Template-Type: ReDIF-Article 1.0 Author-Name: Zhao Lan Author-X-Name-First: Zhao Author-X-Name-Last: Lan Title: Colour offset compensation method of product packaging image based on colour difference interpolation Abstract: In order to effectively improve the accuracy of image colour offset compensation and reduce the compensation time, a colour offset compensation method based on colour difference interpolation is proposed in this paper. Based on bilinear chromatic aberration interpolation algorithm, the full resolution chromatic aberration signal of image is estimated preliminarily. After image filtering, the basic colour data and grey balance data of the image are compared to obtain the colour offset. Taking colour block as the minimum unit of deviation compensation, the average value of extended colour level interpolation was added to the single colour level of the calibrated image to complete colour offset compensation. Experimental results show that the maximum compensation time of the proposed method is only 29.6 s, and the maximum MSE value of the compensation result is only 0.179, indicating that the proposed method has higher compensation accuracy. Journal: Int. J. of Product Development Pages: 1-12 Issue: 1/2 Volume: 28 Year: 2024 Keywords: chromatic interpolation algorithm; linear filtering; product packaging image; colour offset compensation. File-URL: http://www.inderscience.com/link.php?id=137763 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:1-12 Template-Type: ReDIF-Article 1.0 Author-Name: Mayara Silvestre de Oliveira Author-X-Name-First: Mayara Silvestre de Author-X-Name-Last: Oliveira Author-Name: Fernando Antônio Forcellini Author-X-Name-First: Fernando Antônio Author-X-Name-Last: Forcellini Author-Name: Jaime Andrés Lozano Author-X-Name-First: Jaime Andrés Author-X-Name-Last: Lozano Author-Name: Jader Riso Barbosa Author-X-Name-First: Jader Riso Author-X-Name-Last: Barbosa Title: Review of models and frameworks for set-based design Abstract: This paper aims to investigate the state-of-the-art in models and frameworks for set-based design and identify the main gaps and contributions in the literature. As a result, 121 models were analysed. Most models are quantitative, computational, engineering design-oriented and focus on early stages. Although the narrowing down process plays a central role in the set-based design, very little is addressed regarding its management processes. No model was found describing the inputs and outputs of the set-based design and the narrowing down process simultaneously. Thus, knowledge is dispersed and focused on specific parts. The relevance of this study relies on providing a comprehensive investigation of the state-of-the-art, identifying opportunities to advance in this study field and providing recommendations for future works seeking to support the development of new methods for implementing and managing set-based design, enabling and encouraging its adoption. Journal: Int. J. of Product Development Pages: 73-103 Issue: 1/2 Volume: 28 Year: 2024 Keywords: set-based design; lean product development; literature review; narrowing down process; concurrent engineering; design space; value; trade-off; quality function deployment; integrated product teams. File-URL: http://www.inderscience.com/link.php?id=137766 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:73-103 Template-Type: ReDIF-Article 1.0 Author-Name: Trent Owens Author-X-Name-First: Trent Author-X-Name-Last: Owens Author-Name: Christopher A. Mattson Author-X-Name-First: Christopher A. Author-X-Name-Last: Mattson Author-Name: Carl D. Sorensen Author-X-Name-First: Carl D. Author-X-Name-Last: Sorensen Author-Name: Michael L. Anderson Author-X-Name-First: Michael L. Author-X-Name-Last: Anderson Title: A formal consideration of user tactics during product evaluation in early-stage product development Abstract: Frequent and effective design evaluation is foundational to the success of any product development effort. Products used, installed or otherwise handled by humans would benefit from an evaluation of the product while formally considering both the physical embodiment of the technology, termed technology, and the steps a user should take to use that technology, termed tactics. Formal and simultaneous evaluations of both technology and tactics are not widespread in the product design literature. Although informal evaluation methods have advantages, formal methods are also known to be effective. In this paper we propose a formal method for evaluating tactics and technology simultaneously. Unlike the published literature, this evaluation involves explicitly defined tactics in the form of a written description of the actor, environment and series of steps. It also involves the use of stage appropriate, explicitly defined tactics-dependent criteria, which include criteria from a broad range of impact categories. Journal: Int. J. of Product Development Pages: 104-129 Issue: 1/2 Volume: 28 Year: 2024 Keywords: conceptual design evaluation; tactics evaluation; human-centred design; human factors; ergonomics. File-URL: http://www.inderscience.com/link.php?id=137784 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:104-129 Template-Type: ReDIF-Article 1.0 Author-Name: Jinrong Li Author-X-Name-First: Jinrong Author-X-Name-Last: Li Title: Study on evaluation method of human-computer interface quality of intelligent products based on Bayesian classification Abstract: In order to improve the accuracy and efficiency of human-computer interaction interface quality evaluation, this paper proposes an intelligent product interaction interface quality evaluation method based on Bayesian classification. An adaptive Gauss filter is introduced to adjust the colour of intelligent product interaction interface through logarithmic operator, and the intelligent product interaction interface is formally described. Bayesian classification method is used to build the quality evaluation model of intelligent product interaction interface. According to Bayesian classification probability reasoning mechanism, the quality of intelligent product interaction interface is evaluated. According to the relevant verification results, the average significance of the proposed method is as high as 95.7%, the recognition accuracy is 96.4% and the evaluation time is only 7.2 s, which has a good evaluation effect. Journal: Int. J. of Product Development Pages: 24-34 Issue: 1/2 Volume: 28 Year: 2024 Keywords: Bayesian classification; adaptive Gaussian filter; intelligent product; human-computer interaction interface; quality assessment. File-URL: http://www.inderscience.com/link.php?id=137813 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:24-34 Template-Type: ReDIF-Article 1.0 Author-Name: Chaoyong Jia Author-X-Name-First: Chaoyong Author-X-Name-Last: Jia Title: Prediction method of product market demand based on Prophet random forest Abstract: This paper proposed a prediction method of product market demand based on Prophet random forest. After analysing the workflow of Prophet model, generate the random forest and its decision-making process and then pre-process the original data of product market through the process of data filling, feature standardisation and feature mapping, providing a reliable data basis for subsequent demand prediction. Then, the optimal subset selection algorithm is used to extract the product market demand characteristics, and the demand characteristics are input into Prophet random forest to realise the prediction of product market demand. The experimental results show that the prediction accuracy of the proposed method is 0.969, and the maximum prediction time in the experiment is only 14.9 s, and the waveform trend of the predicted result is roughly the same as that of the actual value, which highlights the effectiveness of the proposed method. Journal: Int. J. of Product Development Pages: 60-72 Issue: 1/2 Volume: 28 Year: 2024 Keywords: Prophet model; random forest algorithm; optimal subset selection algorithm; product market demand; demand forecast. File-URL: http://www.inderscience.com/link.php?id=137814 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:60-72 Template-Type: ReDIF-Article 1.0 Author-Name: Ruifang Xing Author-X-Name-First: Ruifang Author-X-Name-Last: Xing Author-Name: Jingjing Feng Author-X-Name-First: Jingjing Author-X-Name-Last: Feng Author-Name: Yayun Fan Author-X-Name-First: Yayun Author-X-Name-Last: Fan Title: Fuzzy edge detection method of product packaging image based on Kalman filter Abstract: The existing fuzzy edge detection methods for product packaging images are vulnerable to noise, resulting in the quality and effect of detection results cannot meet the actual needs, and the detection time is long, which affects the work efficiency. Therefore, based on Kalman filter algorithm, this paper studies the fuzzy edge detection method of product packaging image. Firstly, singular value decomposition algorithm is used to remove the noise of product packaging image. Then, the Fourier spectrum of the product packaging image is obtained by FFT operation, and the image blur parameters are quickly identified. Finally, the image fuzzy edge is processed by Kalman filter to realise image fuzzy edge detection. The experimental results show that the detection signal-to-noise ratio of the proposed method is as high as 61.5 dB, the quality factor is as high as 0.97, and the detection time is short, only 19.7 s. It can be proved that the proposed method can effectively improve the quality and efficiency of fuzzy edge detection of product packaging image. Journal: Int. J. of Product Development Pages: 47-59 Issue: 1/2 Volume: 28 Year: 2024 Keywords: Kalman filter; singular value decomposition; FFT; product packaging image; fuzzy edge detection. File-URL: http://www.inderscience.com/link.php?id=137815 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:47-59 Template-Type: ReDIF-Article 1.0 Author-Name: Yun Xu Author-X-Name-First: Yun Author-X-Name-Last: Xu Title: Colour matching method of product interactive interface based on user experience Abstract: In this paper, a colour matching method of product interactive interface based on user experience is proposed. Firstly, the colour matching visual information of product interaction interface is collected in the adjacent block area. According to the visual information collection results, the colour matching element features of product interaction interface are extracted by wavelet transform. Secondly, the RGB feature decomposition model of colour matching elements in product interaction interface is established to complete the visual information fusion of colour matching in product interaction interface; Finally, considering the user experience, the colour matching reconstruction model of product interactive interface is constructed to realise the colour matching of product interactive interface. The experimental results show that the proposed method has high colour gamut coverage and image signal-to-noise ratio, high user experience satisfaction and good colour matching quality and effect of product interaction interface. Journal: Int. J. of Product Development Pages: 35-46 Issue: 1/2 Volume: 28 Year: 2024 Keywords: user experience; visual information fusion; wavelet transform; product interaction interface; colour matching. File-URL: http://www.inderscience.com/link.php?id=137816 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:35-46 Template-Type: ReDIF-Article 1.0 Author-Name: Chunming Yu Author-X-Name-First: Chunming Author-X-Name-Last: Yu Author-Name: Xin Jin Author-X-Name-First: Xin Author-X-Name-Last: Jin Title: The deep mining of consumer behaviour data on product network marketing platform Abstract: To overcome the problems of low accuracy of behaviour analysis results and low purchase proportion in traditional methods, a deep mining method of consumer behaviour data on product network marketing platform is proposed. Firstly, according to the nearest neighbour data distribution, the relevant subspace of consumer behaviour data of product e-marketing platform is divided, and the sparsity difference of relevant subspace data in each attribute is calculated. Then, the filtering of outlier interference data is completed by setting the difference threshold of local sparsity factor. Finally, the multi-source data mining method is used to analyse the difference between the data attribute weight and the target weight of each attribute behaviour, so as to realise the in-depth mining of consumer behaviour data. Test results show that the maximum error of consumer behaviour analysis of the design method is only 1, and the purchase proportion after secondary marketing reaches 10.2%. Journal: Int. J. of Product Development Pages: 13-23 Issue: 1/2 Volume: 28 Year: 2024 Keywords: network marketing platform; consumer behaviour data; deep mining; nearest neighbour data; local sparse factor; outlier data. File-URL: http://www.inderscience.com/link.php?id=137817 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:1/2:p:13-23 Template-Type: ReDIF-Article 1.0 Author-Name: Xia Wu Author-X-Name-First: Xia Author-X-Name-Last: Wu Author-Name: Decai Jin Author-X-Name-First: Decai Author-X-Name-Last: Jin Title: Multi-objective optimisation method for product appearance colour matching based on colour imagery Abstract: To improve the colour coordination of product appearance, the paper proposes a multi-objective optimisation method for product appearance colour matching based on colour imagery. Firstly, select the product colour image and choose the product appearance colour scheme from two perspectives: the main colour and auxiliary colour. Then, design and optimise the objective function from the perspectives of harmonic ratio and colour matching beauty. Finally, the Grey Wolf algorithm is used to optimise the two-colour parameters in the objective function. By iteratively searching in the solution space, the optimal harmonic ratio and colour beauty are obtained, thereby obtaining the best product appearance colour scheme. Experimental results show that the optimised colour scheme of this method has a harmonious proportion, and the visual effects of different colour matching areas are prominent. The colour difference values of different areas are between 1.47 and 1.60, indicating better colour coordination of the product appearance. Journal: Int. J. of Product Development Pages: 131-146 Issue: 3 Volume: 28 Year: 2024 Keywords: product appearance; optimise colour matching; colour imagery; colour selection; grey wolf algorithm. File-URL: http://www.inderscience.com/link.php?id=140147 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:3:p:131-146 Template-Type: ReDIF-Article 1.0 Author-Name: Chenhan Huang Author-X-Name-First: Chenhan Author-X-Name-Last: Huang Author-Name: Jing Zhu Author-X-Name-First: Jing Author-X-Name-Last: Zhu Title: Contrast enhancement method for product packaging colour images based on machine vision Abstract: To overcome the problems of low-image signal-to-noise ratio, poor quality and long processing time associated with traditional methods, a contrast enhancement method for product packaging colour images based on machine vision is proposed. Correction is performed for camera radial distortion, eccentric distortion and thin prism distortion. The machine vision camera with parameter correction is used to capture the product packaging colour images. Histogram equalisation is applied as a pre-processing step to the captured images. Gamma correction is then used to enhance the contrast of the pre-processed images, resulting in improved contrast of the product packaging colour images. The experimental results show that the average signal-to-noise ratio of the enhanced product packaging colour images using the proposed method is 56.73 dB. The image details are clearer and more defined, with higher saturation and contrast, and the colours are more vivid. The average processing time for contrast enhancement is 68.11 ms. Journal: Int. J. of Product Development Pages: 165-183 Issue: 3 Volume: 28 Year: 2024 Keywords: machine vision; product packaging; colour images; contrast enhancement; histogram equalisation; gamma correction. File-URL: http://www.inderscience.com/link.php?id=140148 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:3:p:165-183 Template-Type: ReDIF-Article 1.0 Author-Name: Xue Bai Author-X-Name-First: Xue Author-X-Name-Last: Bai Author-Name: Yu Zhao Author-X-Name-First: Yu Author-X-Name-Last: Zhao Author-Name: Donghui Lv Author-X-Name-First: Donghui Author-X-Name-Last: Lv Author-Name: Haichao Hu Author-X-Name-First: Haichao Author-X-Name-Last: Hu Author-Name: Huiqi Du Author-X-Name-First: Huiqi Author-X-Name-Last: Du Title: Intelligent vehicle autonomous navigation control method based on speech recognition technology Abstract: To address the issues of poor effectiveness in traditional methods of mobile path planning, low-success rate in autonomous navigation control and long response time, an intelligent vehicle autonomous navigation control method based on speech recognition technology is proposed. The method involves collecting driver's voice signals using speech recognition technology, pre-processing the collected signals with pre-emphasis, framing and windowing techniques to obtain driver instruction recognition results. Based on the driver's instructions, an intelligent vehicle grid map is generated using SLAM technology. In the generated grid map, an improved artificial potential field method is applied to plan the intelligent vehicle's movement path, and PID control algorithm is utilised to control the autonomous navigation of the intelligent vehicle. Experimental results demonstrate that the proposed method plans a shorter path for the intelligent vehicle with precise avoidance of obstacles, achieving a mean success rate of 97.01% and a mean response time of 72.75 ms. Journal: Int. J. of Product Development Pages: 147-164 Issue: 3 Volume: 28 Year: 2024 Keywords: speech recognition technology; intelligent vehicle; autonomous navigation control; SLAM technology; improved artificial potential field method; PID control algorithm. File-URL: http://www.inderscience.com/link.php?id=140149 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:3:p:147-164 Template-Type: ReDIF-Article 1.0 Author-Name: Krishan Gopal Author-X-Name-First: Krishan Author-X-Name-Last: Gopal Author-Name: Vikram Singh Author-X-Name-First: Vikram Author-X-Name-Last: Singh Author-Name: Somesh Kumar Sharma Author-X-Name-First: Somesh Kumar Author-X-Name-Last: Sharma Title: Evolving role of multi-agent technology in product design and development in manufacturing industry using FMCDM techniques Abstract: The manufacturing industry nowadays faces an intensely competitive environment because of market volatility, increasing global competition and technological development. To address these challenges, manufacturing organisations should reconfigure the existing Product Design and Development (PDD) processes by integrating them with Multi-Agent Technology (MAT). In this direction, the paper aims to evolve the role of MAT in PDD by using the Fuzzy Multi-Criteria Decision-Making (FMCDM) techniques. To achieve this objective, a comprehensive literature review identified six factors of PDD and 38 variables of MAT. These findings were then used to construct the conceptual framework for this research. The FMCDM technique utilised in the study evolves the ranking, influence and inter-relationship among the variables. This analysis reveals that concept development, system-level design, testing and refinement are the most significant factors assisted by the feasibility agent, design managing agent, cost estimation agent and manufacturing agent. The study empowers manufacturers by enabling them to develop new, innovative and more complex products with lower development costs, higher efficiency and competitive quality. Journal: Int. J. of Product Development Pages: 184-226 Issue: 3 Volume: 28 Year: 2024 Keywords: product design and development; multi-agent technology; FAHP; fuzzy analytic hierarchy process; FDEMATEL; fuzzy decision-making trial and evaluation laboratory; technique for order preference by similarity to ideal situation; FTOPSIS. File-URL: http://www.inderscience.com/link.php?id=140160 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:3:p:184-226 Template-Type: ReDIF-Article 1.0 Author-Name: Hua Song Author-X-Name-First: Hua Author-X-Name-Last: Song Title: Product image modelling optimisation design method based on improved support vector machine Abstract: In order to make the image modelling of the product more in line with the design goals, this paper proposes an optimised design method for product image modelling based on improved support vector machines. Firstly, construct the ontology triplet of product image modelling and extract the lexical features of image modelling. Secondly, using chaos algorithm and particle swarm optimisation algorithm to improve support vector machine to more accurately capture product appearance features. Finally, based on the extraction results of product appearance features, simulated annealing algorithm was introduced as an optimisation tool to solve the optimisation design problem of product image modelling, achieving efficient optimisation of product image modelling. The experimental results show that for the four-door sedan, the target vocabulary scores of the image modelling method in this article all exceed 0.9, and the highest aesthetic score of the image modelling design reaches 96.67 points. Journal: Int. J. of Product Development Pages: 227-240 Issue: 4 Volume: 28 Year: 2024 Keywords: improving support vector machines; product image modelling; optimise design; simulated annealing algorithm. File-URL: http://www.inderscience.com/link.php?id=143257 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:4:p:227-240 Template-Type: ReDIF-Article 1.0 Author-Name: Chunyan Liu Author-X-Name-First: Chunyan Author-X-Name-Last: Liu Title: Fuzzy decision tree based online precision marketing method for brand products on the internet Abstract: In order to solve the problems of poor user satisfaction, low user purchase rate and low recommendation accuracy in current brand product marketing methods, this paper proposes an online precision marketing method for brand products based on fuzzy decision trees. Firstly, collect and obtain user characteristics of brand products driven by internet data; secondly, construct a user influence relationship model and a user preference and interest model for the product; again, classify brand product data features based on fuzzy decision trees; finally, precise online marketing of brand product users is achieved through cosine similarity calculation. The experimental outcomes demonstrate that the marketing satisfaction achieved by the approach introduced in this article consistently exceeds 92%, reaching a peak user purchase rate of 52.18% and attaining a maximum accuracy of 95.08% in product recommendations. The approach outlined in this article can significantly enhance the efficacy of data-driven online precision marketing strategies for brand products. Journal: Int. J. of Product Development Pages: 241-256 Issue: 4 Volume: 28 Year: 2024 Keywords: fuzzy decision tree; online marketing; user characteristics; interest model; precision marketing. File-URL: http://www.inderscience.com/link.php?id=143258 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:4:p:241-256 Template-Type: ReDIF-Article 1.0 Author-Name: Fan Wu Author-X-Name-First: Fan Author-X-Name-Last: Wu Author-Name: Huaxi Chen Author-X-Name-First: Huaxi Author-X-Name-Last: Chen Title: Colour matching design method in product visual communication based on grey relational analysis Abstract: Colour matching in product visual communication results in high colour differences due to insufficient consideration of the degree of correlation between colours. Therefore, a colour matching design method in product visual communication based on grey relational analysis is studied. Firstly, 201 colours were selected as initial samples, and fuzzy processing techniques were used to combine colour merging and noise removal to screen colours. Then, it introduces the expected value of colour imagery, adjust weights and the initial colour scheme is design. Finally, the grey correlation analysis improved by the tomographic analysis method is used to determine the weighted grey correlation of colours and optimise the initial colour scheme. The colour matching of this method meets the requirements with the highest product colour difference value of 1.63 and a consensus degree of 0.85 according to the experiment. It improves the colour coordination of the product and conforms to user image preferences. Journal: Int. J. of Product Development Pages: 257-271 Issue: 4 Volume: 28 Year: 2024 Keywords: product colour matching; visual communication; colour scheme design; colour screening; grey correlation analysis; weighted grey-scale correlation. File-URL: http://www.inderscience.com/link.php?id=143260 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:4:p:257-271 Template-Type: ReDIF-Article 1.0 Author-Name: Yonghua Li Author-X-Name-First: Yonghua Author-X-Name-Last: Li Title: Optimal styling method of product appearance considering users' emotional preferences Abstract: To improve user satisfaction with product styling, this paper proposes a product styling optimisation design method that takes into account user emotional preferences. Firstly, obtain a data set of target product shape image samples and classify the product shape image samples; secondly, based on the Apriori algorithm, obtain the frequent itemsets of the target product's shape image samples for users, and mine their perceptual preference features; once again, set the preference feature attribute content of the styling image samples, and classify the user's perceptual preference feature attributes based on Naive Bayes; finally, the fitness of each design scheme is calculated using the differential bee colony algorithm, and the product shape optimisation design is achieved through iterative optimisation. The experimental results show that using the proposed method, the user satisfaction rate is over 87%, and the user's product purchase intention score remains above 7 points. The application effect is good. Journal: Int. J. of Product Development Pages: 272-287 Issue: 4 Volume: 28 Year: 2024 Keywords: emotional preferences; product appearance; Apriori algorithm; Naive Bayes differential bee colony algorithm. File-URL: http://www.inderscience.com/link.php?id=143261 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:4:p:272-287 Template-Type: ReDIF-Article 1.0 Author-Name: Lu Zhang Author-X-Name-First: Lu Author-X-Name-Last: Zhang Author-Name: Ruixue Dong Author-X-Name-First: Ruixue Author-X-Name-Last: Dong Title: The application of AI technology to upgrade retailers' traditional marketing means Abstract: In order to improve the conversion rate of users' purchase and the personalisation of marketing push, the application of AI technology in upgrading traditional marketing methods of retailers was studied. Firstly, it analyses the limitations of traditional retailers' marketing methods. Secondly, aiming at the existing limitations, AI technology is introduced to upgrade marketing means, big data analysis technology is used to mine user behaviour data, collaborative filtering algorithm in machine learning algorithm is used to recommend products individually, and natural language processing technology is used to evaluate user satisfaction. Finally, the application effect of this method is evaluated through a case study. The results showed that the conversion rate of this method is high, with the highest value of 48.3% and the highest score of personalisation degree of 1.0, which showed that it can predict users' purchasing behaviour more accurately and provide more personalised recommendation results. Journal: Int. J. of Product Development Pages: 288-300 Issue: 4 Volume: 28 Year: 2024 Keywords: AI technology; retailer; marketing means; user behaviour data; collaborative filtering algorithm; satisfaction evaluation. File-URL: http://www.inderscience.com/link.php?id=143262 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:4:p:288-300 Template-Type: ReDIF-Article 1.0 Author-Name: Yang Yang Author-X-Name-First: Yang Author-X-Name-Last: Yang Title: Study on strategies for enhancing enterprise management decision-making ability facing market demand Abstract: In order to help enterprises achieve strategic goals, meet customer demands and adapt to market changes, this article conducted a strategies for enhancing enterprise management decision-making ability facing market demand. It analyses the relationship between market demand and enterprise management decision-making, elaborating on the importance and necessity of enhancing enterprise management decision-making abilities from multiple perspectives. The article also identifies existing issues in enterprise management decision-making, and proposes targeted strategies such as strengthening market research and analysis capabilities, enhancing information and data management capabilities, simplifying decision-making processes, improving the decision-making abilities of management teams, fostering a culture of cross-departmental teamwork and establishing a learning organisation to enhance enterprise management decision-making abilities. The analysis results show that the maximum recall rate of the proposed method is 98.3%, the average management decision time is 39.2 days and the average management decision fit is 0.96. Journal: Int. J. of Product Development Pages: 301-322 Issue: 4 Volume: 28 Year: 2024 Keywords: market demand; enterprises; management decision-making ability; strategies for enhancing; decision-making process; culture of cross-departmental teamwork; learning organisation. File-URL: http://www.inderscience.com/link.php?id=143263 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijpdev:v:28:y:2024:i:4:p:301-322 Template-Type: ReDIF-Article 1.0 Author-Name: Zhang Yan Author-X-Name-First: Zhang Author-X-Name-Last: Yan Author-Name: Tobias Larsson Author-X-Name-First: Tobias Author-X-Name-Last: Larsson Author-Name: Andreas Larsson Author-X-Name-First: Andreas Author-X-Name-Last: Larsson Title: Future innovation framework (FIF) for value co-creation of smart product-service system design in a global automotive manufacturing company Abstract: The Product-Service Systems (PSS) methodology faces new challenges as digital servitisation drives product-oriented companies to integrate digital services into their offerings. A value co-creation strategy and global collaborative innovation are now essential for these companies to develop smart PSS models. This study introduces the Future Innovation Framework (FIF) which is proposed as a mechanism to facilitate value co-creation in smart PSS design, specifically tailored for global manufacturing contexts. Through qualitative analysis and literature review, the research investigates collaboration among key stakeholders, defines a structured smart PSS design process, and demonstrates how value co-creation can enhance design outcomes. The proposed FIF framework, applied to a Smart Electric Vehicle (SEV) case with Volkswagen, supports early-stage collaborative innovation and informed decision-making. This paper discusses the practical implications, challenges, and future opportunities of implementing FIF in industrial smart PSS design. Finally, the potential for adapting FIF across various industry sectors is explored. Journal: Int. J. of Product Development Pages: 1-29 Issue: 5 Volume: 28 Year: 2024 Keywords: smart product-service system; FIF; future innovation framework; PSS design process; value co-creation; automotive manufacturing company. File-URL: http://www.inderscience.com/link.php?id=142760 File-Format: text/html File-Restriction: Open Access Handle: RePEc:ids:ijpdev:v:28:y:2024:i:5:p:1-29