Optimal Product Line Research Articles

Optimizing Production Line Efficiency through a Standardized Work Tool: A Case Study

Lean methodology focuses on enhancing flow and resource efficiency by prioritizing customer value, eliminating waste, reducing variation, and fostering a culture of continuous improvement involving everyone in the organization. Implementing lean creates conditions that empower all employees to contribute to continuous improvement activities within the value stream. This approach not only lowers production costs, enabling reinvestment and employee engagement but also emphasizes professional development. This paper aims to maximize efficiency in seal production within a manufacturing industry by implementing lean tools such as standardized work and Kaizen. Standardized Work is a systematic method for producing parts or services centred on human motion. As a tool for ensuring quality, it defines efficient, safe work methods and helps eliminate waste, serving as the foundation for every operation. Once implemented, standardized work ensures that the same methods are repeated consistently, avoiding unnecessary motions, maintaining quality, ensuring safety, and preventing equipment damage. The consistent application of these methods improves product quality through standardized processes and procedures. By incorporating lean tools like defined worksheets and Kaizen, the production of seals in manufacturing industries can achieve significant productivity enhancements.

Operation optimization of cement clinker production line based on neural network and genetic algorithm

The operation control plays a great role in the running performance of an industrial process. To achieve a successful control, the key point is searching for the target value of the control parameters. The relationship between operation parameters and performance parameters is usually nonlinear and complex, so it is hard to control an industrial process excellently with workers' experience. Based on a large amount of actual operation data, a bridge between control parameters and performance parameters might be built by the neural network. Prediction model is established by using neural network, genetic algorithm was then used to refine these parameters, the optimal condition can be further achieved by combining the relation bridge and genetic algorithm (GA). Paying attention to a cement clinker production process, this article established an optimizing approach based on the neural network and genetic algorithm and one-year operation data (615 sets). The feeding mass rate of raw meal and coal at the precalciner and the feeding coal mass rate at the rotary kiln are selected as the main independent variable and control parameters, and the specific standard coal consumption is determined as the key performance parameter and the optimization objective function. The mean square error and the correlation coefficient of the established neural network model are 12.84 and 0.89, respectively. The relative errors of approximately all prediction data (92.52 %) are within ±5 %. The optimal values for the raw meal feeding rate, the coal feeding rate into precalciner, and the coal feeding rate into rotary kiln are 259.57 t/h, 7.84 t/h, and 7.40 t/h, respectively. In that optimal condition, the specific standard coal consumption reaches 80.00 kgstandard coal/tclinker (This is a relative value, as there is a slight drift in zero point of the factory's instruments). A highly accurate neural network model is developed, which can significantly reduce standard coal consumption and improve industrial energy efficiency.

Analysis of the Impact of Consumption Downgrading Trends on the Retail Market Landscape

In recent years, the increasingly complex economic environment has led to a clear trend of consumption downgrading, profoundly affecting the retail market landscape. The factors causing consumption downgrading are diverse, including slower economic growth, increased income uncertainty, and rising living costs. Consumers now prioritize cost-effectiveness and practicality in their purchasing decisions. Therefore, the retail market must keep pace with societal trends and actively adjust strategies to meet changing consumer needs. This analysis is from the perspective of product strategy, where retailers clearly measure the balance between practicality and cost-effectiveness, targeting product line optimization to ensure that products meet market development needs. Additionally, enhancing service quality will be a focus of future efforts, including improving pre-sale, in-sale, and after-sale services, raising staff service awareness and overall quality, and providing the best shopping experience for consumers. Moreover, effective brand marketing is crucial, using various means to enhance brand image and visibility, helping the retail market adapt better to changes and achieve sustainable development.

From Deterministic to Data-Driven: AI and Machine Learning for Next-Generation Production Line Optimization

From Deterministic to Data-Driven: AI and Machine Learning for Next-Generation Production Line Optimization

Optimization of Production Line Balance Based on The IE Method

As a part of the manufacturing industry, automotive parts manufacturing enterprises optimize their production line is of great significance to reduce production costs and improve the production efficiency of enterprises [1]. In this paper, we take the condenser production line of WF company as the research object, and we found that the production line has problems of low productivity and imbalance of the production line through investigation. We improve the balance of the production line based on the IE method, and finally help WF company to improve the production capacity and product quality, so that the balance rate of the condenser production line reaches a good level, and at the same time, it also provides a practical case of production line optimization for the whole manufacturing industry.

Deep reinforcement learning for maintenance optimization of a scrap-based steel production line

This paper presents a Deep Reinforcement Learning (DRL)-based optimization approach for determining the optimal inspection and maintenance planning of a scrap-based steel production line. The DRL-based optimization maintenance recommends the adequate time for inspections and maintenance activities based on the monitoring conditions of the production line, such as machine productivity, buffer level, and production demand. Some practical aspects of the system, such as such uncertainty of the maintenance duration and the variable production rate of the machines, were considered. A scrap-based steel production line was modeled as a multi-component system considering components dependencies. A simulation model was developed to simulate the dynamics of the system and assist with the development of the DRL maintenance approach. The proposed DRL-based maintenance is compared with traditional maintenance policies, such reactive maintenance, time-based maintenance, and condition-based maintenance. In addition, different DRL algorithms such as PPO (Proximal Policy Optimization), TRPO (Trust Region Policy Optimization), and DQN (Deep Q-Network) are investigated in the case-based scenario. The findings indicated the potential for significant financial savings. Therefore, the proposed maintenance approach demonstrates system adaptability and has the potential to be a powerful tool for industrial competitiveness.

Repositioning to sink: The pricing and quality decisions for product line considering the sinking market

Opening up new markets (e.g., the sinking market) is one way to boost growth momentum, especially with the decrease in the rate of technological development and the impact of the black swan event (e.g., the COVID-19 pandemic). Considering the difficulty and cost of developing new products, repositioning existing products by a price markdown is an easier way to expand the market. Companies can implement different pricing strategies for different products to target different markets and make extra profits, but products entering the sinking market may deeply erode the original market. Therefore, balancing the advantages and disadvantages of entering the sinking market is a significant challenge for firms selling products as a product line. By building a theoretical model, we investigate the optimal product line repositioning strategy when companies decide to expand the market. First, we examine the optimal conditions for companies to enter a sinking market. We then analyze the optimal product line strategy in the sinking market. Furthermore, we investigate the interaction between repositioning strategy and quality decisions. The main results indicate that with the increase of the consumers’ willingness to pay at the sinking market and the decrease of the production cost, the seller's motivation to enter the sinking market would be greater. More importantly, entering the sinking market with higher quality can also be the optimal decision. What's more, we also find that the seller implementing low-quality repositioning strategy may prefer fiercer product erosion. Interestingly, the consideration of the quality decision would motivate the seller to enter the sinking market more aggressively even when the production cost is relatively high. Yet, this motivation would be weakened when the production cost is relatively low.

Digital Twin for Predicting Progressive Damage in Operating Pressure Vessels

According to the definition of the French Alliance “Industry of the Future”, the digital twin is a virtual clone of a physical system or a process. It systematically implies the existence of a "digital model" coupled with the object it copies. Depending on the system concerned and the desired usage, it can be a geometric, multiphysical, functional, behavioral, and decision-making model. It must evolve over time like its real twin. It can be used to improve the control, security and optimization of production lines and factories, digital continuity at the product level, from its design to its end of life, monitoring and predictive maintenance.The digital twin appears as a reliable way to monitor operation, to evaluate the resistance and safety of pressure vessels (PVs) in real service conditions. Via the connected transducers (IoT), we can integration the life cycle information in an automatic data processing module, and finally to capitalize on all this data to optimize the design of new products.To this end, the first objective of the thesis is to contribute to the development of a methodology for real-time assessment of the integrity of pressure vessels, by implementing a predictive maintenance strategy in place of the classical and costly, preventive or curative maintenance, based on IoT and digital twin technologies. The second technological goal of the thesis is to develop methods to optimize and make reliable the design and operation of new generations of pressure structures.

Energy Flow Modeling and Low Carbon Analysis of Processoriented Production Line

Nowadays, energy and environmental problems are becoming more and more prominent, and the energy-based manufacturing industry also needs to face huge energy consumption problems. Therefore, production processes need to be investigated from an energy efficiency perspective at an early design stage so that maximum benefits can be obtained at low cost, low time and low risk. This process industry carbon emissions system does not calculate the consumption of raw materials and electricity in a generalized way at the boundaries of the plant. Rather, we delve into each step of the process and perform mathematical modeling of the process through a graphical approach, incorporating each link and factor that constitutes and influences the change in carbon emissions, and performing an aggregation of carbon emissions for the entire production line. It facilitates the subsequent improvement and optimization of the entire process-based production line, providing the basis for implementing and establishing the best energy-saving system.

Optimal product line design strategy for green and non-green products with blockchain technology

Optimal product line design strategy for green and non-green products with blockchain technology

Multi-objective coupling optimization of electrical cable intelligent production line driven by digital twin

With the development of the personalized and diversified service production paradigm, production lines need to be updated in a timely manner to meet market demand. In this paper, we propose a digital twinning-based coupled optimization method for an electrical cable intelligent production lines to address coupling issues such as unreasonable production line layout, imbalanced station loads, inaccurate logistics distribution, and inaccurate equipment testing. The system framework is constructed based on the idea of make-twin and object-oriented, and we introduce a piecewise coupling mechanism and a multi-objective optimization algorithm for digital twin model systems. Then, a cable production line example is presented to validate the effectiveness of the modeling approach. Upon implementing the proposed model in an electrical cable production line, productivity and line balancing increased by 13.4 % and 8.4 %, respectively, while equipment utilization increased by 4.38 %. The cycle times and defective product rates were reduced by 4.7 % and 17.6 %, respectively. The results show that the coupled optimization method for digital twin-based intelligent production lines is practical and instructive for improving production line efficiency.

Garment production line optimization using production information based on real‐time power monitoring data

AbstractThe implementation of Fourth Industrial Revolution technologies at a manufacturing site requires analysis of the site status based on real‐time information, optimization of the processes, and onsite execution. However, in labor‐intensive industries, such as the garment‐manufacturing industry, it is extremely difficult to develop smart factories based on real‐time onsite information because such industries are accustomed to managing labor using empirical expert judgment; moreover, they require rapid production circulation and are dependent on human‐related factors. In this study, we developed an optimization simulator using onsite real‐time production information that provides decision‐making support to maximize the productivity of a garment production plant. As an optimization method, a genetic algorithm was used to incorporate operator relocation into various garment production line variables. Through the developed simulator, field managers can predict and optimize productivity with simple operation in connection with production information. The application of the simulation optimization process to an actual production line in an Indonesian garment factory indicated that the simulator can improve productivity by 34.8%. The results of this study will provide guidance regarding the application of industrial information integration in labor‐intensive industries using methods that can systematically support decision‐making to achieve optimal productivity.

Multi-Device Consumption of Digital Goods: Optimal Product Line Design with Bundling

Multi-Device Consumption of Digital Goods: Optimal Product Line Design with Bundling

Push and Pull Hybridization Manufacturing Strategy in Medical Diagnostic Equipment Industry

Abstract: The global manufacturing industry employs two main strategies for production: push and pull manufacturing systems. Each strategy has its own set of advantages and disadvantages. However, in this paper, a hybrid push and pull system model is proposed, aiming to capitalize on the strengths of both strategies while mitigating their weaknesses. The proposed model specifically focuses on the medical diagnostic equipment industry, where the annual production volume is relatively low, typically under 200 products. The objective of the proposed model is to improve various aspects of the production system compared to the current approaches used in the industry. These improvements primarily revolve around optimizing production line efficiency, reducing the lead time between customer orders and product delivery, and enhancing overall productivity. By incorporating elements from both push and pull systems, the hybrid model aims to achieve these goals more effectively. The results of the study indicate that the proposed hybrid model outperforms the existing production system in terms of time optimization in the production line, minimizing the lead time between customer orders and product delivery, and increasing the productivity of the production line. These findings suggest that the hybrid model can offer significant advantages and improvements over traditional production approaches in the medical diagnostic equipment industry.

The optimal vehicle product line strategy considering product information disclosure under government carbon regulation

As customers focus on information disclosure of new energy vehicles and automakers attach great importance to product line strategies under carbon regulation, we study the manufacturer's optimal product line strategy considering product information disclosure under the government's carbon regulation. This study contributes to the emerging vehicle product line area by investigating how manufacturers can establish reasonable vehicle product lines and the impact of different carbon regulation policies on product line strategies. Through comprehensive analyses, we find that the carbon tax rate and carbon trading price affect the revenue of different product lines of the manufacturer and the information disclosure level of products. Importantly, we find that if the valuation (market price) of fuel vehicles is low, manufacturers should adopt a single product line strategy. Conversely, if the valuation (market price) of fuel vehicles is high, manufacturers should adopt a hybrid product line strategy. Moreover, our analyses indicate that when the market valuation of new energy vehicles is high and the valuation of fuel vehicles is low, or when the market valuation of new energy vehicles is low and the valuation of fuel vehicles is moderate, manufacturers prefer to produce new energy vehicle to fuel vehicle. In terms of regret aversion strategies for product line design, our research reveals that a low carbon tax rate (carbon trading price) is beneficial to the fuel vehicle, while a high carbon tax rate(carbon trading price) and high information disclosure cost are beneficial to the revenue of new energy vehicle. Finally, we obtain the automakers’ preferences for carbon regulatory policies through manufacturers’ willingness to disclose information, tax rate and carbon trading price.

Product Line Optimization for Car-Sharing Platforms in the Sustainable Transportation

As the sharing economy develops in sustainable transportation, car-sharing platforms improve the allocation efficiency of idle cars to customers. However, as the travel demand increases, and the vehicle type becomes various, the sharing market faces a new challenge to satisfy the customers’ diverse needs for quality-differentiated cars. Each customer would like to choose the right car type, while the manufacturing firm and the e-commerce platform should set the right car price for profit maximization. How to match the platform’s product line with the customer’s choice behavior becomes a problem for all the stakeholders in the sharing economy. From the perspective of customer heterogeneity, we establish a game-theoretic framework to study product line optimization in business-to-consumer (B2C) and consumer-to-consumer (C2C) sharing. We solve the optimal quality decision and the optimal pricing strategy for the manufacturing firm and the e-commerce platform in the sharing economy. In numerical experiments, we investigate how the sensitivity of the quality and the cost can jointly influence the profits of sustainable transportation. Our findings show that within the product line, the firm’s selling profit in the C2C sharing is negatively influenced by the sharer’s valuation, while the platform’s sharing profit in the B2C sharing is independent of either buyer’s valuation. We give some policy implications for scholars, practitioners, and policymakers in the sharing economy and point out some limitations and recommendations for the product line design.

We’re Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistency-preserving configuration operators</i> (CPCOs)—genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.

Product line extension with a green added product: Impacts of segmented consumer preference on supply chain improvement and consumer surplus

&lt;p style='text-indent:20px;'&gt;With the enhancement of environmental protection, more and more enterprises begin to develop green products. However, the high cost of green R&amp;amp;D leads to an increase of product price, which reduces the competitiveness of green products. In this paper, we model a supply chain which consists of one manufacturer and one retailer providing a primary product and a substitutable green added product in the market. In order to capture the impact of consumer behavior on the supply chain members' decision-making, we classify the market into two segments and assume that high-end green consumers have higher preferences for green products than ordinary consumers. Different to existing research, we assume ordinary consumers hold a positive but lower green preference compared to the green consumers. When analyzing the impacts of consumers' green preferences, we find that there exist specific boundaries of cost and market potential which define the optimal pricing strategy and product line design. Regarding profits, we find that when the green preferences of high-end and low-end consumers increase in the same proportion, the high-end market may not bring greater supply chain revenue. In particular, the marginal profit increase of the manufacturer is always greater than that of the retailer.&lt;/p&gt;

Applying value stream mapping for design and optimization of a chocolate bar production line

Objectives. The objective of this paper is to present the contribution of Value Stream Mapping (VSM) to design and optimization of a chocolate production line. Methodology. Initially data was collected for VSM parameters, within a four-month period for the design of the Current State Map. Future State Map was developed by equalizing production volumes to the line product’s Takt Time. A model implementation was carried out and efficacy was evaluated. Contribution. VSM allowed the company to increase uptime of the cooling process by 31.7% and 85g bar overproduction reduction by almost 100%. The company has found this tool highly useful for diagnostic analysis of production. However, the implementation of continuous improvement tools and infrastructure betterment will be needed to achieve higher efficiency.

Product line optimization with multiples sites

We consider the problem faced by a retail chain that must select what mutual-substitute items to display in each one of its stores to maximize revenues. The number of items cannot exceed the limit space capacity of each store. Customers purchase the one product that maximizes their utility, which depends on the product price, travel cost to the store, and reservation price, known to the retailer. The retailer can set different price markdowns at different stores and products. The retailer considers the decisions of customers, and solves a mixed-integer bilevel optimization problem, which can be formulated as a single-level optimization problem by using optimality conditions for the lower level. We propose Branch and Cut and Cut and Branch methods and include a family of valid inequalities to solve the problem. We compare the results with those of a Benders decomposition method. Our computational results show that the proposed Cut and Branch method obtains the best performance and improves the current state of the art.