Production Process Management Research Articles

STRUCTURAL AND FUNCTIONAL PROTOTYPING OF THE EXECUTIVE PART OF THE ANGULAR MANIPULATOR ROBOT

In today's conditions of total digitization and informatization of all spheres of activity, more and more agro-industrial enterprises direct their resources to the development and implementation of modern technical and technological solutions, to modernize their own production capacities, with the prospect of ensuring competitiveness on domestic and foreign markets. In turn, such technological renewal of the agricultural sector of the economy is usually based on the use of high-tech systems of management, monitoring, control, management and automation of production processes, with the use of highly integrated SMART systems and the involvement of artificial intelligence technologies. Taking into account the significant volumes of cargo processing, agricultural enterprises, in particular in warehouses, a promising direction and reserve for ensuring high labor productivity is the use of robotic manipulators, and research is devoted to increasing their functionality and maneuverability, including through the use of intelligent control systems and optimization of the structure of executive mechanisms. relevant and will have practical value. The article substantiates the conditions for ensuring functional and structural similarity and proposes criterion equations of similarity for a large-scale transition from a natural sample of an industrial manipulator to a physical model-prototype. On the basis of the obtained criteria and using the scaling factors, the design parameters of the prototype were determined, a computer 3D model was developed, and based on the analysis of the trajectories, corrections were made to the linear dimensions of the manipulator and the ranges of angular movements of the links, as well as the compliance of the created prototype with the conditions of physical modeling was checked, according to the criteria of similarity. Also, the article presents the process of manufacturing an angular manipulator by using additive technologies of layer-by-layer build-up of parts on a 3D printer.

Перспективы развития традиционного хозяйства в контексте цифровой трансформации экономики арктического региона

Technological and digital equity remains an elusive goal for rural areas and agribusinesses operating in the Russian Arctic, where digitalization is still insufficient and progressing at a much slower pace compared to businesses in other regions of the country. At the legislative level, the role of digital technologies and the modernization of traditional farms (e.g., reindeer herding, fishing, etc.) has not yet been adequately assessed. The purpose of this study is to analyze the prospects for developing the traditional economy of small Indigenous minorities in the North in the context of the digital transformation of the Arctic economy. The scientific novelty of this study lies in examining the current use of digital technologies in northern reindeer husbandry, specifically in the Nenets Autonomous District. It explores factors affecting the digitalization of production processes and identifies promising areas for introducing new digital technologies to preserve and improve the efficiency of this economic activity. Using non-linear logit regression, the study reveals that the greatest risks to the availability of digital technologies on farms are linked to factors such as digital literacy, access to high-speed Internet, and the quality of equipment, while factors like access to transport and the age distribution of the workforce are less influential. The study also analyzes the digital technologies used by traditional farms, such as the Internet of Things (IoT), satellite communications, and digital platforms. It identifies promising directions for the digitalization of production management processes, including reducing economic risks associated with agrometeorological assessments, forecasting pasture conditions, automating processes, introducing additive technologies, enhancing communication, and improving the quality of life for workers on traditional farms.

STUDY OF THE EFFECTIVENESS OF METHODS FOR IMPROVING THE ACCURACY OF MEASUREMENTS OF TECHNOLOGICAL PARAMETERS OF THE CONTROLLED OBJECT

The norms and indicators of controlled parameters of technological processes are established, which are most often represented by a nominal value with a maximum permissible deviation (confidence interval). It is shown that the section on production control and process management is one of the most important parts of the technological regulations. A separate task of this section is purely metrological, which ensures the selection of measuring instruments in accordance with the requirements for the above indicators and norms of the controlled parameters. It is noted that the greatest difficulty in such selection arises when controlling a process parameter using a measuring set consisting of a primary converter, a possible intermediate converter and a secondary instrument. At the same time, according to the regulated standard, the determination of the total measurement error in such circumstances is carried out in the assumption that the most probable value of the parameter is only one measurement result, and the components of the measurement result error are the absolute basic and additional (non-excluded systematic) errors of the measuring instruments. Recommendations are given on the choice of the main error, namely, at the point with the nominal or the limit value at which the error is maximum. A condition is formulated under which the selected measuring instruments with their standardised metrological characteristics must meet the requirements of the technological regulations. The effectiveness of various methods of improving the accuracy of measurements of some technological parameters is investigated on specific examples, including the calculation of the efficiency factor of their application. This includes an analysis of the impact of both basic and additional errors that occur during measurements. The errors were calculated on the basis of the data provided in the regulatory and technical documentation for the relevant devices, which made it possible to accurately assess their impact on the overall measurement accuracy. It is proved that the greatest efficiency in improving the measurement accuracy of a process parameter is provided by increasing the measurement accuracy of the primary converter or reducing its measurement range.

Integration of Thermo-, Hydrodynamic, and Kinetic Factors in the Mathematical Modeling of the Catalytic Reforming Process

Background: The integration of various factors affecting processes in oil refining is crucial for enhancing both the efficiency and sustainability of the industry. In a changing market and increasingly stringent environmental regulations, it is essential to continuously update approaches, develop innovative solutions, and optimize production processes to achieve the best possible outcomes. Aim: The study aims to integrate thermodynamic, kinetic and hydrodynamic aspects into a unified model, and to validate the outcome based on experimental data and real-world operating conditions to ensure the accuracy and reliability of model predictions. Materials and methods: The primary research methods include statistical data analysis, process modeling, and experimental studies at various stages of the production cycle. Results: The study identified the key parameters that significantly impact the quality of the final product and production efficiency. Furthermore, it offers recommendations for optimizing production processes based on the data obtained. Conclusion: The study concludes that integrating various factors can significantly enhance production performance and reduce refining costs. The study emphasizes the importance of an integrated approach to the management of production processes in the oil refining industry, which can facilitate the further development of the industry. The model created can be utilized for training personnel in process simulation. With its user-friendly interface, it requires no extensive programming knowledge, making it well-suited for the initial training of specialists.

Development of intelligent systems for monitoring and management of agricultural enterprises

Modern agriculture faces a number of challenges, among which are the growing demand for food, the impact of climate change, limited natural resources and the need to ensure the environmental sustainability of production. In this context, the introduction of new technologies, in particular intelligent systems, becomes an important tool for improving the efficiency and competitiveness of agro-industrial enterprises. Intelligent Systems Based on Use artificial intelligence, machine learning, the Internet of Things (IoT) and big data are opening up new opportunities for monitoring and managing agricultural processes. The article presents the results of a study aimed at the development of intelligent systems that provide comprehensive monitoring of the state of fields and management of production processes at agricultural enterprises. The proposed system integrates data from various sources, such as sensors, drones, satellite images and other IoT devices, which allows you to create a detailed and up-to-date view of the state of crops, soils and climatic conditions. This, in turn, allows you to make prompt and sound management decisions that increase the productivity and efficiency of agricultural production. Particular attention is paid to the development of algorithms for processing big data and machine learning methods, which are used to analyze the data obtained and generate recommendations for optimizing the use of resources. This includes managing water resources, fertilizers, crop protection products, as well as predicting yields and identifying potential threats such as plant diseases or pests. The implementation of such systems allows not only to reduce resource costs, but also to minimize the environmental impact of agriculture by reducing the use of chemicals and water. Thus, the article makes a significant contribution to the development of intelligent technologies in agriculture, offering new approaches to the management and monitoring of agricultural enterprises. These approaches can form the basis for the development of future innovations in this area, contributing to improving the resilience and efficiency of agro-industrial production in response to modern challenges.

The implementation of lean and digital management techniques using artificial intelligence in industrial settings

This article proposes an interdisciplinary approach to analyzing the problems associated with the introduction of digital technologies into industry, with special attention to artificial intelligence. The author offers a methodological framework based on the principles of lean manufacturing and artificial intelligence to eliminate the identified gaps. To improve the efficiency of production systems, a model is proposed that combines the principles of lean manufacturing with digital tools such as artificial intelligence. The introduction of a new concept and method of production process management requires a revision of the project management strategy and organizational practices. As one of the main results, a project is proposed aimed at introducing lean and digital production methods using modern digital technologies. The introduction of digital technologies has led to the appearance of preliminary results that allow the heads of production departments to make informed decisions based on up-to-date data. Enterprises using the potential of artificial intelligence demonstrate higher efficiency, complete orders ahead of schedule and benefit customers through innovative software and algorithms. This approach allows organizations to effectively manage their activities, providing value to customers and increasing their satisfaction. Issues related to Industry 4.0, digitalization and artificial intelligence play a key role in the context of business transformation, therefore, the study of these aspects can be valuable both for the academic community and for the practical activities of companies.

Design and Development of an Innovation Model for the Opak Machine at Opak Ketan MSMEs Cibuaya Karawang using the House of Quality Method

One of the MSMEs (Micro, Small Medium Enterprise) in the food sector located in Cibuaya Karawang area, namely Opak Ketan MSMEs, still carries out the opak production process manually, resulting in various shapes and diameters of opak. Opak ketan itself is made from a type of opak with different sizes and thicknesses, such as larger and thicker opak, and smaller opak with thin thickness and a crisp texture due to being baked. According to field analysis and interviews conducted at the research site, the manual production process presents difficulties and causes fatigue for workers, particularly in the formation of opak diameter and thickness. The printing process is done one by one (repeatedly), making it time-consuming and causing fatigue for workers who sit for long periods. As a result, the shape and thickness of opak lack precision (inconsistencies in size), leading to a lack of standardization. In terms of work activities, workers easily experience fatigue, particularly in their hands, waist, and back due to repetitive static movements. Ergonomically, working conditions are a factor that can enhance productivity, safety, and comfort for workers. Therefore, there is a need for the development and design of a suitable technology innovation model that can facilitate opak workers by creating a tool for printing opak ketan using an opak printing machine. This innovation can influence increased production capacity to be faster and standardized in shape and thickness. This design also applies the House of Quality (HOQ) model which successfully links consumer desires with a product design which produces 10 assumed criteria and 15 characteristics in order of importance. So it is concluded that the first priority that needs to be taken into account is determining price, flexibility and engine resources (fuel).

Inovasi Produk Kopicek (Kopine Wong Picek) Sebagai Upaya Pemberdayaan Kelompok Difabel Tunanetra Mata Hati

Coffee is an important commodity with significant global demand. In Indonesia, East Java Province contributed 85.15% of the national coffee production in the Large Estate sector in 2022, supporting the growth of the local coffee industry such as coffeeshops. In Surabaya, the Mata Hati Community, consisting of people with disabilities, launched a coffee brand called "Kopicek" as an effort to empower disabled individuals, especially the blind. However, they face challenges in terms of quality control, manual production processes, and limited promotion. To carry out and create innovations in empowering the visually impaired or the blind, our team provides mentoring and training to improve the quality and efficiency of Kopicek's production. Through a community service program, the social service team designed solutions to improve the quality and efficiency of Kopicek's production. These solutions include quality control training, provision of automated coffee machines and custom packaging press machines that are accessible to blind baristas, as well as digital marketing training. The results of this program are expected to help the Mata Hati Community maintain coffee product consistency, expand marketing through digital media, and enhance Kopicek's branding in the local market.

The Application of Virtual Reality Technology in Clothing Product Development

With the rapid development of modern technology, virtual reality technology is gradually emerging in the clothing industry, which has a profound impact on key aspects such as product design, sample clothing production, production management, and supply chain management. This study delves into the specific application status, efficiency advantages, and complex challenges faced by virtual reality technology in clothing product development. Through system analysis, this article provides a detailed explanation of the technology's implementation of digital clothing design, virtual fitting, and efficient integration of 3D modeling and simulation systems. Meanwhile, this article provides a detailed and in-depth discussion on the application details of virtual reality technology in areas such as sample clothing production, production process management, and supply chain operations. The research results show that virtual reality technology has brought new design and management tools to the clothing industry, effectively improving design efficiency, reducing cost expenditures, enhancing user experience, and driving the entire industry towards more efficient, intelligent, and sustainable development.

DIGITALIZATION OF SHOP FLOOR MANAGEMENT AT SELECTED SEGMENT IN THE INDUSTRIAL ENTERPRISE IN THE CONTEXT OF INDUSTRY 4.0

As businesses gradually transition to the concept of Industry 4.0, they strive to digitize all necessary data. The aim of this article is to demonstrate how the digitalization of shop floor management (dSFM) can enhance the efficiency of production process management, and to propose steps for faster and more effective implementation of dSFM in industrial enterprises. The digitalization of shop floor management increases agility, responsiveness, and overall improves decision-making processes. In the analyzed enterprise, the traditional shop floor management was overly burdensome, accompanied by excessive documentation and inefficient processes. It was necessary to analyze three areas: data collection, information visualization, and the flow of shop floor meetings. Based on this analysis, shortcomings were identified, and measures were proposed to automate routine activities and streamline processes. This case study thus fills a gap in the research by focusing on specific challenges in implementing dSFM in an industrial enterprise.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

FORECASTING AND OPTIMIZATION OF CATALYTIC CRACKING UNIT OPERATION UNDER CONDITIONS OF FUZZY INFORMATION

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.

Advanced YOLOv8-Based Efficient Detection Method for Sugarcane Stem Nodes

The precise identification of sugarcane stem nodes is crucial for intelligent seed cutting, planting placement, sugarcane garden production management process optimization, yield improvement, and economic benefits. However, efficiency, model complexity, and real-time performance are still issues with the sugarcane stem node detection methods are currently in use. This study decided to visually detect distinguish between sugarcane stem nodes in a structured scene using the advanced YOLOv8 model in order to address this issue. For the purpose of to create an image training set and a test set, a field sugarcane image collection experiment was first designed. The collected sugarcane images were manually labeled. The YOLOv8 network was then utilized as the sugarcane stem node detection model to find the ideal hyperparameter combination and train the model. Finally, field-based recognition experiments are carried out to verify the method's effectiveness and efficiency. Experimental results show that the precision, recall, mAP, single-frame inference time and model size of our method on the test set are 0.973, 0.958, 0.974,19.80 ms and 6.30 MB respectively. Compared with the Edgeyolo_S_Coco network and Edgeyolo_Tiny network, the mAP value of the YOLOv8_n network has increased by 1.70% and 1.30% respectively, the single-frame inference time has been reduced by 4.71 ms and 1.50 ms respectively, and the model size has been reduced by 33.70 MB and 17.50 MB respectively. This method has advantages in detection performance and generalization ability, and can effectively meet the requirements for algorithm accuracy and model complexity in outdoor environments, providing solid technical support for sugarcane harvesting and planting in intelligent agricultural production.

The Digital Environment and Its Impact on Labour Market Development Regulation

The article considers the influence of digitalisation in the management of production processes. As a result of the introduction of digital technologies and the transfer of the mechanism for the management of production processes into a digital environment, approaches and methods for regulation of the development of the labour market are being transformed. On the basis of the conducted analysis of research and regulatory documents, the author’s interpretation of the term “digital environment” is offered, which is interpreted from two points of view: on the one hand, as an integrated environment, providing information to all users of the labour market; on the other hand, as an environment for digital interaction between economic actors and the entire digital infrastructure. Using this approach, the authors have identified the system-forming components of the digital environment in the sphere of labour market development regulation, which are grouped into three blocks: the system of digital environment management, the system of labour relations management, and, as a result of the interconnection of these systems, a block of ecosystem directions of labour regulation, which in relation to grouped system blocks allow to influence the basic economic indicators of labour in the digital environment, has been allocated.

Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications

Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA.

Improving the Quality of Production Management Processes Based on Neural Network and Neuro-Fuzzy Models and Tools

Improving the Quality of Production Management Processes Based on Neural Network and Neuro-Fuzzy Models and Tools

ARTIFICIAL INTELLIGENCE IN LABOUR RELATIONS: A THREAT TO HUMAN RIGHTS OR NEW OPPORTUNITIES?

This article explores the possibility of using artificial intelligence in the field of labour relations. Modern technologies provide new opportunities, but at the same time, they give rise to a number of complex issues, solving new approaches to the realization of the right to work and proper social security by able-bodied citizens, which is important today, when Ukraine is defending its sovereignty and independence from military aggression by the Russian Federation. The use of machine learning algorithms and systems can lead to significant improvements related to the professional training of labour resources, management of production processes, labour protection and other aspects of labour relations.We came to the conclusion that the modernization of labour law involves expanding the circle of participants in labour relations and revising the meaning of the term "employee". Thus, the presence or absence of access to technology will create new forms of inequality in the interaction between "employee-to-employee" and "worker-robot (AI)".A proactive approach is proposed to mitigate the consequences of possible threats to the use of artificial intelligence in labour relations through an in-depth study of all possible threats arising in connection with the use of modern technologies. A proposal is made to take as a basis the international experience of using AI in the social structure of the state and adapt it to the life of the state, which, in turn, will contribute to the promotion of important theses on the quality and accessibility of data at the country level, and in cities in particular, promoting participation in data exchange schemes at the level of the private and public sectors.

INTEGRATION OF SENSOR TECHNOLOGIES INTO THE STRUCTURAL SCHEME OF AUTOMATED GREENHOUSE CONTROL

The development of wireless sensor network technologies has opened up new opportunities for monitoring and controlling greenhouse parameters in precision agriculture. Over the past decades, significant advances in agricultural technology have been made, which has contributed to higher yields. With the uneven natural distribution of rainwater, it is important for farmers to ensure that water is distributed evenly to all crops on the farm or according to the needs of individual crops. There is no one-size-fits-all irrigation method that is suitable for all weather conditions, soil types, and different crops. Greenhouse technologies can be the best solution to this problem. Choosing the right method requires a detailed analysis of all greenhouse parameters. Farmers often face large financial losses due to incorrect weather forecasting and the wrong method of irrigating crops. The aim of the study is to create conditions for increasing the efficiency of growing vegetables in greenhouses by introducing highly integrated systems for control, monitoring, management and automation of production processes. This involves the development of a structural diagram of a mechatronic smart system for monitoring and dispatching technological processes. As a result of the research, a flowchart for automated greenhouse control was developed. In this scheme, the adaptation unit, based on the obtained values of environmental parameters, compares them with the standard values. If deviations are detected, the system sends information signals to adjust the agroclimatic conditions by activating the appropriate actuators - humidifiers, lighting lamps, internal irrigation and heating systems.

Machine Learning Analysis Using the Black Oil Model and Parallel Algorithms in Oil Recovery Forecasting

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model’s performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.

Determining inventory, purchasing and cutting policies to manage imperfect and perishable raw material

ABSTRACT Several industries deal with imperfect and perishable raw materials that need to be cut in order to assemble products. Managing the purchasing, cutting, and sequencing decisions is a challenging problem that spans both inventory control and production process management, where decisions are usually optimised separately. In this research we develop a dynamic programming model that determines these joint decisions. The inspiration comes from furniture companies where the raw material are sheets of plywood that may contain imperfections that need to be avoided when cutting pieces to build the assembled furniture. We evaluate decisions regarding the layout of pieces onto the plywood, dispatching policies of ordered furniture , and the quality of the raw material. The model is useful to perform cost analysis for different scenarios. Variations on the expected demand, purchasing, ordering, disposal, holding costs and initial inventory are considered. Experiments conclude that focusing in quality becomes more important than the age of the plywood sheet if the companies implement a production flow that cuts and sorts all pieces before assembling the furniture. Cost analysis confirms that a just-in-time policy in which little inventory is kept results in important savings when compared with the standard operation of the companies.