Individual Customer Preferences Research Articles

Utilizing machine learning algorithms to enhance predictive analytics in customer behavior studies

Machine learning (ML) algorithms have revolutionized the field of predictive analytics, particularly in understanding and anticipating customer behavior. This review explores how these advanced algorithms are utilized to enhance predictive analytics in customer behavior studies, driving more informed and strategic decision-making processes within businesses. Predictive analytics leverages historical data to predict future outcomes, and when combined with ML, it becomes significantly more powerful and accurate. ML algorithms can analyze vast amounts of data, identifying patterns and trends that would be impossible for humans to discern. These algorithms, including decision trees, neural networks, and support vector machines, can handle complex and nonlinear relationships within data, making them exceptionally well-suited for customer behavior studies. The application of ML in predictive analytics begins with data collection from various sources, such as transaction records, social media interactions, and customer feedback. This data is then preprocessed to ensure quality and relevance before being fed into ML models. Through techniques like clustering, classification, and regression, ML algorithms can segment customers, predict purchasing behaviors, and identify potential churners. One significant advantage of using ML for predictive analytics in customer behavior is the ability to deliver personalized experiences. By predicting individual customer preferences and needs, businesses can tailor their marketing efforts, product recommendations, and customer service interactions, thereby enhancing customer satisfaction and loyalty. For instance, e-commerce platforms use ML-driven predictive analytics to suggest products that a customer is likely to purchase based on their browsing and buying history. Moreover, ML algorithms continuously learn and improve from new data, allowing for real-time updates and more accurate predictions over time. This dynamic nature is crucial in today’s fast-paced market environments where customer preferences and behaviors can change rapidly. In conclusion, utilizing ML algorithms in predictive analytics significantly enhances the ability to understand and predict customer behavior. This integration not only helps businesses optimize their strategies and operations but also fosters deeper customer relationships through personalized and timely engagements. As ML technology continues to evolve, its impact on predictive analytics and customer behavior studies is expected to grow, offering even more sophisticated and actionable insights.

Innovative strategic marketing practices to propel small business development and competitiveness

In today's dynamic and competitive business environment, small businesses face unique challenges in establishing their presence and achieving sustainable growth. This paper explores innovative strategic marketing practices that can propel small business development and enhance competitiveness. The primary focus is on leveraging digital marketing, personalization, customer engagement, data analytics, and strategic partnerships. Digital marketing has revolutionized how small businesses reach and engage with their target audiences. By utilizing social media platforms, search engine optimization (SEO), content marketing, and email marketing, small businesses can create cost-effective and highly targeted campaigns. These strategies not only increase brand visibility but also foster customer loyalty through consistent and relevant communication. Personalization is another key strategy that drives small business competitiveness. By tailoring marketing messages and offers to individual customer preferences and behaviors, businesses can create more meaningful and impactful interactions. This approach enhances customer satisfaction and encourages repeat business, contributing to long-term growth. Effective customer engagement is critical for small businesses to build strong relationships and foster community. Utilizing interactive content such as polls, quizzes, and live chats on social media and websites can create a more engaging and immersive customer experience. Additionally, loyalty programs and personalized incentives can strengthen customer retention and advocacy. Data analytics plays a crucial role in informing and optimizing marketing strategies. By analyzing customer data, market trends, and campaign performance, small businesses can gain valuable insights that drive strategic decision-making. This data-driven approach allows for more precise targeting, efficient resource allocation, and continuous improvement of marketing efforts. Strategic partnerships and collaborations with other businesses, influencers, and community organizations can also enhance marketing effectiveness. These partnerships can expand reach, provide access to new customer segments, and enhance credibility. Co-marketing initiatives, joint events, and cross-promotions are examples of how small businesses can leverage strategic alliances for mutual benefit. In conclusion, innovative strategic marketing practices, including digital marketing, personalization, customer engagement, data analytics, and strategic partnerships, are essential for propelling small business development and competitiveness. By adopting these practices, small businesses can effectively navigate the competitive landscape, achieve sustainable growth, and secure a strong market position in today's ever-evolving business environment.

Optimizing Social Media Promotion Strategy to Increase Customer Retention Rate (CRR) with GKG Customer Engagement

In the digital age, businesses are increasingly relying on social media platforms to engage with their customers and foster brand loyalty. This paper presents a comprehensive study aimed at optimizing social media promotion strategies to enhance Customer Retention Rate (CRR) while utilizing the GKG (Get Keep Growth) Customer Engagement framework. By examining the interplay between social media promotion tactics and customer engagement metrics, we investigate how businesses can leverage data-driven insights to improve customer retention. Our research showcases the importance of tailoring social media campaigns to individual customer preferences and behaviour, ultimately leading to increased customer satisfaction and loyalty. The results of the analysis of the development of the Customer Retention Rate graph were produced on FMIPA social media with an average CRR in the base case of 71%. Through a combination of data analysis and case studies, we provide actionable recommendations for businesses seeking to maximize the effectiveness of their social media promotion efforts and elevate their CRR.

Customer personality analysis and clustering for targeted marketing

A customer’s personality has been stated to be a key determinant in his or her purchasing behavior. Therefore, in today's highly competitive market, understanding customer behavior and preferences is very important for businesses aiming to stay ahead. This research presents a customer personality analysis and clustering of consumers using machine learning into different consumer groups, this kind of clustering is key for targeted marketing strategies. The machine learning technique used in this work is the k-means machine learning clustering algorithm which divides a set of n observations into k clusters with the aim of designating each observation as a representative of a cluster. The model revealed insightful patterns and correlations between customer characteristics and their purchasing behavior. Through comprehensive data analysis and feature engineering, the model identifies key personality traits such as extroversion, openness, conscientiousness, agreeableness, and neuroticism, based on customer interactions, demographics, and psychographic data. The dataset that was used for this work was obtained from kaggle, it originated from different sources such as customer relationship management (CRM) systems, e-commerce platforms, social media platforms, surveys. The dataset comprises information on "people," "products," and "promotion," aiming to understand customer behavior, preferences, and responses to various marketing efforts. The output of this work provides businesses with actionable insights to tailor marketing campaigns and product offerings to individual customer preferences, thereby enhancing customer satisfaction and maximizing profitability.

Study of the effect of bio-inspired surface texture on the shear strength of bonded 3D-printed materials: Comparison between stainless steel and polycarbonate joints

Additive Manufacturing (AM) is one of the important technological pillars of Industry 4.0. Thanks to its tool-free manufacturing and extreme flexibility, AM has the potential to respond to individual customer preferences with a customized final product. Nevertheless, the size of 3D printed products is limited by the size of the build chamber of commercially available printers. In addition, deformations, stability, tolerances, and especially cost are highly dependent on the maximum size of the parts. Therefore, a larger final product can only be realized as an assembly group. Therefore, adhesive bonding appears to be an attractive solution for assembling parts made by AM. In this paper, a comparison of the influence of surface texture on the shear strength of 3D-printed metallic and polymeric adhesive joints was performed. A fish scale (FS) bio-inspired surface texture was chosen to examine how surface patterning influences the strength of joints. Fused deposition modeling (FDM) and selective laser melting (SLM) techniques were used to produce polycarbonate (PC) and stainless steel (17-4 PH) specimens, respectively. The effect of bio-surface and material type on the shear strength of adhesive joints was assessed using the ASTM D4501 standardized block shear testing method. The morphological analysis revealed that FDM exhibits reduced resolution and accuracy, making it less suitable for the additive manufacturing of complex surface features in 3D-printed parts when compared to SLM technology. In addition, bio-surface texturing significantly improved the wettability of stainless steel, requiring the synergy of higher surface free energy and increased surface roughness (FS bio-surface) for super-hydrophilicity, while a combination with lower surface free energy (PC material) and the same surface roughness resulted in hydrophobic behavior. On the other hand, the bio-surface texture greatly enhanced the shear strength (+31%) of stainless steel specimens, compared to PC specimens, due to the increase of physical adsorption forces and the enhancement of the mechanical interlocking effect. The analysis of variance (ANOVA) results highlight the predominant influence of material type on surface wettability (84%), while surface texture has a more pronounced impact on the shear strength of bonded joints (71%).

A Case Study of Customer Relationship Management in J&K Grameen Bank Samba of J&K

This study aims at making contribution to the literature on understanding the relationship between customer relationship management, customer satisfaction and customer loyalty in context to J&K Grameen Bank. This study attempts to investigate the dimensions of customer relationship management (CRM) and Customer satisfaction (CS) that influence customer loyalty (CL). Today the banking sector is making significant contribution to the economic development and redefining the consumption habits of Indians. The constantly changing consumer attitudes and buying behavior is making it difficult for bankers and service providers to take their customers for granted. The highly demanding and knowledgeable consumers are compelling banks to stock a huge product range, offer attractive discounts, in an aesthetically set up environment and so on- all in the hope of having a loyal customer base. But high lead times, seasonal fluctuations, evolving consumer preferences are some of the issues creating a stiff completion in the business environment. So, banks and service providers have realized that it is very essential to work towards getting into customer relationship management programmes. For this, banks and service providers are working out ways to understand the opportunity and having preferences of individual customers that chart out strategies and programs so as to develop a base of loyal customers who will patronize the banks and service providers frequently. The study is based on descriptive and exploratory style using primary data.

Personalization in Marketing: Customizing the Customer Experience for Greater Engagement

In the realm of marketing, personalization refers to the strategic approach of designing and creating content and products that align with the specific preferences of individual customers. By tailoring offerings to customer preferences, businesses can effectively reduce customer fatigue and the time required for decision-making, ultimately alleviating cognitive load. Despite its significance, the existing body of literature on personalized marketing lacks a comprehensive review that consolidates the intellectual structure of the field. By thoroughly examining 383 publications, this study uncovers trends in publication and citation, identifies the most prolific authors, journals, and publications, and highlights six major themes that define the knowledge base of personalized marketing. These themes include personalized recommendations, personalized relationships, the personalization-privacy paradox, personalized advertising, the concept and discourse of personalization in marketing, and customer insights in personalized marketing. In conclusion, this study suggests future research directions for personalized marketing, emphasizing the exploration of new-age technologies such as artificial intelligence, big data, blockchain, the Internet of Things, and wearables. By leveraging these technologies, businesses can discover innovative methods to curate personalized experiences across both online and offline channels.

Application of Artificial Intelligence in E-commerce

The rapid development of artificial intelligence in recent years is directly reflected in commercial activity, where a large number of transactions are carried out, a significant amount of data is formed and processed about deliveries made, sales made, and customers served. This is also true of e-commerce, which is establishing itself as the dominant non-store form of commercial exchange. The rise of artificial intelligence is changing the way businesses approach e-commerce. The algorithms and systems powered by it enable e-retailers to make data-driven decisions, automate processes and offer personalized experiences that match the unique preferences of individual customers. The purpose of this paper is to present the essence, advantages and guidelines for the application of artificial intelligence in e-commerce, and on this basis, the challenges for online merchants in its implementation are presented. The paper provides a brief analysis of data from Statista on the areas where artificial intelligence is helping e-commerce.

Mobile parcel lockers with individual customer service

AbstractThe ongoing growth of e‐commerce deliveries has led to a significant increase in last‐mile delivery volumes. New technologies are being investigated to provide these deliveries efficiently and in a customer‐friendly manner. A common practice is to use fixed parcel lockers (FPLs) to make deliveries independent from the presence of the customer as is the case in attended home deliveries (AHDs). FPLs are usually installed at key locations in cities, and customers can collect their package at any time once it has been delivered to this particular location. Mobile parcel lockers (MPLs) represent a new idea: they can be parked for temporary collection of items at different locations, keeping the pickup distance to the customer short and avoiding high infrastructure costs. However, customers need to collect their parcels within a restricted time window. This service is supposed to become especially efficient through autonomously operating vehicles that can move MPLs at low costs. In this article, we introduce the heterogeneous locker location problem to study the effects of fleets combining two of the services—FPLs, MPLs, AHDs—within one framework. In our comparison, we consider that customers may have different expectations regarding their maximum pickup distance as well as their temporal flexibility in accepting deliveries. A fixed fleet is applied to maximize the number of customers served, respecting individual customer preferences in terms of pickup distances and time windows. We evaluate the different delivery services regarding managerial insights on service quality and efficiency. In the experiments, we analyze the impact of structural demand differences and different fleet sizes, as well as the operational fleet utilization and the individual customer experience. Results show the potential to increase the number of customers served by about 14%–19% through the use of MPLs while considering individual customer preferences.

Enhancing Customer Experience in E-Commerce Through AI-Powered Personalization: A Case Study

Aim: This study is intended to engage in the in-depth study of AI-enabled personalization tactics on the quality of customer experience as competition informs the e-commerce environment. The research employs a case study assessment of a prominent world-wide retailer with the primary aim of revealing the dominant influence of cutting-edge AI personalisation technology in actual applications. Methods: The study applied the mixed-methods research, which was made up of quantitative as well as qualitative research techniques such as sound data analysis and field research approaches to arrive at a comprehensive apprehension of the phenomenon. Data science system features such as studying key customer behaviour metrics, conversions, average order, customer value, and satisfaction, appreciate the company's case from superior data systems. The qualitative side of this study was indicated through the revelation of in-depth interviews that were done with a group of educated customers and an extensive online survey that was designed to capture their preferences, opinions, and perceptions in relation to the personalized shopping experience(Gao & Liu, 2022b). Results: The result shown a superlative boost in a lot of customer experience metrics, such as loyalty, proactivity, predictability, and automation, after the execution of the advanced AI personalization engine. What need to be prominently mentioned is an increase of conversion rate which saw a hike by 25% endorsing the fact that now, more customers on the site would complete their purchase process. Also, there was a remarkable 17% increase in the average order value, showing that personalized suggestions and tailor-made experiences had an impact on how customers would spend more per time they place an order. Customer’s life-time value (CLV) was extended by 12%. User stayed loyal and engaged for the longer time period. This probably was the most impressive outcome of the brand's satisfaction scores, as the jumps of them by 22% show the improvement of the customer experience overall. The analysis of qualitative data reveals that consumers experience genuine appreciation of the personalized shopping experience they get from being offered items that match their personal attributes such as the in-depth interpretations of customer likes and dislikes which the algorithm platform uses, quick discovery of products with minimal scrolling, saving of the time used in the search phase, and emotional closeness to freedom/ your identity, in the case of fashion (Raji et al., 2021). Conclusion: This comprehensive case study provides compelling evidence of the transformative potential of AI-powered personalization in enhancing customer experience within the e-commerce landscape. By leveraging advanced machine learning algorithms and vast customer data repositories, businesses can deliver highly tailored content, product recommendations, and optimized search results that resonate deeply with individual customer preferences and needs. The findings demonstrate that implementing AI personalization strategies can drive improved customer engagement, increased sales, and foster long-term loyalty, ultimately conferring a significant competitive advantage in the rapidly evolving e-commerce industry.

Maximizing Online Revisiting and Purchasing: A Clickstream-Based Approach to Enhancing Customer Lifetime Value

ABSTRACT Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers’ revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer’s website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level.

Utilizing additive manufacturing and mass customization under capacity constraints

Additive manufacturing (AM), originally used for prototyping, is increasingly adopted for custom final part production across different industries. However, printing speed and production volume are two barriers for the adoption of AM for product customization at large scale. Nevertheless, manufacturers could aim to combine the benefits of AM for product customization with traditional mass customization (MC) technologies over the product life cycle (PLC). This approach is showcased in our paper as a manufacturing opportunity and is addressed via a non convex-concave optimization model that considers a monopolist manufacturer producing horizontally differentiated products at scale. To satisfy individual customer preferences under capacity considerations, the firm jointly decides on the inventory, production quantity, product variety, optimal technology-switching times (between AM and MC) and pricing strategy. Our approach can be implemented by decision-makers to leverage customer-centricity and benefit from this novel hybrid manufacturing practice. By deriving a closed-form solution for the production quantity based on an adaptive inventory policy, the resulting optimization problem is solved using the Sample Average Approximation framework grounded by analytical results. Our results demonstrate that the new usage of AM with MC can benefit a manufacturer for customer-centric driven strategies. Significant profit improvements can be achieved with an AM–MC–AM technology-switching scenario under certain capacity conditions and with an increasing-decreasing pricing strategy. Our results also indicate that the benefits of pricing flexibility are highest when capacity is unlimited or when the firm does not hold inventory. Under capacity constraints, a simple decreasing pricing policy combined with inventory performs very well.

USING SOCIAL MEDIA ANALYTICS IN THE BANKING INDUSTRY TO DRIVE BUSINESS ADVANTAGE

This paper highlights the opportunities that social media can offer banks and explores the ways that “banks can leverage this medium to enhance customer engagement” [8]. The widespread use of social media in business has redefined the way enterprises approach customer engagement and business growth strategies. With social media becoming a mainstream form of communication, people are providing detailed information about themselves, their preferred products and services, their daily activities, and other important aspects of their personality. This information has the power not only to influence their personal network, but also to help “companies understand consumer preferences and customize their product and service offerings accordingly. In a market characterized by economic” uncertainty “and increased competition, an undivided focus on customer acquisition and retention has become the primary objective of the banking and financial services industry. Banks compete to reach out to potential customers with personalized service offerings and products that cater to individual customer preferences and investment behaviors. However, acquiring information on target customers, their choices and financial preferences is a challenging task given the enormous amount of data that must be gathered, processed, analyzed and interpreted accurately to drive effective business decisions. This is where social analytics come in. Social analytics involve aggregating unstructured data from social networks and analyzing” [15] it objectively to help enterprises make effective business decisions, create better channels of communication with their customers and other key stakeholders, increase cross-selling opportunities, provide an enhanced customer experience, and offer targeted solutions. This paper explores the power of social media and how it can be used by banks “to gain an advantage over their competitors by providing” [16] improved products and services, thereby making the customer experience easy and responsive.

“We Missed You!”: A joint optimization strategy of appointment window and reminder sending

Customized service, specifically designed to meet individual customer preferences, is a rising consumption pattern along with the progress of socialization and urbanization. The customization nature requires service lead time, during which providers reserve required resources and prepare for individualized service delivery. As a result, when customers fail to show up (customer no-shows), it is impossible to use that time slot right away to serve another customer who can have a different set of service requirements. The vacant time slot and preparations will be forfeited, leading to significant indirect costs and opportunity loss. Meanwhile, service lead time makes it unrealistic for customized service providers to adopt some of the extant optimal scheduling strategies designed for traditional services, such as overbooking, open-access scheduling, and allowing walk-ins. In this study, we recognize the difference between customized services and traditional services in our analytical model formulation and jointly optimize the utilization of customized services from two aspects: adjusting the appointment windows and subsequently the reminder sending time. Optimal reminder sending time depends on when customers reserve their spots during the optimal appointment window. The earlier they reserve, the earlier they should be reminded. We further adopt a dynamic programming algorithm to calculate the optimal appointment window and the optimal reminder sending time in the closed form. Our proposed joint optimization strategy performs significantly better than two extant strategies: optimal appointment window only, optimal appointment window and sending the customer a reminder one day before the appointment window is closed. Finally, we conduct the sensitivity analysis and comparative analysis. We find that our joint optimization strategy is most beneficial to these customized services that have relatively short service lead time yet face the challenge of high customer no-show profiles and a low customer demand.

Toward Fair Recommendation in Two-sided Platforms

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. However, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed FairRec algorithm guarantees Maxi-Min Share of exposure for the producers, and Envy-Free up to One Item fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus ) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.

Keeping it personal while growing the business: the German Mittelstand approach

PurposeFor firms that depend on personalized management as a key element of their competitive advantage, maintaining personalized management in the face of sustained growth presents a particular challenge. The purpose of this paper is to examine how firms in the Germanic Mittelstand have endeavored to “scale up” personalization.Design/methodology/approachDifferent ways of scaling up personalization are explained with examples.FindingsThe concept of personalization need not just concern customers, in contrast to conventional treatments of personalization. Mittelstand firms illustrate the scaling up of personalization to target stakeholder groups other than just customers.Research limitations/implicationsIn recent years, personalization has come to refer to the customization of products to the preferences of individual customers. In contrast, a neglected but important topic is personalization of and within firms. Personalization refers to imbuing a firm with the personal qualities of individual personalities indissociable from management of the company.Practical implicationsMethods for scaling up personalization need to be truly scalable to be effective. Methods that only enable a one-time enlargement in the scope of the personalized business are liable to fail in the longer run.Originality/valueBy examining personalization as an important characteristic of small to medium-sized firms that they wish to maintain as they grow larger, this study highlights a little noticed dimension of Mittelstand growth strategies – and endeavors to bring personality back into research on “personalization.”

Planning for a Resilient Home Electricity Supply System

Resilience of power systems is already a key issue that is getting frequent attention all over the world. It is useful to analyze resilience issues not only for bulk supply, but at all levels including at a customer level. This is because distributed energy resources can play a prominent role in enhancing resilience. Although the literature on planning models, tools and data for bulk supply and distribution systems have expanded in recent years, customer-centric planning, e.g., for an individual household, is yet to receive adequate attention. Although solar PV and battery storage at a household level have been analyzed, how these resources can be optimally combined, together with grid supply, from a resilience perspective is the focus of this study. The study demonstrates how a conceptual framework can be developed to show the trade-off between system costs and resilience including its dimensions such as duration, depth and frequency of service outages. A planning model is developed that incorporates multiple facets of resilience and individual customer preferences. The model considers power system resilience explicitly as a constraint. The model is implemented for a household level case study in Miami, Florida. The results show there are complex trade-offs among different dimensions of resilience. The study demonstrates how combined resilience metrics can be formulated and evaluated using the proposed least-cost planning model at a household level to optimize grid supply together with solar, battery storage and diesel generators. The model allows a planner to directly embed a resilience standard to drive the optimal supply mix. These concepts and the modeling construct can also be applied at other levels of planning, including community level and bulk supply system planning.

Benefiting from additive manufacturing for mass customization across the product life cycle

Additive manufacturing (AM) was initially designed for prototyping and product personalization, where high production quantities were not required. Now, it is also implemented for final part production to achieve cost-effective mass customization (MC). Thanks to its tool-less production and extreme flexibility, AM has the potential to address individual customer preferences with custom final parts. Nevertheless, despite its increased competitiveness, AM is not yet likely to replace traditional MC systems, but it can complement them, improving manufacturing efficiency. To broaden our understanding of how AM can complement traditional manufacturing systems, we develop an exploratory quantitative model. First, we leverage customer-centricity in a novel time-varying locational choice model of heterogeneous customers, coupling the Bass and the Hotelling–Lancaster models. Then, we investigate customer-centric marketing and operations decisions, exploring technology-switching scenarios that interchange AM with MC across the product life cycle (PLC). We formulate and solve an optimization problem by jointly deciding on technology-switching times, pricing, and product variety strategies to maximize a manufacturer’s profit and meet individual customers’ diverse and evolving needs. We use a validated Sample Average Approximation approach for the numerical solution of our non-convex optimization problem. Testing different pricing strategies, we show that decreasing and flexible trajectories are optimal. We derive analytical properties for the optimal pricing policy and demonstrate that a manufacturer can benefit from interchanging AM and MC across the PLC, in particular by adopting an AM-MC-AM scenario.

Incorporating customer personalization preferences in open product architecture design

Open product architecture is a key enabler for product personalization, as it allows the integration of personalized modules in a product architecture to satisfy individual customer needs and preference. A critical challenge for integrating personalized modules into a product architecture is determining the optimal assembly architecture when considering market expectations and manufacturing constraints. In this paper, an optimization method is proposed for determining the personalized product design architecture that incorporates individual customer preferences. First, a decision hierarchy is presented to describe the integrated design decisions of the product architecture, including product variety determination, module variant selection, and personalized module configuration. Next, a profit model is formulated as an overall performance metric that incorporates customer preferences and manufacturing cost. The systematic patterns and randomness of diverse customer preferences are modeled by combining conjoint analysis and market segmentation with a multivariate normal mixture model. Individual customer product utilities in the target market and their product purchase intent probability are estimated through Monte-Carlo simulation, which is incorporated into the profit calculation. Manufacturing limitations on processes and materials are included as they influence manufacturer’s planning on candidate module variants and production strategies of personalized modules. These models are used to determine a product family architecture that maximizes profit by optimally determining its offering of product variants, module combinations, and personalized module configuration through a genetic algorithm. The proposed method is demonstrated by a personalized bicycle architecture design example.

Benefiting from Additive Manufacturing for Mass Customization across the Product Life Cycle

Additive manufacturing (AM) was initially designed for prototyping and product personalization, where high production quantities were not required. Now, it is also implemented for final part production to achieve cost-effective mass customization (MC). Thanks to its tool-less production and extreme flexibility, AM has the potential to address individual customer preferences with custom final parts. Nevertheless, despite its increased competitiveness, AM is not yet likely to replace traditional MC systems, but it can complement them, improving manufacturing efficiency. To broaden our understanding of how AM can complement traditional manufacturing systems, we develop an exploratory quantitative model. First, we leverage customer-centricity in a novel time-varying locational choice model of heterogeneous customers, coupling the Bass and the Hotelling-Lancaster models. Then, we investigate customer-centric marketing and operations decisions, exploring technology-switching scenarios that interchange AM with MC across the product life cycle (PLC). We formulate and solve an optimization problem by jointly deciding on technology-switching times, pricing, and product variety strategies to maximize a manufacturer’s profit and meet individual customers’ diverse and evolving needs. We use a validated Sample Average Approximation approach for the numerical solution of our non-convex optimization problem. Testing different pricing strategies, we show that decreasing and flexible trajectories are optimal. We derive analytical properties for the optimal pricing policy and demonstrate that a manufacturer can benefit from interchanging AM and MC across the PLC, in particular by adopting an AM-MC-AM scenario.