Industrial engineers gather knowledge during their bachelor studies through lectures and
practical classes. The goal of practical class might be an extension of knowledge and/or a
consolidation and application of already gathered knowledge. It is observed that there exists
a gap between theory learnt during lectures and practical classes. If practical classes require
holistic approach and solving complex tasks (problems), students strive with understanding
relations and connections between parts of knowledge. The aim of this article is to show an
example of a simple practical assignment that can serve as a bridge between lectures and
practical classes through discussion of interactions and relations between parts of theoretical
knowledge. It is an example of in-class simulating of a line and cellular layout considering
discussion of elements impacting and impacted by the type of layout (e.g. learning curve,
changeovers, etc.). In-class verification of the presented approach confirmed its usability for
teaching industrial engineers and bridging the gap between theory delivered through lectures
and more advanced practical classes.
The paper discusses possible applications of wireless technologies in support of lean manufacturing tools. The typology of lean tools is provided. It distinguishes three main categories, which are identification and analysis of waste, improvement implementation, and process monitoring. The set of lean tools was analyzed in terms of information requirements. On the other hand, the typology of wireless technologies was discussed including RFID and Wi-Fi. The literature review of wireless technology applications for support of lean tools was conducted. The literature was systematically reviewed from the point of view of specific technologies and specific tools which were the subjects of the analyzed publications. Both typologies were synthesized to establish a framework for wireless technologies applications in the context of lean manufacturing implementation. It also could serve as a guideline for lean practitioners and implies future research directions. This paper is an extended version of paper published by [1].