The objective of this thesis is to develop a maintenance approach that fulfills the requirements of Industry 4.0. It explores the role and importance of maintenance activities in today’s industry. Then, it develops the features and tasks required to be performed by maintenance to fulfill the demands of Industry 4.0. Finally, it develops a reference model to be used in designing maintenance system for Industry 4.0. To perform these studies, real data were collected and applied as well as a typical scenario was implemented. The results achieved in the papers of this thesis are 1) a mathematical representation and application of a model that identifies, analyses and prioritizes economic weakness in working areas related to production, 2) a model that analyses, identifies and prioritizes failures that impact the competitive advantages and profitability of companies, 3) characterization of a suitable maintenance technique for Industry 4.0 and 4) a reference model i.e. a framework, that could be utilized to develop a maintenance approach for Industry 4.0. The conclusion of this thesis confirms that maintenance has a significant impact on companies’ competitive advantages, other working areas and profitability. To achieve a suitable maintenance technique for Industry 4.0, this technique must be able to monitor, diagnose, prognosis, schedule, assist in execution and present the relevant information. In order to perform these tasks several features must be acquired, the most important features are to be: digitized, automated, intelligent, able to communicate with other systems for data gathering and monitoring, openness, detect deviation in the condition at an early stage, cost- effective, flexible for adding new CM techniques, provide accurate decisions and scalable. The developed framework could be used as a base to design a maintenance system for Industry 4.0. This study contributes to our understanding of the maintenance importance in today’s industry and how to develop a maintenance approach for Industry 4.0.

Winter in Northern Sweden comes with very harsh and unpredictable condition associated with large amounts of snowfall covering roadways thereby affecting transportation by roads. When the road conditions i.e. the snow depth, road unevenness and friction of the road surface are accessed and found to exceed the threshold, a maintenance action must be carried out to retain the road to the required condition for the user. The aim of maintenance, in this case, is to make the road comfortable, safe and economical for the road user. Decision support system, therefore, comes in handy to facilitate on deciding what maintenance action to carry out and when the action should be carried out, where the action should be carried out and how to go about the action based on the various data and resources available. This thesis project concentrates on how to carry out a winter road maintenance after receiving an alert of an action to carry out, when to carry it out and the road network that needs to be maintained. The thesis work focusses only on two of the winter road maintenance actions namely snow ploughing of bus stops in Luleå and application of abrasives commonly referred to as sanding of bus stops. Carrying out winter road maintenance comes at a huge cost from both direct and indirect costs with the Swedish government spending about SEK 1.75 billion every year as indicated by Jana Sochor and Cecilia Yu (Sochor & Yu, 2004). This means that reduction in the maintenance cost of even 5% through optimisation of the maintenance cost would translate into a saving of about 87.5 million SEK per year and in 10 years could amount to close to 1 billion SEK. Optimization also leads to efficiency and effectiveness that could result in improved movement on the road and reduced environmental and social-economic impacts. Maintenance planning thus becomes essential for the effective and efficient execution of work and utilisation of the available scarce resource. This thesis project focusses on the use of Operations research methods to minimise the cost of carrying out a winter road maintenance action by finding the near optimal or if possible optimal solution and still deliver the required service level. The thesis delivers two main things: It first delivers a framework to support winter road maintenance decision making after an alert of an action is received and secondly an algorithm for the route that minimises the cost of maintenance by providing the route that minimises the travel distance of the ploughing/sanding vehicle from its source depot and back to the depot after completing a maintenance action assuming that the vehicle and material (fuel and sand) are in the same depot. The routes with minimum travel distance will, therefore, be that route that will reduce the labour time and in turn the labour cost, reduce the fuel consumption and the maintenance of the equipment due to reduced usage. The project uses a vehicle routing problem which is a generalised travelling Salesman as the optimisation technique to determine the optimal solution for the allocation of resources for carrying out a maintenance action to facilitate efficient utilisation of the available resources. This is with the help of a commercial optimisation software and support tools namely ArcGIS. To come up with the algorithm, the first step was a digital representation of the vehicle road network in Luleå for network analysis after which the bus stops were imported from google earth into the network. A two-stage optimisation was then carried out: first was a model for route optimisation based on the road network in ArcGIS with the objective function to minimise the travel distance and constraints based on the available resources. The results of ii the model were then exported into excel for the second optimisation for the optimal cost of maintenance done through a developed excel algorithm. The total cost of maintenance comprised direct and indirect cost. The direct cost consisted of the cost of fuel, the cost of personnel and the cost of hiring vehicles while the indirect cost results from the penalty fee charged for sanding and ploughing a bus stop after the threshold time given to a maintenance contractor by the municipality. Any bus stop that is ploughed after the threshold attracts a penalty per hour of the exceeded time. Six penalty threshold times were considered i.e. 30, 60, 90, 120, 150 and 180 minutes and a single parameter deterministic sensitivity analysis was carried out for each cost parameters to determine the sensitivity of the total maintenance cost. The more relaxed penalty thresholds were found to be less sensitive to the direct cost and the total maintenance cost compared to the more sensitive ones. When the penalty threshold is relaxed, the optimal maintenance cost reduces, and the required number of vehicles reduces. The cost of vehicle hire was found to be more sensitive than the other costs. The results of this project can help the maintenance contractor in developing a work schedule for the maintenance personnel and improve vehicle fleet management. By modelling the worst scenario, a contractor can plan for the maximum number of vehicles required and consequently the personnel required. With the optimal travel route for each vehicle and the total maintenance cost determined, maintenance contractors can determine the sustainability and profitability of their business and be able to negotiate for a better and more sustainable agreement (Contract) or for the relaxation of the penalty threshold time if it does not affect the service level required i.e. the quality and safety requirements. The approach used in this project can also be used for other winter road maintenance problems.