Supply and planning in the factory of the future

Abstract

Recent developments in technology innovations show that huge efficiency improvements can be made in the manufacturing industry. Moreover, companies that adopt the new innovations, associated with Industry 4.0, can create a huge competitive advantage. However, these rather conservative businesses are slow adopters and usually wait for proof-of-concept before actual implementation. Since this industrial revolution, Industry 4.0, is still in its infancy, more research is required to get to full adoption. Although the industry still awaits proof-of-concepts, many different case studies have been performed, and with success! These clearly exhibit the versatility of the Industry 4.0-philosophy, makingwidespread adoption just a matter of time. One of the identified reasons for this lag in adoption is the lack of clear implementation guides despite the thorough research and redundancy of technology. Due to the holistic Industry 4.0-concept, many practitioners lose sight on how and what to implement. Various researches proposed the creation of a widely applicable implementation model, but this is yet to be developed. One of the prominent issues related to creation such model is the all-encompassing nature of Industry 4.0; it includes novel innovation in supply chains, in factories, and even in the products manufactured. Since there are clear differences between these ’applications’, a generic overarching model seems unreasonable considering the immense amount of variables to consider. This thesis depicts the first ever-made implementation model specifically aimed at improving raw material supply & planning in complex manufacturing companies.Supply & planning processes are the closest connections between a manufacturers’ own operations and its closest neighbours in the supply chain; i.e. suppliers and customers. Tapping into this specific field of operations enhances the utilization of Industry 4.0 both on the supply chain and manufacturing aspects. Through answering the main question, and several subquestions, relevant information is gained that enable the construction of an implementation model. The design of such implementation model includes a step-by-step approach for practitioners of manufacturing companies, and a clear description on what to consider at each step. Creation of the artefact (i.e. implementation model) happens by explaining the research question: How can Industry 4.0 be implemented into supply and planning departments of complex manufacturing companies using an implementation framework?By means of a design science research methodology (DSRM) the Industry 4.0 supply & planning implementation framework is designed. Through 6 pre-determined steps; (i) problem identification, (ii) objective definition, (iii) design & development, (iv) demonstration, (v) evaluation, and (vii) communication, it ensured that all relevant stages are included to construct a scientific substantiate artefact. Three of these elements in particular were considered to be main constructs of the thesis report. Through the objective definition stage, qualitative research in the form of interviews and literature review imposed what had to be included in the implementation model. In the design & development stage this information was casted into a mold, thereby being the first result to the thesis’ ultimate goal. The demonstration phase was assigned to check the applicability and effectiveness of the model by putting it into practice. Altogether a significant base of information was collected, obtaining the firstconceptual implementation model for Industry 4.0. In the existing tight markets in which various manufacturers operate, the utilization of improvement technologies is high. Techniques derived from methods like Lean, Agile and Six Sigma are used on a daily basis. Because companies are familiar with the use of these models, the adoption of newer versions becomes straightforward. Consequently, the implementation model is a derivative of such method, namely the DMAIC (Define, Measure, Analyze, Improve, and Control). Since these overarching steps do not provide sufficient information for actual implementation, extra delineation is applied through a combination of the Continuous Quality Improvement model and practitioners’ experiences. Via combination of the two, a first model consisting of 11 steps (i.e. within the 5 DMAIC stages) was constructed.The implementation model starts with goal identification, in which the companies’ digital transformation (i.e. Industry 4.0-adoption) strategies are adapted to local needs. Subsequently, the business processes are investigated thoroughly. By clever modification of an existing model called RAMI (ReferenceArchitecture Model Industrie 4.0), a standardized approach for identifying the key aspects of the business processes was obtained. Using the results of this business process modelling allows to diagnose the so-called key variables that have a considerable effect on the performance of operations. The top five of these key variables provide the focus for the execution of the consecutive steps. Data and information about these 5 variables is gathered through a process of replacing paper forms by digital forms and through connection of existing Operational Technology (OT) systems with Information Technology (IT) systems. Once all the relevant data for the five variables is obtained, a data analysis follows. Examining the inconsistencies in this data pinpoint the location where data enhancement (i.e. Industry 4.0-adoption) will significantly improve the process. Defining the performance indicators then help to know the business’ existing performance and allow comparison with future results, but also help users to monitor real-time process-efficiency by means of a dashboard. According to the Key Performance Indicators (KPI’s) chosen, technology introduction can finally happen. Thirteen different enabling technologies were identified during the literature research, providing practitioners a wide portfolioof options in their Industry 4.0-implementation. Shortly after implementation follows continuous monitoring according to the aforementioned KPI’s. By carefully assessing the business process’ performance, improvement studies can be performed and actual improvement of the system can take place. In the final stage it is evaluated whether the implementation was effective and what lessons-learned should be brought to the next technology-implementation. To test whether the implementation model indeed fulfil its vows, a test run is performed at an agriculture fertilizer manufacturing facility that definitely classifies as a complex factory according to the definition of this thesis (i.e. large portfolio of products and raw materials). The first few stages were quite obvious in their execution, mainly because of the clear instructions given. Especially the modified RAMI model gave useful insights and abandoned the requirement of complete Business Process Mapping which is very time consuming. Various key variables were obtained using a quality team. Since the majority of data -for these key variables- was already available, it was only a minor effort to obtain the rest using either OT-IT merger or digital reporting. In the case study, the data analysis stage was the most demanding task in both time and extra investigation. After describing the KPI’s related to the data analysis and describing the technology introduction stage, the real version of the case studyhad come to an end due to time and resource limitations. Continuous monitoring, improvement, and evaluation were further concluded through the sense of ’modelling’, where providing examples and describing the expected outcomes served as enclosure of the first trial. Although the model was designed with extra care and the input from both the literature review and the interviews were significant, some limitations still apply. It was observed that some of the stages were not definitive enough, making the actual goal of each step rather vague. As a result, some stages could take considerably more time than necessary, diminishing the model’s effectiveness. Moreover, the power of the data analysis, as described before, was truly reliant on my experience in statistical and data analytics. Therefore, the current data analysis-description requires more attention to advance the usefulness of this stage regardless of the users’ experience. Finally, the effectiveness and generalizability of the model were only touched upon briefly and require more in-depth investigation before claiming its novelty.