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Continuous improvement (CI) is a key component of lean manufacturing (LM), which is fundamental for organizations to remain competitive in an ever more challenging market. At present, the new industrial revolution, Industry 4.0 (I4.0), is taking place in the manufacturing and service markets, allowing more intelligent and automated processes to become a reality through innovative technologies. Not much research was found regarding a holistic application of I4.0′s technological concepts towards CI, which clarifies the potential for improving its effectiveness. This clearly indicates that research is needed regarding this subject. The present publication intends to close this research gap by studying the main I4.0 technological concepts and their possible application towards a typical CI process, establishing the requirements for such an approach. Based on that study, a conceptual approach is proposed (PDCA 4.0), depicting how I4.0 technological concepts should be used for CI enhancement, while aiming to satisfy the identified requirements. By outlining the PDCA 4.0 approach, this paper contributes to increasing the knowledge available regarding the CI realm on how to support the CI shift towards a I4.0 industrial paradigm.

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The planning of injection molds production is a complex task where the classic planning approaches has proven limitations because of the following particular characteristics: the mold expands in a myriad of components for production; the “one-of-a-kind” type of production and recurrent mold design changes asked by the customer. To overcome this challenge a methodology to Molds Production progress Mapping is proposed (MPM). The methodology was built on the authors experience in collaborating with mold making companies, on the principles of lean manufacturing and visual management (VM) and also have in mind the trend towards production digitalization to be aligned with Industry 4.0 evolution. As a VM tool to support molds production planning, the MPM shows the instant progress of each mold, the deviation from planning and the remaining time for the due date.  The user has “only” to input the mold’s components critic level and the relative weight of each production process in the total time; it also requires that the production process are armed with sensors and the production task are digitalized and compiled in the information management system. The MPM can also be used in other complex production systems.

This work responds to the gap in integrating the Internet-of-Things in Continuous Improvement processes, especially to facilitate diagnosis and problem-solving activities regarding manufacturing workstations. An innovative approach, named Automatic Detailed Diagnosis (ADD), is proposed: a non-intrusive, easy-to-install and use, low-cost and flexible system based on industrial Internet-of-Things platforms and devices. The ADD requirements and architecture were systematized from the Continuous Improvement knowledge field, and with the help of Lean Manufacturing professionals. The developed ADD concept is composed of a network of low-power devices with a variety of sensors. Colored light and vibration sensors are used to monitor equipment status, and Bluetooth low-energy and time-of-flight sensors monitor operators’ movements and tasks. A cloud-based platform receives and stores the collected data. That information is retrieved by an application that builds a detailed report on operator–machine interaction. The ADD prototype was tested in a case study carried out in a mold-making company. The ADD was able to detect time performance with an accuracy between 89% and 96%, involving uptime, micro-stops, and setups. In addition, these states were correlated with the operators’ movements and actions.