International Journal of Industrial Engineering and Productional Research
Volume:35 Issue: 4, Dec 2024

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.

The Internet of Things (IoT) emerged as a pivotal catalyst in shaping the landscape of Industrial Revolution 4.0. Its integration within the manufacturing sector holds transformative potential for enhancing productivity on the production shop floor. Real-time monitoring of production processes becomes feasible through the implementation of IoT. Allows companies to promptly assess whether production outcomes align with predetermined plans, facilitating agile adjustments for swift improvements. In the face of volatile consumer demand, the company can efficiently strategize planned production approaches in response to significant shifts in consumer needs. This study endeavours to design a robust real-time production monitoring system employing the Internet of Things paradigm. The system's architecture emphasizes embedding sensors within the production floor processes to discern product types. Subsequently, a web platform enables seamless dissemination of production data to all relevant components. By leveraging real-time monitoring capabilities through IoT, the company gains the agility to swiftly decide and adapt production strategies, especially amid dynamic shifts in consumer demand.

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.

This paper proposes a decision-support model for supplier selection based on integrating the step weight assessment ratio analysis (SWARA), the method based on the removal effects of a criterion (MEREC), and Additive Ratio Assessment (ARAS) using a case study of the leather industry in Indonesia. The model starts by identifying the main criteria using the opinions of leather industry experts using Delphi. The second stage is to weigh them based on the main criteria, using compromising of objective and subjective weighting methods, namely MEREC and SWARA. The suppliers are selected and ranked based on the main criteria. Lastly, a sensitivity analysis will be performed to check the robustness. Delphi methodology adopted in this study gives managers in Indonesia's leather industries insights into the factors that must be considered when selecting suppliers for their organizations. The selected approach also aids them in prioritizing the criterion. Managers can utilize the supplier selection methodology suggested in this study to rank the suppliers based on various factors/criteria. This study makes three novel contributions to the supplier selection area. First, Delphi is applied to the Indonesian leather industry and integrates MEREC, SWARA, and ARAS into supplier selection. Second, sensitivity analysis allows the determination of the impact of modifications in the primary criteria on the ranking of suppliers and assists decision-makers in assessing the resilience of the process. Last, we find it essential to develop a simple methodology for managers of the Indonesian leather industry to select the best suppliers. Moreover, this method will help managers divide complex decision-making problems into more straightforward methodologies.

Linguistic variables (LVs) provide a reliable expression of cognitive information. By inheriting the advantages of LVs, we can express uncertain and incomplete cognitive information in multiple attribute decision-making (MADM), and they do so better than existing methods.  In the decision-making process, we can consider decision experts’ (DEs’) bounded rationality, such as cognition toward loss caused by the DEs’ cognitive limitations during the decision process. Therefore, it is necessary to propose a novel cognitive decision approach to handle MADM problems in which the cognitive information is expressed by LVs. In this paper, we employ LVs to represent uncertain and hesitant cognitive information. Then, we propose a mathematical programming approach to solve the MADM problems where attributes or cognitive preferences are not independent.  Moreover, the validity and superiority of the presented approach are verified by dealing with a practical problem.

Madura Island, comprising four regencies, exhibits a diverse array of agricultural resource potential, particularly in paddy, maize, cassava, and soybeans. Althought the Gross Regional Domestic Product assesses economic progress. it inadequately reflects the whole spectrum of potential within each region. A comprehensive observation of this diversity is required to facilitate a more focused development approach. This study aims to employ a hybrid hierarchical clustering method to delineate and classify the geographical regions of Madura Island according to their agricultural potential. K-means clustering, that part of hybrid hierarchical clustering approach was used to achieve aims of research. Number of farmers, land area, and commodities production were variable that used to classify regional based on its potentials. First, hierarchical method was performed to determine the appropriate number of clusters then K-means clustering was applied to classify the regions based on agricultural commodities. The results show effectively determined Madura Island's agricultural potential using the hybrid hierarchical clustering method, which categorizes locations based on characteristics of agricultural production. The research reveals six clusters, each characterized by a unique profile of primary commodity production, including paddy, corn, soybeans, and cassava. Implication of this result is offering insights into regional development of Madura based on agricultural potential.

Improving hard machining efficiency is a growing concern in industrial production, but environmentally friendly characteristics are guaranteed. Nanofluid minimum quantity lubrication (NF MQL) has emerged as a promising solution to improve cooling and lubrication performance in the cutting zone. This paper utilizes Box-Behnken experimental design to identify the influences of Al2O3/MoS2 hybrid nanofluid MQL hard turning using CBN inserts on surface roughness and cutting forces. Mathematical models were employed to predict thrust cutting force, tangential cutting force, and surface roughness in hard turning under MQL conditions using Al2O3/MoS2 hybrid nanofluid. The study results reveal that the minimum thrust force (Fy) occurs at a nanoparticle concentration of 0.5%, air pressure of 5 bar, and flow rate of 236 l/min. In comparison, the tangential force (Fz) reaches its minimum at a nanoparticle concentration of 0.8%, air pressure of 5 bar, and airflow rate of 227 l/min. The minimum surface roughness was achieved with a nanoparticle concentration of 1%, air pressure of 4.7 bars, and airflow rate of 186 l/min. Additionally, based on the multi-objective optimization, an optimal parameter set of NC=1%, p=5 bar, and Q = 210 l/min was identified to bring out the minimal values of surface roughness (Ra) of 0.218 µm, thrust force (Fy) of 115.9 N, and tangential force (Fz) of 93.3 N.

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.

Robots are one of the testbeds that can be used as objects for the application of intelligent systems in the current era of Industry 4.0. With such systems, robots can interact with humans through perception (sensors) like cameras. Through this interaction, it is expected that robots can assist humans in providing reliable and efficient service improvements. In this research, the robot collects data from the camera, which is then processed using a Convolutional Neural Network (CNN). This approach is based on the adaptive nature of CNN in recognizing visuals captured by the camera. In its application, the robot used in this research is a humanoid model named Robolater, commonly known as the Integrated Service Robot. The fundamental reason for using a humanoid robot model is to enhance human-robot interaction, aiming to achieve better efficiency, reliability, and quality. The research begins with the implementation of hardware and software so that the robot can recognize human movements through the camera sensor. The robot is trained to recognize hand gestures using the Convolutional Neural Network method, where the deep learning algorithm, as a supervised type, can recognize movements through visual inputs. At this stage, the robot is trained with various weights, backbones, and detectors. The results of this study show that the F-T Last Half technique exhibits more stable performance compared to other techniques, especially with larger input scales (640×644). The model using this technique achieved a mAP of 91.6%, with a precision of 84.6%, and a recall of 80.6%.

SMEs (Small and Medium Enterprises) play a vital role in developing countries like Indonesia, contributing 12.85% to the GDP. However, Indonesia ranks low in the Global Index of Digital Entrepreneurship Systems by the Asian Development Bank. A study in  Bekasi regency found that nearly 100% of SMEs still rely on conventional systems, facing common issues like low stock accuracy and lack of transparency. While software solutions exist, they often fail to address the real issues SMEs face in the real world. This research aims to create a digital transformation framework tailored to the real issues of SMEs, confirmed by stakeholders. This study used exploratory mixed methods, identifying seven steps for digital transformation: defining customer needs, identifying gaps, setting goals, selecting technology, addressing current problems, planning and financing, and evaluation. These steps cover six dimensions: Customer needs, Processes, Planning and Strategy, Technology, Resources, and Financing. The findings highlight that digital transformation is not just about adopting technology but involves a comprehensive approach grounded in customer needs. This framework offers significant value as a main contribution to academics, practitioners, policymakers, and stakeholders by addressing SMEs’ real-world challenges and ensuring that digital transformation is effective and relevant

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.