Current competition among companies is fought through their supply chains. As the performance of the supply chain depends not only on manufacturing and marketing attributes, it becomes necessary to establish a framework upon which to develop a supply chain strategy. The paper presents the rationale and principles of a customer–product–process–resource (CPPR) framework for the simultaneous analysis of the business, supplier, manufacturing, planning, marketing and customer dimensions of a supply chain strategy. The originality of the CPPR framework is that it establishes a set of supply chain structural elements, that when put together determine the degree of alignment of a supply chain strategy. As the main objective of a supply chain strategy is to achieve customer satisfaction as an output of the supply chain operations, the CPPR framework becomes a realignment tool when used in combination with a realignment methodology, which is also presented in the paper.

Digital Twins (DTs) are one of the disruptive technologies associated with the Industry 4.0 concept. A DT connects the physical manufacturing system with the digital cyberspace, via the synchronization of the simulation (i.e., physical configurations) and data models (i.e., product, process, and resource models) of the manufacturing system. This synchronization of both worlds—the physical and digital—allows one to address the issue of manufacturing customized products. This challenge of mass customization (1) puts forward the goal of achieving the highest level of customer satisfaction, and (2) creates the need for the optimization of the complete value creation process. Within an Industry 4.0 context, the latter is translated as the interlinking of production resources and systems, via a DT, as it is in the physical world where the actual value-creation process takes place. The success of an Industry 4.0 mass customization environment (or mass customization 4.0), depends on its degree/level of sustainability. For these reasons, the present paper presents a review of relevant concepts related to the role of DTs in the achievement of a mass customization 4.0 environment, plus some proposals of how to address the identified research challenges. A future research agenda is proposed at the end of the paper.

It has been stated that Industry 4.0’s goal is, among others, the sustainable success in a market characterized by exigent and informed consumers demanding personalized products and services, where the level of manufacturing complexity increases with level of product customization. Even though different manufacturing complexity measures have been developed, there seems to be a lack of a comprehensive metric that address both the mass customization variety-induced complexity, and the complexity derived from the adoption of the Industry 4.0 paradigm. The main original contribution of this paper is the development of an entropy-based (entropic) formulation to address this last issue. Its validity and usefulness is put to the test via a discrete-event simulation study of a mass customization production system operating within an Industry 4.0 context. Our findings show that the entropic formulation acts as a fairly good trend indicator of the system’s performance parameter increase/decrease, but not as an estimator of the final values. A discussion of the managerial implications of the obtained results is offered at the end of the paper.

In today's economic environment, manufacturing organisations compete against each other as part of supply chains (SC). As both the SC strategic level and production floor operational level are interdependent, a misalignment between them has a deep impact on the performance of the manufacturing organisation. For this reason, in this paper we develop an analytical expression of the impact such misalignment has on the manufacturing organisation performance, specifically, its demand fulfillment ability. The usefulness of the analytical expression is illustrated via the development, for the case of a local furniture company, of a system dynamics (SD) simulation model. The SD simulation model is tested under different operational conditions, so the case study company can derive conclusions regarding actions to improve its demand fulfillment ability.