Traditional approaches to product development have primarily focused on aspects related to business profits and providing a suitable functionality of products [...]

Celem pracy jest przedstawienie problemu wariantowania przedsięwzięć informatycznych zagrożonych niepowodzeniem w aspekcie Problemu Spełnienia Ograniczeń (PSO). Deklaratywna struktura modelu wiąże obszary funkcjonalności przedsiębiorstwa oraz realizowanego w nim projektu. Wymienione obszary funkcjonalności modelowane są w postaci PSO, zawierają one zbiory zmiennych decyzyjnych, rodziny zbiorów dziedzin tych zmiennych oraz zbiory ograniczeń wiążących te zmienne. Otwarta struktura modelu pozwala rozwiązywać problemy decyzyjne różnego poziomu szczegółowości, i to związane z pytaniami zarówno o skutki zakładanych decyzji, jak i o decyzje gwarantujące oczekiwane skutki. Deklaratywny charakter proponowanego modelu w naturalny sposób pozwala implementować go w środowiskach programowania z ograniczeniami. Możliwości tego typu ilustruje załączony przykład.

Improving product sustainability is becoming an increasingly significant challenge for modern enterprises. A growing number of manufacturers are interested in enhancing product sustainability throughout the product life cycle. This study is concerned with using case-based reasoning to identify ways of improving product sustainability and select variables for model specification. Parametric models are applied to search for opportunities to improve product sustainability. This can be achieved through changes introduced at the product design stage. Simulations are performed using constraint-satisfaction modeling to identify conditions for achieving the sustainability targets of new products. Constraint-satisfaction modeling provides a suitable framework for finding all possible sustainability-enhancing changes (if any) during the new product development process. These changes may support R&D specialists in identifying opportunities to improve the sustainability of new products. We demonstrate the usefulness of the proposed approach with an example in which our method enabled a reduction in the product failure rate and an increase of battery lifespan for a robot vacuum cleaner line. We analyzed several factors affecting two targets of product sustainability: minimizing the product failure rate and maximizing battery lifespan. Our findings indicate that R&D staff size is the biggest factor in reducing the product failure rate, and that battery capacity is the most significant factor in battery lifespan.

The article proposes indicators to evaluate a thermal insulation investment in a building, such as net present value (NPV), profitability index, discounted payback period, and ecological cost efficiency. Economic and ecological aspects were taken into account. Life Cycle Assessment (LCA) was used in the ecological analysis. The following heat sources in the building were considered: condensing gas boiler and heat pump. The developed indicators also depend on the pre-set temperature in residential premises. A methodology to determine the optimum thermal insulation thickness for both economic and ecological reasons was also proposed. A case study was analyzed, and a reference building, typical for Polish construction conditions, was used for research. Various solutions were suggested regarding the type of thermal insulation material and heat sources. The values of the indicators were determined for the proposed variants and for the economically and ecologically optimum thermal insulation thicknesses. Based on the conducted research, it was found that air temperatures maintained in the rooms of the building undergoing thermal modernization should be taken into account in the energy audit. The energy demand of the building for a room temperature of 26 °C is higher by 61% compared to the demand for the same building at the design temperature (20 °C). The innovation in the proposed approach to the economic and ecological assessment of a building is the combination of a wide range of temperatures potentially maintained in living spaces with ecological cost-effectiveness.

The transition of the energy system in the European Union (EU) from non-renewable to renewable energy aims to guarantee the energy supply, reduce greenhouse gas emissions, reduce energy costs, and lead to industrial development, growth, and occupation. The revised renewable energy directive EU/2023/2413 raises the binding renewable target for the EU in 2030 to a minimum of 42.5%. This means almost doubling the existing share of energy from renewable sources in the EU. This study is concerned with presenting state-of-the-art regarding renewable energy sources in EU countries, predicting the share of renewable energy in 2030, and investigating the relationships between this share and the reduction of greenhouse gas emissions. The results of the research indicate a significant relationship between increasing renewable energy sources and decreasing greenhouse gas emissions in the EU.

Environmental concerns and challenges are constantly increasing in recent years. These challenges affect the national economies of many countries and business strategies in companies. Environmental regulations and greater awareness of consumers in the aspect of environmental concerns force companies to develop products that can reduce harmful environmental effects. This paper is concerned with investigating the importance of factors affecting environmental innovation in the European Union (EU), and the environmental benefits obtained during the consumption or use of innovative products. Moreover, this study presents the difference in assessing the importance of factors related to introducing environmental innovations by the EU innovative and non-innovative enterprises.

A product’s impact on environmental issues in its complete life cycle is significantly determined by decisions taken during product development. Thus, it is of vital importance to integrate a sustainability perspective in methods and tools for product development. The paper aims at the development of a method based on a data-driven approach, which is dedicated to identifying opportunities for improving product sustainability at the design stage. The proposed method consists of two main parts: predictive analytics and simulations. Predictive analytics use parametric models to identify relationships within product sustainability. In turn, simulations are performed using a constraint programming technique, which enables the identification of all possible solutions (if there are any) to a constraint satisfaction problem. These solutions support R&D specialists in finding improvement opportunities for eco-design related to reducing harmful impacts on the environment in the manufacturing, product use, and post-use stages. The results indicate that constraint-satisfaction modeling is a pertinent framework for searching for admissible changes at the design stage to improve sustainable product development within the full scope of socio-ecological sustainability. The applicability of the proposed approach is verified through an illustrative example which refers to reducing the number of defective products and quantity of energy consumption.

Current environmental regulations and customer expectations force manufacturing companies to develop new products considering ecological, social, and economic issues. At present, product sustainability, time-to-market, and profit are equally important factors in the new product development process. Consequently, product design should embrace issues related to sustainability in the entire product life cycle. These issues refer to manufacturing processes, product use, and the post-use phase, including product reuse, remanufacturing, recycling or disposal. Product design considering these issues is called sustainable design or eco-design. The product development process aims to create a new product that can be a modification of existing products or be completely new for a company and market. Innovations are widely used as a key component for addressing sustainable development concerns. The role of product design and innovation in the quest for sustainability has received considerable attention among researchers and businesses in recent years. This research is a novel bibliometric analysis of sustainable product development, product design, and product innovation in the last 30 years, from 1993 to 2022. The results of data analysis embrace trends regarding the yearly number of publications devoted to sustainable product development, sustainable product design, and sustainable product innovation. Moreover, the collected publications were classified into subject areas, source titles, and source type. The bibliometric analysis reveals the most popular journals to knowledge dissemination of sustainable product development, sustainable design, and sustainable innovation. The results show that the increase of publications devoted to sustainable product design and sustainable product innovation is particularly strong in the last 5 years. Moreover, the number of publications related to sustainable product design is over ten times larger than related to sustainable product innovation.

Improving the efficiency of maintenance processes is one of the goals of companies. Improvement activities in this area require not only an appropriate maintenance strategy but also the use of a new approach to increase the efficiency of the process. This article focuses on using Six Sigma (SS) to improve maintenance processes. As an introduction, the generations of SS development are identified, and traditional and advanced analytical tools that can be useful in SS projects are reviewed. As part of the research, an example of the implementation of the SS project in the maintenance process using the DMAIC and selected advanced analytical methods, such as PCA and logistic regression, was presented. The PCA results showed that it was enough to have seven main components to keep about 84% of the information on variability. In developed logistic regression explained the impact of the individual factors affecting the availability of the machines. The identified factors and their interactions made it possible to define maintenance activities requiring improvements.

Environmental issues and sustainability performance are more and more significant in today’s business world. A growing number of manufacturing companies are searching for changes to improve their sustainability in the areas of products and manufacturing processes. These changes should be introduced in the design process and affect the whole product life cycle. This paper is concerned with developing a method based on predictive and prescriptive analytics to identify opportunities for increasing sustainable manufacturing through changes incorporated at the product design stage. Predictive analytics uses parametric models obtained from regression analysis and artificial neural networks in order to predict sustainability performance. In turn, prescriptive analytics refers to the identification of opportunities for improving sustainability performance in manufacturing, and it is based on a constraint programming implemented within a constraint satisfaction problem (CSP). The specification of sustainability performance in terms of a CSP provides a pertinent framework for identifying all admissible solutions (if there are any) of the considered problem. The identified opportunities for improving sustainability performance are dedicated to specialists in product development, and aim to reduce both resources used in manufacturing and negative effects on the environment. The applicability of the proposed method is illustrated through reducing the number of defective products in manufacturing.

: The selection of the most promising new product development projects is one of the most important decisions in a company. This decision affects the cost of failed projects, company’s profitability, and its survival. The selection of new product development projects with the greatest potential requires the evaluation criteria that reflect the adjustment of a new product to customer requirements, company’s strategy, manufacturing, and knowledge management issues. There are specified sub-criteria to identify the impact of a new product on a single area of the company’s activity, for example, on the knowledge management area. The criteria and sub-criteria are evaluated by managers, engineers (including knowledge engineers), and IT specialists, who work in departments such as research and development, sales and marketing, manufacturing, IT, and top management. Company professionals have the most updated information of the ongoing processes related to new product development. This paper is concerned with using the analytic hierarchy process (AHP) methodology to a new product screening problem, paying attention to the knowledge management perspective. So far, this perspective is neglected in the context of the decision problem of new product screening. This research develops the field of knowledge acquisition from experts towards selecting and evaluating criteria related to the potential of a new product. Knowledge acquisition refers to issues related to a new product, customer requirements, and uncertainties of project performance. Using criteria related to various areas of the company’s activity, the decision maker can identify factors significantly impacting performance of new product projects, and compare these projects with each other. Moreover, the AHP approach prioritizes criteria and sub-criteria, and as a result, it can identify areas of the company’s activity that could be improved.

Currently used decision support systems allow decision-makers to evaluate the product performance, including a net present value analysis, in order to enable them to make a decision regarding whether or not to carry out a new product development project. However, these solutions are inadequate to provide simulations for verifying a possibility of reducing the total product cost through changes in the product design phase. The proposed approach provides a framework for identifying possible variants of changes in product design that can reduce the cost related to the production and after-sales phase. This paper is concerned with using business analytics to cost estimation and simulation regarding changes in product design. The cost of a new product is estimated using analogical and parametric models that base on artificial neural networks. Relationships identified by computational intelligence are used to prepare cost estimation and simulations. A model of product development, production process, and admissible resources is described in terms of a constraint satisfaction problem that is effectively solved using constraint programming techniques. The proposed method enables the selection of a more appropriate technique to cost estimation, the identification of a set of possible changes in product design towards reducing the total product cost, and it is the framework for developing a decision support system. In this aspect, it outperforms current methods dedicated for evaluating the potential of a new product.

The selection of the most promising new product development projects is one of the most important decisions in a company. This decision affects the cost of failed projects, company’s profitability, and its survival. The selection of new product development projects with the greatest potential requires the evaluation criteria that reflect the adjustment of a new product to customer requirements, company’s strategy, manufacturing, and knowledge management issues. There are specified sub-criteria to identify the impact of a new product on a single area of the company’s activity, for example, on the knowledge management area. The criteria and sub-criteria are evaluated by managers, engineers (including knowledge engineers), and IT specialists, who work in departments such as research and development, sales and marketing, manufacturing, IT, and top management. Company professionals have the most updated information of the ongoing processes related to new product development. This paper is concerned with using the analytic hierarchy process (AHP) methodology to a new product screening problem, paying attention to the knowledge management perspective. So far, this perspective is neglected in the context of the decision problem of new product screening. This research develops the field of knowledge acquisition from experts towards selecting and evaluating criteria related to the potential of a new product. Knowledge acquisition refers to issues related to a new product, customer requirements, and uncertainties of project performance. Using criteria related to various areas of the company’s activity, the decision maker can identify factors significantly impacting performance of new product projects, and compare these projects with each other. Moreover, the AHP approach prioritizes criteria and sub-criteria, and as a result, it can identify areas of the company’s activity that could be improved.

The paper is concerned with predicting energy consumption in the production and product usage stages and searching for possible changes in product design to reduce energy consumption. The prediction of energy consumption uses parametric models based on regression analysis and artificial neural networks. In turn, simulations related to the identification of improvement opportunities for reducing energy consumption are performed using a constraint programming technique. The results indicate that the use of artificial neural networks improves the quality of an estimation model. Moreover, constraint programming enables the identification of all possible solutions to a constraint satisfaction problem, if there are any. These solutions support R&D specialists in identifying possibilities for reducing energy consumption through changes in product specifications. The proposed approach is dedicated to products related to high-cost energy use, which can be manufactured, for example, by companies belonging to the household appliance industry.

The paper presents the use of computational intelligence techniques to cost estimation and identification of possibilities of cost reduction at the early phase of new product development. Parametric estimation models are based on artificial neural networks and neuro-fuzzy systems that identify dependencies between product parameters and the costs of product development, manufacturing, and after-sales service. These dependencies are also used to search for the possibility of cost reduction through changes in designing a customized product. A problem of cost optimization is specified in terms of a constraint satisfaction problem and solved using constraint programming. All possible solutions are identified within requirements for a new product and business resources. The presented method consists of five steps: collecting data, conducting sensitivity analysis, identifying relationships, estimating costs, and identifying possible variants that ensure target costs. An example of the proposed approach refers to mass-customized products.

Scalability is a key feature of reconfigurable manufacturing systems (RMS). It enables fast and cost-effective adaptation of their structure to sudden changes in product demand. In principle, it allows to adjust a system's production capacity to match the existing orders. However, scalability can also act as a "safety buffer" to ensure a required minimum level of productivity, even when there is a decline in the reliability of the machines that are part of the machine tool subsystem of a manufacturing system. In this article, we analysed selected functional structures of an RMS under design to see whether they could be expanded should the reliability of machine tools decrease making it impossible to achieve a defined level of productivity. We also investigated the impact of the expansion of the system on its reliability. To identify bottlenecks in the manufacturing process, we ran computer simulations in which the course of the manufacturing process was modelled and simulated for 2-, 3-, 4- and 5-stage RMS structures using Tecnomatix Plant Simulation software.

The paper is concerned with predicting the total cost of a new product and searching for cost reduction at the early stages of product development. The costs of a new product development project, product promotion, production and after-sales service are predicted using parametric models. The identified relationships are also used to searching for possibilities to reduce the cost of faulty products and after-sales service through increasing prototype tests. As a result, the trade-off between the cost of a product development project and costs of production and after-sales service are sought. Company resources and product specification are formulated in terms of variables and constraints that constitute the systems approach for a problem related to cost optimization. This problem is described in the form of a constraint satisfaction problem and implemented using constraint programming techniques. An example shows the applicability of the proposed approach in the context of searching for the desirable level of the cost related to prototype tests, faulty products and after-sales service. This study develops previous research in the context of adding the cost of after-sales service to a model of total costs of a new product. Moreover, the proposed method of predicting cost has been developed towards using the similarity value to data selection.

This paper presents a novel approach to the joint proactive and reactive planning of the deliveries by a UAVs’ fleet. We develop a receding horizon based approach to a contingency planning for the UAVs’ fleet mission. We considered the delivery of goods to spatially dispersed customers, over assumed time horizon. In order to take into account forecasted weather changes which affect the energy consumption of UAVs and limit their range we propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. These rules are used in course of the contingency plans design related to the need to implement an emergency return of the UAV to the base or handling of ad hoc ordered deliveries. Due to nonlinearity of environment’s characteristics a constraint programming paradigm has been implemented and due to the NP-difficult nature of the considered planning problem, conditions have been developed that allow for the acceleration of calculations. The computational experiments have shown that the developed model is capable of providing feasible plans contingency plans of UAVs’ mission performed in dynamic environment.

The paper is concerned with estimating the cost of production and warranty in the context a new product development project. All possible variants of the trade-off between costs are sought within the company's resources and requirements for a product development project. A company and its projects can be specified in terms of variables and constraints that constitute the systems approach for a problem related to cost optimization. This problem is described in the form of a constraint satisfaction problem and implemented with the use of parametric modelling and constraint programming techniques. The paper also presents a method for estimating the cost of prototyping, faulty products in manufacturing and the after-sales stage, and simulating variants that ensure the desirable level of costs. An example shows the applicability of the proposed approach in the context of a product development project.

Today, project-oriented companies often have difficulties completing projects according to schedule. Currently used decision-support solutions allow decision makers to estimate the cost of a new product development project, and compare the estimated cost to the target cost. The proposed approach provides a framework for searching for possible project completion scenarios, which allow us to reach the target cost. The focus of this article is a project prototyping problem described in terms of a constraint satisfaction problem. The proposed method uses artificial neural networks, to identify relationships between variables, and constraint programming, to search for project completion scenarios within the company's resources and project requirements. The results of an experiment indicate that constraint programming provides effective search strategies for finding admissible solutions. Consequently, the proposed approach allows decision makers to obtain alternative project completion scenarios within the limits imposed by project requirements.