary sources through a questionnaire, survey, observation, and focus group discussion. The data is analyzed using the supply chain operations reference model (SCOR version 12.0). Accordingly, the overall SCP is found to be 67.33%, suggesting an average rating as per the set benchmark. The source process is identified as the most influential element for the overall low SCP, with a percentage gap of 17.23%. Taking corrective action on the identified elements could help the industry overcome the existing challenges by improving its SCP.
Mariam Atwani, Mustapha Hlyal , Jamila El Alami ,
Volume 35, Issue 2 (6-2024)
Abstract
In today's dynamic and competitive manufacturing landscape, accurate demand forecasting is paramount for optimizing production processes, reducing inventory costs, and meeting customer demands efficiently. With the advent of Artificial Intelligence (AI), there has been a significant evolution in demand forecasting methods, enabling manufacturers to enhance the accuracy of the forecasts.
This systematic literature review aims to provide a comprehensive overview of the state-of-the-art on demand forecasting models in the manufacturing sector, whether AI-based models or hybrid methods merging both the AI technology and classical demand forecasting methods. The review begins by establishing an overview on demand forecasting methods, it then outlines the systematic methodology used for the literature search.
The review encompasses a wide range of scholarly articles published up to September 2023. A rigorous screening process is applied to select relevant studies. Accordingly, a thorough analysis in the basis of the forecasting methods adopted and data used have been carried out. By synthesizing the existing knowledge, this review contributes to the ongoing advancement of demand forecasting practices in the manufacturing sector providing researchers and practitioners an overview on the advancements on the use of AI models to improve the accuracy of demand forecasting models.
Amin Amini, Alireza Alinezhad, Davood Gharakhani,
Volume 35, Issue 2 (6-2024)
Abstract
The selection of a sustainable supplier is a multi-criteria decision-making issue that covers a range of criteria (quantitative-qualitative). Selecting the most eco-friendly suppliers requires balancing tangible and intangible elements that may be out of sync. The problem gets more complicated when volume discounts are taken into account, as the buyer needs to decide between two issues: 1) What are the best sustainable suppliers? 2) Which amount needs to be bought from each of the selected eco-friendly suppliers? In current study a combined attitude of best-worst method (BWM) ameliorated via multi-objective mixed integer programming (MOMIP) and rough sets theory is developed. The aim of this work is to contemporaneously ascertain the order quantity allocated to these suppliers in the case of multiple sourcing, multiple products with multiple criteria and with capacity constraints of suppliers and the number of suppliers to employ. In this situation, price reductions are offered by suppliers based on add up commerce volume, not on the amount or assortment of items acquired from them. Finally, a solution approach is proposed to solve the multi-objective model, and the model is demonstrated using a case study in Iran Khodro Company (IKCO). The results indicate that ISACO is the most sustainable supplier and the most orders are assigned to this supplier.
Fakhri Ikhwanul Alifin, Bermawi Priyatna Iskandar, Nadia Fasa, Fransisca Debora,
Volume 35, Issue 2 (6-2024)
Abstract
This study develops warranty cost models for repairable products subject to Lemon Laws, encompassing Critical and Non-Critical components forming a multi-component system. Failures can arise naturally or be induced by other components (i.e., failure interaction), defining a lemon if recurrent failures reach a threshold (k) during the warranty period. A lemon declaration triggers a refund or replacement by the manufacturer. Four warranty cost models are proposed from the manufacturer's standpoint, considering failure mechanisms. Increasing failure thresholds in the warranty scheme substantially decreases warranty cost rates. For instance, a threshold (k) of 5 in refund and replacement schemes yields the lowest cost rates of 33.7159 and 25.8249, respectively. Failure interactions escalate total warranty costs; for instance, in a refund scheme (k = 5), costs with failure interaction reach 31.0169 compared to 28.7603 without. Similar trends apply to replacement schemes. Moreover, a lower warranty cost rate will extend the period, indicating regulation fulfillment due to a closer warranty period to the Lemon period. Sensitivity analysis also underscores the role of higher reliability in reducing warranty costs and complying with Lemon Laws. Finally, maintenance strategies and product reliability are emphasized to fulfill Lemon Laws with minimal costs, i.e., fewer warranty claims.
Theodore Alvin Hartanto, Seng Hansun,
Volume 35, Issue 3 (9-2024)
Abstract
One method to diagnose retinal diseases is by using the Optical Coherence Tomography (OCT) scans. Annually, it is estimated that around 30 million OCT scans are performed worldwide. However, the process of analyzing and diagnosing OCT scan results by an ophthalmologist requires a long time so machine learning, especially deep learning, can be utilized to shorten the diagnosis process and speed up the treatment process. In this study, several pre-trained deep learning models are compared, including EfficientNet-B0, ResNet-50V2, Inception-V3, and DenseNet-169. These models will be fine-tuned and trained with a dataset containing OCT scanned images to classify four retinal conditions, namely Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The models that have been trained are then tested to classify the test set and the results are evaluated using a confusion matrix in terms of accuracy, recall, precision, and F1-score. The results show that the model with the best classification results in the batch size of 32 scenario is the ResNet-50V2 model with an accuracy value of 98.24%, precision of 98.25%, recall of 98.24%, and F1-score of 98.24%. While for the batch size of 64, the EfficientNet-B0 model is the model with the best classification results with an accuracy value of 96.59%, precision of 96.84%, recall of 96.59%, and F1-score of 96.59%.
Yuvaraj M, Jothi Basu,
Volume 35, Issue 3 (9-2024)
Abstract
Refrigerated trucks in the cold chain enhance the shelf-life of food. In the fruit supply chain (FSC), if each different fruit necessitates its dedicated fleet of refrigerated vehicles, the total cost of the supply chain would increase. On the other hand, if there are several fruits in a single compartment, the quality and freshness of the fruits will be impacted since each fruit requires a different operating temperature. Therefore, partitions are necessary within the container. While the use of cold chain infrastructure will result in a reduction in food loss and an enhancement in food security, it will also incur an increase in the overall cost of the supply chain. Therefore, this paper aims to create a mixed integer non-linear programming (MINLP) mathematical model considering multi-compartment reefer trucks (MCRTs) to minimize the total cost in the FSC. To assess the efficiency of the model, a case study is carried out in India, and the formulated mathematical model is solved using a heuristic approach. The findings indicate that utilizing MCRTs leads to a reduction in the number of vehicles required and a drop in total supply chain cost. Three-compartment reefer trucks offer a more significant cost-saving advantage in the FSC compared to two-compartment reefer trucks. Furthermore, it is noted that operating three distribution centers (DCs) results in a reduction in the overall cost. The decrease in total supply chain costs enhances the affordability of fruits for low-income populations and contributes to the enhancement of food security. In addition to cost reduction, implementing MCRT has also beneficial environmental impacts such as decreased emissions due to a decrease in the number of trucks utilized and reduced food waste.
Khamiss Cheikh, El Mostapha Boudi, Hamza Mokhliss, Rabi Rabi,
Volume 35, Issue 3 (9-2024)
Abstract
Maintenance plan efficacy traditionally prioritizes long-term predicted maintenance cost rates, emphasizing performance-centric approaches. However, such criteria often neglect the fluctuation in maintenance costs over renewal cycles, posing challenges from a risk management perspective. This study challenges conventional solutions by integrating both performance and robustness considerations to offer more suitable maintenance options.
The study evaluates two representative maintenance approaches: a block replacement strategy and a periodic inspection and replacement strategy. It introduces novel metrics to assess these approaches, including long-term expected maintenance cost rate as a performance metric and variance of maintenance cost per renewal cycle as a robustness metric.
Mathematical models based on the homogeneous Gamma degradation process and probability theory are employed to quantify these strategies. Comparative analysis reveals that while higher-performing strategies may demonstrate cost efficiency over the long term, they also entail greater risk due to potential cost variability across renewal cycles.
The study underscores the necessity for a comprehensive evaluation that balances performance and resilience in maintenance decision-making. By leveraging the Monte Carlo Method, this research offers a critical appraisal of maintenance strategies, aiming to enhance decision-making frameworks with insights that integrate performance and robustness considerations.
Arifa Khan, Saravanan P,
Volume 35, Issue 3 (9-2024)
Abstract
Optimizing production in the plastic extrusion industry is a pivotal task for small scale industries. To enhance the efficiency in today’s competitive market being a small-scale manufacturer over their peers is challenging. With the limited resources, having constraints on manpower, capital, space, often facing fluctuations in demand and production, simultaneously maintaining high quality became very important for the success. Among the plethora of KPIS used in manufacturing, Overall Equipment Effectiveness (OEE) stands out as corner stone. In this study, we collected real-world data from a plastic extrusion company. i.e., an HDPE Pipe manufacturing company. It serves as the backdrop for our study, this is based on the plastic extrusion sector and set out a goal of enhancing OEE through a comparative investigation of various ML models. To forecast and estimate OEE values, we used various Machine Learning models and examine each algorithm’s performance using metrics like Mean Squared Error (MSE) and model comparisons using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), creating a comprehensive picture of each algorithm’s strength which enables the small businesses to make informed decisions and empowers them to stay agile and adapt to the changes in the manufacturing environment.
Zahrasadat Hasheminasab, Esmaeil Mazroui Nasrabadi, Zahra Sadeqi-Arani,
Volume 35, Issue 3 (9-2024)
Abstract
In today’s world, supply chains must adopt new and intelligent technologies to achieve objectives such as enhancing productivity and performance, competitiveness, and overcoming challenges. The Internet of Things (IoT), as an emerging and transformative technology, is considered one of the most significant technology areas today and has garnered considerable attention across various industries. However, the implementation of IoT at the supply chain (SC) level faces numerous challenges and obstacles, and its acceptance at this level requires specific drivers. To date, no specific classification has been provided for drivers at the SC level, and existing classifications for challenges also need to be reviewed and updated. Given the importance of IoT in SC management, a systematic review at this level is necessary. This article provides a systematic literature review to identify and classify the challenges and drivers of IoT at the SC level. The study reviewed articles published from 2004 to 2023, ultimately identifying and categorizing 92 challenges into 16 categories: financial, standards and government regulations, privacy and security, energy consumption, health issues, hardware and software issues, culture in the SC, lack of knowledge and awareness, poor IT management, coordination in the SC, perception, the Challenge of uncertainty, lack of Plan and Strategy, incompatibility with existing technology, supply Problems, and user acceptance and trust in technology. Additionally, the study identified 4 antecedent drivers (pressures, understanding the benefits, government regulations, government incentives) and 10 consequent drivers (production benefits, improving competitive advantage, inventory management, cost management, improving transparency, efficiency of information flow, development of responsiveness and agility, sustainable development, facilitation of management, and development of cooperation and coordination). Finally, a model for implementing IoT technology in the SC is presented. This model synthesizes the findings from the literature review and offers a practical roadmap for organizations seeking to leverage IoT in their supply chains. By addressing the identified challenges and utilizing the drivers, organizations can effectively integrate IoT technology, thereby enhancing the efficiency, transparency, and overall performance of their SC operations.
Pardis Roozkhosh, Amir Mohammad Fakoor Saghih,
Volume 35, Issue 3 (9-2024)
Abstract
The reliability of each component in a system plays a crucial role, as any malfunction can significantly reduce the system's overall lifespan. Optimizing the arrangement and sequence of heterogeneous components with varying lifespans is essential for enhancing system stability. This paper addresses the redundancy allocation problem (RAP) by determining the optimal number of components in each subsystem, considering their sequence, and optimizing multiple criteria such as reliability, cost uncertainty, and weight. A novel approach is introduced, incorporating a switching mechanism that accommodates both correct and defective switches. To assess reliability benefits, Markov chains are employed, while cost uncertainty is evaluated using the Monte-Carlo method with risk criteria such as percentile and mean-variance. The problem is solved using a modified genetic algorithm, and the proposed method is benchmarked against alternative approaches in similar scenarios. The results demonstrate a significant improvement in the Model Performance Index (MPI), with the best RAPMC solution under a mixed strategy achieving an MPI of 0.98625, indicating superior model efficiency compared to previous studies. Sensitivity analysis reveals that lower percentiles in the cost evaluations correlate with reduced objective function values and mean-variance, confirming the model's robustness in managing redundancy allocation to optimize reliability and control cost uncertainties effectively.
Rahma Fariza, Melinska Ayu Febrianti, Qurtubi Qurtubi, Hari Purnomo,
Volume 35, Issue 4 (12-2024)
Abstract
A business faces challenges in terms of product structuring, design, and space layout; it needs to adapt traditional design management models to scientific developments, like customer shopping behavior data. This article contains a systematic review of planograms and is essential because a similar complete literature review has yet to be found. Therefore, this research is necessary, especially for business actors such as retailers and suppliers. This research aims to analyze studies on shelf-space allocation and store layout and provide advice for future research. This study used the systematic review methodology to incorporate relevant literature, of which 50 articles were later obtained. The review protocol guides a comprehensive and systematic analysis of the articles. This study proposes potential avenues for future research to offer a thorough and precise examination of the impact of shelf-space allocation and store layout. The gaps in previous studies are opportunities to create more complex and comprehensive research results on similar topics. This article added scientific value by presenting an exhaustive literature review, and it can fill the theoretical gap by completing the previous literature review.
Dwi Kurniawan, Aghnia Nazhiifah Ulhaq, Aditya Fadhilah Althofian, Rubby Nur Rachman,
Volume 35, Issue 4 (12-2024)
Abstract
In industrial and commercial settings, inventory systems often involve managing multiple products with diverse demand patterns, making the direct application of the single-item newsvendor model inefficient. To address this complexity, this study proposes an adaptation of the newsvendor model through demand aggregation, where related items are grouped into a product family. By aggregating demand and financial parameters, the traditional newsvendor approach can be extended to multi-item systems, simplifying the inventory management process. This method was tested in two different case studies—a coffee roaster company and a meatball producer—demonstrating its validity and applicability. The aggregated newsvendor model was found to enhance inventory accuracy and efficiency, reducing random error and improving operational performance. This approach offers a valuable extension of the newsvendor model, with potential for broader application across various industries.
Parinaz Esmaeili, Morteza Rasti-Barzoki,
Volume 35, Issue 4 (12-2024)
Abstract
This paper examines the simultaneous decisions regarding advertising, pricing, and service to supply chain coordination involving one manufacturer and one retailer. Demand is impacted by these decisions, with service playing a crucial role in enhancing customer loyalty and boosting sales. The study employs three well-known game theory approaches—Nash, Stackelberg-Retailer, and Cooperative games—to analyze their effects on the supply chain. Optimal strategies for both the manufacturer and the retailer are identified within each approach, and the strategies' results are compared. Results show that the retailer manufacturer, and the entire system achieves higher profits through the Stackelberg-Retailer game compared to the Nash game, while the Cooperative game results in the highest overall profits. Finally, the Nash bargaining model is outlined and analyzed to assess opportunities for sharing profits.
Hana Catur Wahyuni, Rahmania Sri Untari, Rima Azzara, Marco Tieman, Diva Kurnianingtyas,
Volume 35, Issue 4 (12-2024)
Abstract
This research discusses the application of the Failure Mode and Effect Analysis (FMEA) method in designing a blockchain system for mitigating food safety and halal risks in the beef supply chain. The complexity of the meat supply chain involving various parties increasing the risk of contamination and changes in the halal status of the meat. This research aims to identify food safety and halal risks, prioritise the risks, and design blockchain-based mitigation solutions. Blockchain was chosen for its advantages in providing high transparency and accountability, enabling real-time tracking at every stage of the supply chain. The research results show that most of the risks in the meat supply chain fall into the low category, but there are some critical medium risks, especially related to the slaughtering process. The proposed blockchain design includes product traceability features, halal certification, temperature monitoring, and smart contracts to ensure automatic validation of food safety and halal compliance. The implementation of this blockchain is expected to increase consumer trust in meat products, reduce the risk of contamination, and strengthen accountability throughout the meat supply chain.
Nia Budi Puspitasari, Anggit Kurnia Alfiati Devytasari, Aries Susanty ,
Volume 36, Issue 1 (3-2025)
Abstract
Sustainable Development Goals (SDGs) 12 promotes environmentally responsible consumption and production. One of its sub-objectives is to improve sustainable public procurement practices, in line with national policies and priorities. Sustainable Public Procurement (SPP) is a process of public organizations carrying out goods/services procurement activities that consider economic, social, and environmental aspects. This study identifies and evaluates the factors that drive the implementation of SPP in Yogyakarta Provinces, and seeks recommended solutions based on these driving factors. The respondents selected as the object of this study were 30 procurement actors in Yogyakarta Province. In this study, the driving factors for the application of SPP were divided into 6 factors with 22 subfactors. The analysis method used is the RII method. RII is a method for identifying the relative importance of causation of an event based on its likelihood and effect using the Likert Scale. The results showed that 6 of the 22 subfactors that encourage the implementation of SPP are the availability of sustainable products, sustainable goods/services procurement policies and procedures, the availability of sustainable human resources, the availability of sustainable product/service suppliers, organizational values, and the cost of sustainable products/services.
Maryam Ghasemi, Mehdi Seifbarghy, Nezir Aydin, Wichai Chattinnawat,
Volume 36, Issue 1 (3-2025)
Abstract
One of the most important issues regarding community health is animal health, followed by the health of animal products. Providing a sustainable environment for production facilities like livestock centers is essential. In this study, we have proposed designing four fuzzy inference systems for managing the sustainability of livestock centers. The first, second, and third systems are applied for the economic, social, and environmental dimensions. The fourth is for a system whose output is the sustainability level while its inputs are the three addressed sustainability dimensions. The data source was experts' judgment, and the major limitation of this research was access to a limited number of experts in making system rules. The validation is made by cross-checking with other experts. Considering a maximum of 10 points for each sustainability dimension and supposing that the economic dimension is 5.05, the social dimension is 7.77 and the environmental dimension is 8.12, the sustainability level turns out to be 7.92
Imam Djati Widodo, Qurtubi Qurtubi, Elisa Kusrini, Feris Firdaus, Roaida Yanti,
Volume 36, Issue 1 (3-2025)
Abstract
Food supply chain management has become a crucial issue due to increasing food waste caused by globalization and population growth, which not only harms the environment but also social and economic aspects. The circular model has proven to be a powerful solution to overcome this, but its implementation is quite challenging due to the involvement of many stakeholders along the supply chain. So, it is important to understand the driving factors of a circular economy in the food supply chain (FSC) which can stimulate the development of a circular food supply chain, the barrier factors that can cause the failure of circular practices in the FSC, as well as strategies to overcome and mitigate the barriers that arise. Therefore, this study conducted a systematic literature review by analyzing 43 articles to answer specific research questions related to drivers, barriers, and circular food supply chain (CFSC) strategies. The results present nine main drivers, main barriers, and strategies, of which there are 47 sub-drivers, 50 barriers, and 47 strategies. Out of all the strategies identified, 24 greatest strategies using Pareto and SWOT analysis can be adopted for CFSC practice in Indonesia. This research contributes to the existing literature with the strategies, along with the responsible FSC stakeholders.
Atef Fakhfakh, Amr Noureldin, Mohamed Aboueldahab, Basem Nabil,
Volume 36, Issue 1 (3-2025)
Abstract
This paper focuses on mobile telecommunication companies (MTCs) in Egypt to investigate the impact of digital leadership (DL) on sustainable performance (SP). The mediating role played by digital organizational culture (DOC) in the relationship between DL and SP is also examined. The survey method is employed to conduct this research, and data is collected from 331 respondents. The proposed hypotheses are tested using structural equation modeling and analyzed using structural equation modeling Smart PLS V.4. The results indicate that DL directly influences DOC. SP and DOC partially mediate the relationship between DL and SP. Previous research has not extensively examined the mediating role of DOC in the relationship between DL and SP. This research is one of the first studies to demonstrate that DL positively impacts the SP of Egyptian MTCs through the mediating role of DOC.
Atef Fakhfakh, Salaheldin Salaheldin, Amr Noureldin, Mohamed Aboueldahab, Neama Elwakeel,
Volume 36, Issue 1 (3-2025)
Abstract
This study investigates the interplay between manufacturing ambidexterity, Industry 4.0 readiness, and sustainable excellence in Egypt's food and beverage sector. It explores how Industry 4.0 readiness mediates and moderates the relationship between ambidexterity and sustainability outcomes. A quantitative research design was employed, utilizing a survey of 308 professionals in Egypt's food and beverage industry. Structural equation modeling (SEM) was used to analyze the relationships among manufacturing ambidexterity, Industry 4.0 readiness, and sustainable excellence. The results reveal that Industry 4.0 readiness fully mediates and significantly moderates the relationship between manufacturing ambidexterity and sustainable excellence. While manufacturing ambidexterity alone does not directly impact sustainable excellence, its effect becomes significant through Industry 4.0 readiness, highlighting the importance of digital transformation. This study focuses on a single sector in Egypt, limiting generalizability. Future research could explore other industries and regions or examine specific dimensions of Industry 4.0 readiness. The findings emphasize the need for organizations to invest in digital infrastructure and foster ambidextrous capabilities to achieve sustainability goals. Policymakers are encouraged to support Industry 4.0 adoption through incentives and training programs to enhance competitiveness and sustainability in emerging markets. This study contributes to the limited research on the application of manufacturing ambidexterity and Industry 4.0 technologies in developing economies, offering insights into achieving sustainable excellence through digital transformation.
Muh Syarif, Ismie Roha Mohamed Jais, Iffan Maflahah, Ihsannudin Ihsannudin,
Volume 36, Issue 1 (3-2025)
Abstract
The research focuses on improving the performance of the corn supply chain in Madura Island, Indonesia. The purpose of the study is to identify, evaluate, and prioritize risks that have the potential to disrupt the smooth operation of the corn supply chain. The research method uses Failure Mode and Effects Analysis (FMEA) to identify risk levels and Root Cause Analysis (RCA) approach for mitigation strategies. Risk level assessment is based on severity, probability, and detectability at the level of farmers, middlemen, processing industries, and distributors. Based on the analysis, it shows that the risks are a priority in handling and prevention as well as proposals that can be made to improve the root cause of the occurrence of risks with the highest category based on the RPN value at the farmer level are the occurrence of pest and disease attacks (648), the middleman level is when the amount of corn is abundant (336), the processing industry level is the price of corn is unpredictable (252), and the level of distributors is a limitation in product promotion (324). To improve the efficiency and quality of the corn supply chain, namely increasing storage capacity, using more efficient processing technology, flexible production planning, and more innovative marketing strategies. The managerial implications of corn-supply chain risk assessment are the need to improve product quality, corn supply stability, price management, and strengthen partnerships and mutual benefits between all parties in the supply chain. Every element of the supply chain needs to encourage the adoption of modern technologies in maize cultivation, processing, and distribution to increase productivity and reduce risks associated with manual processes. It is necessary to establish mitigation strategies to address environmental risks, including the implementation of sustainable agricultural practices and early warning systems.
