Due to automation and robotization of material handling systems (MHS) in the transportation industry, maintaining equipment and systems becomes more important, and needs to be integrated in the company’s business strategy (Tsang, 2002). Implementing and investing in the right maintenance approach to keep these systems in optimum state is therefore essential for the performance of a company and more important, for the customer’s satisfaction. When a company has to deal with an increase in failures and decreasing performance, it needs to anticipate to change this negative trend. In literature, most of these problems are analysed by using electronic data and condition-based monitoring (Bouvard, Artus, Bérenguer, & Cocquempot, 2011). Analysing the condition of equipment requires data monitoring which is not always possible. Besides, these analyses are often based on a single system which conditions do not apply on systems with different specifications. TNT Express (initiator of this research) is dealing with this problem within the Benelux. They are dealing with decreasing on-time delivery (OTD) performance and increasing costs of smaller sorting-sites without technicians. Having high OTD performance is an essential element of TNT’s business strategy, and is a competitive advantage in their industry. Their time-critical processes combined with the interrelated network structure of their sites makes it hard to develop a single strategy or solution that positively affects the performance of all sites. The differences in size, work methods, level of technology, different size of freight and fluctuating demands makes it a dynamic and complex environment with a lot of variables that influence the performance of on-time delivery. That makes it hard to identify the causes and effects of these failures over time. Besides, TNT doesn’t have the useful data to base their maintenance on. Therefore, finding the bottlenecks and the right variables that influence this performance most is essential for decreasing the costs of TNT. Therefore, the objective of this research is: “Identify the constraints of TNT’s Material Handling Systems in a dynamic environment, to be able to apply the right maintenance strategy that preserves TNT’s delivery performance at lower cost.” Before a start could be made on finding the bottlenecks of the sorting sites, TNT’s maintenance structure, operational structure, financial structure and operational structure needed to be defined. TNT’s sorting operation, where freight is unloaded, sorted, measures and loaded, runs around 19 hours a day, 6 days a week all year long. That limits the time for maintenance and check-ups to 6 hours a day. Still, a fast response is needed when the sorter does fail during operation, to prevent the process getting delayed which generates extra costs. TNT’s maintenance of those sites turned out to be depending on mostly corrective maintenance, and less on preventive and predictive maintenance. Mechanics are traveling from site to site to solve current incidents that cause delays, and less for preventive maintenance and check-ups. Original equipment manufacturers (OEMs) are also responsible for some maintenance activities, although these are mostly time-based and less used for trouble shooting. When specifying TNT’s on-time delivery performance, the performance of the sorting sites turned out to be the most important factor of the OTD. This sorting site performance is measured by the capacity of freight it theoretically can handle, and the amount of freight it is transporting in reality. This site performance is also influenced by several variables, where the Material Handling Systems availability turned out to be the largest influential variable. The failing devices and equipment are the cause that IV operators on the floor have to sort manually, which takes a lot more time. Besides, these delays are generating extra costs, only how much and which costs is not quite clear. TNT’s limited registration of essential elements like downtime, maintenance costs and OEM activities made it difficult to link these elements, and to find possible bottlenecks. So, to be able to link the MHS- downtime with the related costs on a daily basis, three Critical-to-Quality factors (CTQs) are analysed:  Breakdown costs  Maintenance costs  MHS availability Because the financial impact of a breakdown was not yet specified, the most important factors are determined by interviewing business improvement experts. Some costs turned out to be different for the import or export process during a working day. For TNT, the next financial factors turned out to be important: 1. Missed Check-Weight-Cube (CWC) revenue 2. Hiring extra vans and trucks 3. Personnel costs (overtime) 4. Financial consequences of lower service level (loss of customers) From these factors, the missed CWC revenue turned out to be largest costs factor in the import process, where hiring extra trucks and vans turned out to be the largest costs factor of the export process. These two factors are further specified and calculated, to link them to specific breakdowns. Depending on the volume in different locations, the missed CWC revenue is variating between €000,- and €000,- for a single shift per day. These costs are calculated by determining the number of packages that are affected by a breakdown and do not get a second weighing check on a next location, multiplied by the average revenue missed per parcel. These calculations showed that without a working sorter or CWC on a single location comes with large financial consequences. These specific insights in CWC revenue loss show the need for fewer failures to improve the financial performance of TNT by changing the maintenance activities. The extra costs of hiring extra vans to transport all freight towards the customers in the export process turned out to be €000,- for a single shift (1day). These costs are in the same order as the CWC revenue losses, and from financial perspective just as bad. The cost analysis regarding this data showed how disperse the information is within TNT, and that it is hard to develop a strategy without this kind of crucial information. Maintenance costs are referred as costs that are made to solve the incidents and include, service contracts, mechanics salaries etc. However, costs that are made for decreasing the number of incidents are more important. Think of hiring extra employees, changing service contracts, training current employees. Quantifying these costs is only possible when the bottlenecks of the breakdowns have been derived. V The lacking quality of the data regarding downtime and availability of the MHS made it impossible to use mathematical solutions to find the bottlenecks, so “Soft Operations Research” methods have been used to find the causes of the failures (Heyer, 2004; Masys, 2015). Using Pareto charts and Ishikawa diagrams on the incident file controlled by TNT’s mechanics, the data in this file is structured and enriched to find failures with recurring root causes that are a structural problem. First, three devices that have the most impact on the MHS availability are determined. These are the Sorter, Roller track/belts and Check- Weigh-Cube (CWC) and are responsible for 130 of the 210 incidents for the first 5 months in 2017, which represents more than 60% of all incidents of the MHS. The registered incident data of these three devices is enriched by going a step further in the cause, structuring all data, and visualising them in an Ishikawa diagram. By doing so, it became clear that a high number of incidents (35%) have an operation cause, which means that they are caused by human error. These incidents are not specifically location bounded, and solving the most frequent occurring incidents will prevent a lot of future incidents. The three selected root causes that are responsible for most incidents are: 1. Lack of system knowledge Operators, especially Team Leaders (TL) and Leading Hands (LH), have too little knowledge of the process, the effects of failures and simple technical solutions. There skills haven’t been developed with the increasing mechanisation. Not knowing the impact of failures, and how simple incidents can be prevented leads to unnecessary failures. 2. Sensor related incidents A large part of those operational incidents are sensor related. Because these incidents are still frequently occurring, they need to be handled separately. These incidents can be solved quite easy most of the times, and a support system to help operators to solve them is very helpful. 3. Lack of working according instructions A large part of the incidents are caused by wrong choices of operators, by putting wrong parcels on the sorter, putting them on the sorter at the wrong place or working not according the given instructions. The lack of knowing the consequences results in an uninterested work attitude which results in a performance decrease of the sorting site. For these three root causes, several improvements have been developed. For the first root cause, a training program is recommended which start with a well-communicated plan to create the urge for change. The current attitude of personnel is asking for a plan that provides support from the whole organisation. TNT needs to know the value of knowledge amongst their employees, and this needs support from top management. Combining this with low-technical training sessions and useful supporting tools will increase the employees’ knowledge, and also creates ownership with the employees. To do so, TNT needs to review its distribution between part-time employees from employment agencies, and employees contracted by TNT. Increasing system knowledge amongst employees is only effective if that knowledge stays within the company. Rewarding well performing VI employees with a contract by TNT will have positive effect on the knowledge on the work floor and not only prevents, but also decreases the amount of downtime. For sensor related incidents, a supporting manual has been developed that functions as a simple flow chart that guides TL through the process of solving these failures. Following these steps in the included manual gives simple but clear instructions how to act and what to do. Before using this supporting manual, the TL need sufficient training before they can execute the process. Although this solution is more focussed on corrective maintenance, this manual helps to solve simple incidents that mechanics do not have to solve by themselves. Not only are these incidents solved much quicker, due to direct handling of the TL, but the mechanics do not have to drive towards the location of the incident. That also gives the mechanics more time to do preventive maintenance tasks, and therefore further decrease the number of incidents of the MHS. This manual strengthens the need for more direct or autonomous maintenance within the sorting sites of TNT (Chen, 2013). Currently, these sites are too depended on the knowledge of external maintenance providers to repair the failures which is not desirable. To improve that employees work more according instructions, better supervision is advised and recommendations are made to reward well-performing operators. Rewarding operators also means that they will be more responsible for their colleagues and have to make sure that they understand the instructions. They become responsible for preventing wrong parcels being put on the sorter, and have to correct their employees if they ignore the instructions because a lot of incidents are caused by wrong packages being put on the sorter. However, TNT should also improve the visibility and clarity of these instructions by using dummy parcels to indicate the allowed parcels sizes. Improving this product flow on the sorter will improve the sorters performance and lowers the number of incidents. The effect of these improvement could only be expressed by a decrease on the number of incidents, and not on downtime due to lacking data registration. If, as a starting point, already half of these structural incidents is solved by these improvements, the total amount of these 210 incidents over 2017 would already decrease with 13%. Due to the unknown length of the breakdowns, they cannot be compared to financial results although these results definitely improve. All these improvements are based on autonomous maintenance which is focussed on letting operators do more maintenance and technical tasks. Finally, all these improvements will help retaining TNT’s on-time delivery and decrease their breakdown costs. This process of finding these bottlenecks led to an even more valuable advice for gathering data. This research showed that TNT needs to improve their data collection system, and document important factors with a computerized maintenance management system (CMMS) like time registration, downtime during operations, and weight of breakdown. This data also needs to be accessible for OEM’s, so they also have insight in the failures, and can help improve the MHS performance. With this data, TNT can measure the downtime and better monitor its MHS availability to see which devices or incidents need further improvement. The importance of such data collection system is once emphasized by the fact that it was hard to determine the bottlenecks, especially with multiple locations at the sorting locations of TNT. VII The goal was to identify the constraints in a dynamic environment that has a lot of influential factors that make it hard to determine the effects of these variables. Using multiple techniques based on the number of incidents and the failure history, the first bottlenecks could be derived that needed improvement. Together with a costs analysis on the most important financial factors, solutions and recommendations are developed that will retain TNT’s on-time delivery at lower costs. However, the recommendations for a more centralized data-based maintenance system could even be more valuable in the future.