promising technology of IT infrastructure for seamlessly integrating classical networks and networked objects, for data acquisition and sharing great effects of the performance for many enterprise systems in modern manufacturing [33, 35]. On the other hand, CPS is a term to describe the interconnection between the physical and cyber world. By integrating analog/digital hardware, middleware, and highly flexible software behind cyberspace, CPS achieve the creation of a link between virtual elements and real entities [34, 36, 39]. In this way, physical entities can be controlled by the intelligence from cyber elements. Currently, embedded systems only focus on the stand‐alone computation rather than interaction with physical elements. Thus, CPS are usually referred as advanced embedded systems because of being intrinsically connected with internet‐connected objects [38] for performing desired functions that are frequently accompanied with real‐time computing capability and are able to link each embedded system to digital networks for independently facilitating data processing. A large number of recent studies [37, 38, 40, 41] also emphasized that IoT and CPS are supposed to have intelligence because they are assumed capable of being identified, sensing events, interacting with others, and making decisions by themselves. In summary, IoT provides a basic platform for connecting all CPS, and CPS cooperate seamlessly with real and virtual spaces to make Industry 4.0 possible. Therefore, we can definitely say that there is no CPS without IoT; no Industry 4.0 without CPS and IoT.
Cloud computing has emerged as a new trend of internet application in recent years [42]. By leveraging and extending the characteristics of cloud computing to meet the global and distributed requirements of current manufacturing industry, cloud‐based manufacturing, also referred as CMfg, has recently emerged as a next‐generation manufacturing paradigm. As remarked in [42], CMfg is characterized by many factors (such as scalability, agility, resource pooling, virtualization, multi‐tenancy, ubiquitous access, self‐service, search engine, social media, crowdsourcing, etc.) and is different from traditional web‐ and agent‐based manufacturing paradigms from several aspects, such as computing architecture, data storage, operational process, business model, etc. Hence, CMfg is surely a new paradigm which will revolutionize the manufacturing industry. In fact, CMfg is also regarded as one of the best solutions for implementing IoT/CPS [30, 32, 34, 36, 37, 42, 43, 44] because of its powerful computing capability.
To realize CPS, the technology of Big Data Analytics (BDA) are adopted widely. Therefore, BDA is also one of the core technologies of Industry 4.0.
1.2.2.2 Migration from e‐Manufacturing to Industry 4.0
Both e‐Manufacturing and Industry 4.0 adopt ICT as the enabling tool and emphasize the necessity of big data collection; while the former was proposed in 2000 and the later in 2012. The four key components of e‐Manufacturing are MES, SC, EES, and EC; while the four core technologies of Industry 4.0 are IoT, CPS, CMfg, and BDA. Because the cloud‐computing technology was not mature yet in 2000, e‐Manufacturing did not adopt CMfg as one of the enabling technologies.
e‐Manufacturing utilizes equipment managers in MES to collect all the process and metrology data; while Industry 4.0 applies IoT devices to collect all the data required. The technologies of IoT, CPS, and CMfg of Industry 4.0 can be applied to implement various EES functions (such as VM, PdM, and APC) of e‐Manufacturing with a more systematic and efficient fashion. The functions of SC in e‐Manufacturing can be accomplished by the CPS technology of Industry 4.0 as well. Also, BDA of Industry 4.0 can be applied to find the root causes of a yield loss for yield enhancement and yield management. Therefore, as mentioned previously, e‐Manufacturing is the predecessor of Industry 4.0. However, the function of EC in e‐Manufacturing is not considered in Industry 4.0 because EC is specific for the semiconductor industry but not the machinery industry.
1.2.2.3 Mass Customization
With the upcoming age of IoT [35, 43, 45] and CPS [46], Industry 4.0 redefines the industrial manufacturing system in a completely automated scenario. The characteristics of “digitization, intelligentization, and customization” of this industrial evolution advance the traditional manufacturing techniques from mass production towards a deep‐rooted mass‐customization (MC) [47].
Although MC is not a new concept, it is emphasized again in Industry 4.0 for the fact that customers are returning to the center of the core value [48, 49]. One of the core values of Industry 4.0 targets to integrate people’s demand into manufacturing for enhanced products, systems, and services for a wider variety of increasingly personalized customization of products [49]. Thus, a further change will happen to the manufacturing industries with Industry 4.0 that the customers can benefit from [50].
Frankly, it is the birth of IoT/CPS that lifts data‐collection and communication technologies to a new level so as to allow a faster response to customers’ needs. Industrial manufacturers can efficiently build relationships with the end‐customers by combining the flexibility and personalization of “custom‐made” in real‐time. MC is also known as the concept of “made to order” or “build to order” [51]. The production only happens after manufacturers know what customers’ demands are. Customers or end‐users can easily decide the certain functionalities or personal attributes of a unique product or service what they exactly want just via a web portal. In other words, customers, manufacturers, and equipment closely interact with one another through seamless connections via IoT/CPS – a win‐win situation for all participants in modern manufacturing relationships.
MC aims to provide customers with varieties of increasing customized products and a near mass‐production efficiency without the corresponding increase in cost and lead time. Since MC first coined by Davis [52], it has attracted a large number of researchers to take their great efforts to make MC possible for decades. So far there has been a significant progress, such as Gilmore and Pine [53] defined four approaches: collaborative, adaptive, cosmetic, and transparent customizations for targeting different mass consumer groups in MC markets depending on degrees of customization in the product itself, and representation of the product. Collaborative customization seeks to help clients who struggle to spot exactly what they want and helps to understand the needs of the customers and strives to make it clear to them. Adaptive customization allows customers to handle customized products themselves without manufacturer’s assistance. Cosmetic customization presents a standard product with various representation to different customers. Transparent customization means that manufacturers provide unique products without needing to inform customers. Silveira et al. [54] surveyed the earlier studies on MC to point out the visionary and practical conceptualizations of MC theory; also, fundamental requirements for developing a basic MC framework composed from eight generic levels of MC were thoroughly discussed in [54]. Further, as information technologies evolves, Fogliatto et al. [55] updated the latest successful MC applications among various fields, including the food industry, electronics, large engineered products, mobile phones, and personalized nutrition; or special MC applications such as homebuilding and the production of foot orthoses. They clearly identified required conditions in different fields and situations of implementing a suitable MC platform from the view of economics, success factors, enablers, and customer‐manufacturer interactions.
For manufacturers, two mandatory factors of agility and quick responsiveness to manufacturing changes are expected to minimize the escalating costs [51, 53, 55]. They have to ensure the production facility must be flexible enough for switching between complex variants with some delay and be agile enough to adapt to changes in customized products at a low cost, thereby retaining economic benefits [55, 56]. For customers, after the emergence of Industry 4.0, the state‐of‐the‐art of IoT/CPS replaces traditional MC scenarios, and gives customers more chances to actively participate in a collaborative design of customized products.
However, no matter how production technologies are improved in the era of Industry 4.0, the ultimate aim for manufacturing has not changed, which is the manufacturing quality of products. Manufacturers are imperative to ensure that the manufacturing quality of deliverables conforms to the design specifications before delivering them
