Abstract
The potential of the Internet of Things (IoT) and other technologies in the realm of Industry 4.0 to generate valuable data for monitoring the performance of the production processes and the whole supply chain is well established. However, these large volumes of data can be used within planning and control systems (PCSs) to enhance real-time planning and decision-making. This paper conducts a literature review to envisage an overall system architecture that combines IoT and PCS for planning, monitoring and control of operations at the level of an industrial production process or at the level of its supply chain. Despite the extensive literature on IoT implementations, few studies explain the interactions between IoT and the components of PCS. It is expected that, with the increasing digitization of business processes, approaches with PCS and IoT become ubiquitous in the near future. © 2020, Springer Nature Switzerland AG.

Abstract
Chatbots have been used in business contexts as a new way of communicating with customers. They use natural language to interact with the customers, whether while offering products and services, or in the support of a specific task. In this context, an important and challenging task is to assess the effectiveness of the machine-to-human interaction, according to business’ goals. Although several analytic tools have been proposed to analyze the user interactions with chatbot systems, to the best of our knowledge they do not consider user-defined criteria, focusing on metrics of engagement and retention of the system as a whole. For this reason, we propose the KnowBots tool, which can be used to discover relevant patterns in the dialogues of chatbots, by considering specific business goals. Given the non-trivial structure of dialogues and the possibly large number of conversational records, we combined sequential pattern mining and subgroup discovery techniques to identify patterns of usage. Moreover, a friendly user-interface was developed to present the results and to allow their detailed analysis. Thus, it may serve as an alternative decision support tool for business or any entity that makes use of this type of interactions with their clients. © Springer Nature Switzerland AG 2019.

Abstract
The paper deals with a relevant scheduling problem associated with moulding injection machines. A footwear company, equipped with advanced automation machinery, faces true difficulties in planning the injection equipment production. It is crucial to respect delivery times without disruptions. There are many conditions associated with footwear and technological issues to consider, such as the weekly demand for different models and sizes, which is major to satisfy them on time. The moulds for each size of a model and distinct available machines, with varying quantities of positions for the moulds, are other concerned matters. Changeover times, which occur when changing moulds, are critical. Stocks are also considered. The time horizon attains tens of weeks. We developed an integer optimisation model with the objectives of minimising both changeovers and stocks. That initial model underwent a few simplifications, acceptable from a strategic and technological point of view, due to the impossibility of reaching admissible solutions. The new version can solve the real dimension problems optimally, those that matter. The paper describes one case, and the solution obtained. The new approach followed, and the solutions obtained, are essential for the company, given the planning difficulties; moreover, the method may also be relevant for any footwear industry facing similar combinatorial optimisation problems. © IEOM Society International.