A context-aware concept evaluation approach based on user experiences for smart product-service systems design iteration

Abstract

With the trend of ‘digitalization’ and ‘servitization’ in the manufacturing industry, numerous product-service systems fail to seize a market share and encounter an imbalance between digital investments and expected revenues. This phenomenon is probably caused by the insufficient evaluation on user experience and by the lag between user requirement changes and the offered solutions. Both limitations can be mitigated via automatic Smart PSS evaluation based on broader concerns on user experience information that was collected from either product-service bundles or user behavior. In this paper, a context-aware concept evaluation approach is proposed for Smart PSS design iteration, aiming to satisfy users in a more timely and automatic manner. Derived from the conventional information axiom method, the proposed approach introduces a context-aware evaluation indicator identification module and an automatic system range identification procedure based on natural language processing techniques, and eventually return the most robust concepts during the usage phase. With less human intervention in the design process, it relieves the lag between user requirement changes and the solutions, and reduces the prescriptive instructions in the conventional information axiom method. A case study of a 3D printer company’s design iteration is conducted, which proves the proposed approach’s feasibility. It is hoped that this work provides practical guidance for achieving a more context-aware Smart PSS development.

Introduction

With the dramatic transformation of manufacturing servitization, product-service systems (PSS) have been globally accepted by manufacturing companies in recent years [1]. As an inherently dynamic and multi-dimensional system including multiple stakeholders and product-service bundles (PSBs), PSS delivers user-required functionalities in a way that reduces the impact on the environment [2], [3], [4]. Since 2014, a novel business paradigm called Smart PSS [5] appears along with many cutting-edge information and communication technologies (ICT), such as Internet-of-Things (IoT), Cyber-Physical Systems (CPS), and Artificial Intelligence (AI) [6]. Those new techniques enable even more massive accessible data from multiple parties, much more flexible interactive modes, and proper decision supports throughout the product lifecycle [7], [8], [9], [10], [11]. From this perspective, the appearance of Smart PSS denotes a further transformation from servitization into digitalization, making the manufacturing business an ever-evolving and much more flexible manner that can be examined and then upgraded even after launching to the market [12], [13], [14].

However, according to an investigation [15], only a small proportion of companies succeeded in their digitalization transformation to obtain the expected economic returns [16]. The imbalance between the digital servitization investments and the expected economic returns, the so-called ‘digitalization paradox’, has been discovered in many firms [16], [17]. For example, Michelin has launched a comprehensive tire management solution called Michelin Fleet Solution for the large European transportation companies in 2000, but received far below-expected contracts and profits [18]. Another example is that General Electric has reached $ 3.9 billion in digital revenue in 2018, but it is still nowhere approaching its goal of $15 billion in digital revenue in 2020 [17].

Demonstrating the inherent unsustainability in the economic aspect and some non-linear effects on company performance [19], the traps of digitalization paradox in Smart PSS development are commonly regarded to be caused by (1) the excessive attention on technical possibilities rather than customer experiences [17]; (2) the frequent change of user experience due to the insufficient satisfaction on user requirements and the influence of fashion trend/public media [20]; and (3) the lag between the changes of user experience and the solutions [21].

Facing the above challenges, several strategies are taken to comprehensively and wisely evaluate Smart PSS and pursue a win–win situation for both companies and customers [22]. Firstly, the product-service bundles evaluation should be conducted comprehensively based on user experience indicators, rather than only on technical attributes. Secondly, service providers are expected to offer a quick approach to explore user-concerned indicators to the PSBs. Since the user experience changes can be reflected in both their behavior physically [23] and their attitude cognitively [24], a context-aware concept evaluation approach for Smart PSS design iteration is expected. Thirdly, service providers should select the most robust PSB concepts to relieve the lag effect between the customer experience changes and the solutions [25]. To achieve it, the information axiom in axiomatic design is one of the effective methods for robust concept evaluation [15].

Although the above strategies have been separately discussed in numerous product development studies [15], [26], [27], [28], in the big and content-rich world that is encoded by massive user-generated data and sensed-data in Smart PSS design iteration [8], [29], PSB concept evaluation approach still needs to be further enhanced in automation and rapid reaction capability. Therefore, the primary focus of this paper is on (1) how to identify evaluation indicators automatically and (2) how to rapidly evaluate the current design concepts considering user experience.

The remaining sections are organized as follows. Related studies on concept evaluation and context awareness are reviewed in Section 2. Section 3 expounds on the proposed context-aware Smart PSS evaluation method. Subsequently, in Section 4, an example of a 3D printing company’s concept evaluation is demonstrated for the feasibility of the proposed method. Finally, we discuss the primary results and summarize the main academic contributions in Section 5 and Section 6, respectively.