Customer | EPFL Graph Search

In sales, commerce, and economics, a customer (sometimes known as a client, buyer, or purchaser) is the recipient of a good, service, product or an idea - obtained from a seller, vendor, or supplier via a financial transaction or exchange for money or some other valuable consideration. Etymology and terminology Early societies relied on a gift economy based on favours. Later, as commerce developed, less permanent human relations were formed, depending more on transitory needs rather than enduring social desires. Customers are generally said to be the purchasers of goods and services, while clients are those who receive personalized advice and solutions. Although such distinctions have no contemporary semantic weight, agencies such as law firms, film studios, and health care providers tend to prefer client, while grocery stores, banks, and restaurants tend to prefer customer instead. Client The term client is derived from Latin clients or care meaning "to incline" o

Product platform development

Ali Saffarpour

Companies are valued by their market competitiveness. To be competitive in the market, companies have to respond to their customer requirements as best as possible. To this end, companies introduce new products to the market to better capture customer requirements. The use and employment of market data have been widely studied in academia and industry. The trend in customer can be determined from market data and can bring useful and interesting knowledge of the true customer requirements. In this thesis, a systematic methodology of "Product Platform development" is proposed which uses market sales data. The proposed methodology is composed of several steps. The steps each take advantage of one or several algorithms. The theoretical background of each step of the methodology and the algorithms are presented in the thesis. The first step of the proposed methodology is to normalize the input data and perform robust fuzzy clustering of normalized data. The second step is the extraction of both positive and negative association rules. The positive association rule concept is employed to find the relationships between customer requirements and functional requirements. Some of the general early stage design constraints can be identified by using the negative association rules concept is used to identify which can facilitate the product concept generation and selection procedure. An algorithm is developed to find the positive and negative association rules and to select the correct set of association rules. The extracted association rules are developed in a way which makes it possible to have minimum length antecedents and maximum length consequents. To this end, some modified algorithms have been proposed which not only consider the job of finding frequent itemsets from the fuzzy clusters of functional requirements but also extract the non-redundant association rules. During the third step, trends in customer requirements are identified by tracking the changes of association rules over several consecutive time periods. These trends are identified by extracting meta-rules. Finally, a new algorithm to aggregate the extracted association rules helps the product development engineers in making decisions about the desirability of possible product platforms. Given the different extracted association rules and the meta-rules, rules that show the trend of changes in the customer requirements and the products that are bought, the possible future product platforms are proposed as the stable part of a product family which can meet customer requirements. Modular software to investigate the performance of methodology was designed which allows the user to accomplish the whole proposed methodology without the need for any other software. This software allows future methodology extensions to be easily implemented.

EPFL2009

Industry 4.0 Technologies and Customer-centricity for Digital Supply Chains

Rachel Agnès Laetitia Rosemonde Marie Lacroix

The last few years have experienced the emergence of Industry 4.0 (I4.0), ultra-customization, and the explosion of demand for ethical, fair trade, and sustainable consumption. Organizations have therefore started a digital transformation of their SCs and of their production. They have placed consumers at the center of their strategic agenda. In the context of ultra-customization, mass customization (MC) is gaining momentum, especially with the arrival of digital technologies like additive manufacturing (AM), also known as 3D-printing). Due to technological advances, AM is now very popular for the final part production in series, and on a large scale. SC performance is, among others, driven by operational excellence, information sharing, and trust between the different SC stakeholders. Looking at the second trend, which is focused on new consumption patterns, organizations are now encouraged to evaluate the potential of adopting new digital I4.0 technologies. The combination of Blockchain (BC) with the Internet-of-Things (IoT) seems promising to improve SC performance and meet customer demand. Despite the recognized potential of I4.0 technologies, and the transition toward digital SCs, organizations are struggling to adapt to the two trends. This is mainly due to the lack of decision support tools for the adoption of these new technologies and to the lack of user-centric approaches. In this thesis, we develop user-centric approaches from which we model and analyze the impact of three I4.0 digital technologies on the SC. First, we develop a new demand model, the HLB model, taking into account the individual demand of heterogeneous customers. This model is the first, to our knowledge, to model both the heterogeneity of customers and the evolution of their purchasing behavior over time. It couples the Bass diffusion and the Hotelling-Lancaster models. Then, building on the HLB model, we analyze, across the PLC, marketing and operations decisions that result from technology-switching scenarios. We formulate and solve an optimization problem by jointly deciding on: technology-switching times, inventory, production quantity, pricing, and product variety strategies. The goal is to maximize a manufacturer's profit while addressing the individual and evolving needs of customers. We use a sample average approximation for the numerical solutions of our non-convex optimization problem. Based on an adaptive inventory policy, we derive a closed-form solution for the production quantity decision. Our results demonstrate that the new usage of AM with MC, and a user-centric approach, can benefit a manufacturer. Significant profit improvements can be achieved with a hybrid AM-MC-AM technology-switching production scenario, under certain production capacity conditions. Second, we adopt a three-step approach to discover the BC IoT success conditions for lean and agile SCs: (i) a multivocal literature review (MLR), (ii) a topic modeling to categorize the success factors (SFs), and (iii) associate the categories of SFs to SC macro-processes for lean and agile SCs, respectively. Our findings are summarized into a conceptual framework and research propositions. This last study offers valuable insights into when and how the sweet spots for both SC types would materialize in practice, as well as their impacts with respect to the SC macro-processes performance.

EPFL2021

Patterns for Value-Added Services Illustrated with SEAM

Aarthi Gopal, Maude Laetitia Grossan, Gorica Tapandjieva Sekulovska, Alain Wegmann

The basic idea behind the provision of a service is to hide the service implementation details from the client in such a way that more value is provided to the client. Very often support services allow too much of their implementation details to be visible to customers resulting in poor value proposition. In this paper we describe a project that aimed towards the requisite abstraction of the support service implementation for the research funding at the Swiss Federal Institute of Technology (EPFL). We show the current service, the problem it creates for its customers, and propose four patterns for improving the value that customers receive from a requisite service abstraction. The four patterns are: provide a simple service interface, incorporate needed external actors into the service view of the organization, recognize new customers and use data and process linking technology. These patterns are applied in a to-be model.