High availability of a system can be achieved by performing timely replacement of degraded or failed components. To this end, spare parts are expected to be available and reordered when needed. It is not uncommon that spare parts may deteriorate on the shelf because of their physical characteristics and/or the imperfect storage and transportation conditions. Such phenomena will affect the reliability of spare parts and the availability of the system. In this dissertation, we first focus on a system with single critical operating component and one unit of deteriorating spare part. For such a system, to ensure the system availability and cost efficiency, making a joint decision on component replacement and reordering time is of vital importance. In particular, we study both failure-switching and preventive-switching strategies, where cumulative damage is considered for the spare part switching from its in-stock to operating conditions. To determine the corresponding optimal component replacement and reordering policies, the long-run average costs are minimized under a fixed lead time. It is expected that the work will benefit quite a few industry sectors, such as mining, oil and gas, and defense, where the operation of systems heavily relies on capital-intensive components. To advance the research a step further, we have relaxed the system with only a single operating component to a more complex system with multiple components. In addition, we have eliminated the limitations on the order quantity and inventory capacity. To capture the on-shelf part deterioration, a two-phase deteriorating process is adopted, for which the first phase is from the spare's new arrival to the identification of its degradation, and the second phase is the period thereafter but before the unit fails. Based on the parts' degradation states, we introduce two different replacement strategies for the spare consumption, i.e., the Degraded-First strategy and the New-First strategy. Because of the random nature of component failures and on-shelf deterioration, stochastic cost models for both DF and NF strategies are derived. With the objective of cost reduction through coordinating the inventory and maintenance policies, an enumeration algorithm with stochastic dynamic programming is employed for finding the joint optimal solution over a finite time horizon. Numerical experiments are conducted to study the impacts of these two strategies on the operation costs, and the analysis of key parameters that affect the optimal solutions is also carried out in the numerical study. The joint policies of our interest focus on both replacement and reordering of spare parts, which are more realistic and complex than those policies handling maintenance and spare parts inventory control separately. In particular: When the maintenance planning and inventory control strategy are jointly optimized, we consider the spare parts inventory experiencing on-shelf deterioration, which has not been well studied in the related literature. When dealing with a system carrying only one spare part, the impact of on-shelf deterioration of the spare part on its remaining operational lifetime is explicitly dealt with and described by the Cumulative Exposure (CE) model. For the extended model for a multi-component system, we make an early attempt to adopt a two-phase process to take into account on-shelf degradation of parts. The issues in the degradation-level-based ordering of spare parts in the multi-component system are also discussed. Several integrated cost models are developed in both systems and are used to determine the optimal replacement and reordering decisions with the objective of minimizing the expected long-run cost rate over an infinite/finite horizon.

A dissertation submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Engineering. Johannesburg, November 1994. / Inventories are produced, used (e.g. for raw materials, supplies, spare parts, and So forth) or distributed by every organisation. Moreover, inventories represent a major investment from the perspectives of both individual firms and entire national economies. In addition, enormous costs are incurred in the planning, scheduling, control and actual carrying out of replenlshment-Iprocuretnentl related activities. Interest in the subject of inventory management is constantly increasing, yet Silver and Petarsonlll (P(eface) found that "although invi ,~ory management ha.l been studied in considerable depth from a theoretical perspective, yet, those of us who, throuah consulting work, come into. clos>!)contact with mananerlal decision procedures in this arer are repeatedly surprised to find how limited, and ad hoc, many of the existing decision systems actually are. The rate at which theory has been developed has far outstripped the rate at which decision practices of firms have been successft,Jlly upgraded. A major g~o has existed between the theoretical solutions, on the one hand, and the real world problems, on the other". Inventory control is the science-based art of ensuring that lust enough inventory (or stockl is held by an organisation to meet both its internal and external demand commitments economically. There can be disadvantages in holding either too much 01 too little inventory. Therefore, inventory control is primarily concerned with obtaining the correct inventory with compromise between these two extremes. The control and maintenance of inventories is a problem common to all enterprises in any sector of a given economy. The primary aim of this study is to identify What the inventory policy of a company shoull;I be to Secure a reduction in inventory-related costs while maintaining a high level of customer service. Lewis(2) defines two bMlt:~ tvpes of inventory policy. Those in which decisions concerning replenishment are based on the lellel of inventory held, are known as "fixed-order quantity models" or "re-order level policies" and those in which such declslons arc made on a time basis are known as "fixed-time period models" or "re-order cycle policies". According to Nadder(3) (7I 11) the basic distinction between fixed-order quantity models and fixed-tlme period models is that the former are "event-triggered" while the latter are "time-triggered". That is, a fixed-order quantity model initiates an order when the "event" of reaching a specified re-order level occurs. This event may take place at any time, depending on the demand for the items considered. In contrast, the fixed-time period model is limited to placing orders at the end of a predetermined time period; hence, the passage of time alone "triggers" the model. In this thesis, we shall discuss both classical inventory models and heuristic models. We shall also conduct an investigation into the factors affecting high levels of inventory ~ mainly lead times (supplier and internal lea' times) in relation to spare 9arts in the heavv-dutv truck and bus industry. The thesis also suggests guidelines for controlling stock or these types of commodities in a practical environment. This will be done by either researching the existing inventory models or developing new inventory models or a combination of both, the intention being not to look for absolute optimisation, but rather to achieve significant improvements over current operations. / GR 2016

We propose a mathematical framework that integrates low-level sensory signals from monitoring engineering systems and their components with high-level decision models for maintenance optimization. Our objective is to derive optimal adaptive maintenance strategies that capitalize on condition monitoring information to update maintenance actions based upon the current state of health of the system. We refer to this sensor-based decision methodology as "sense-and-respond logistics". As a first step, we develop and extend degradation models to compute and periodically update the remaining life distribution of fielded components using in situ degradation signals. Next, we integrate these sensory updated remaining life distributions with maintenance decision models to; (1) determine, in real-time, the optimal time to replace a component such that the lost opportunity costs due to early replacements are minimized and system utilization is increased, and (2) sequentially determine the optimal time to order a spare part such that inventory holding costs are minimized while preventing stock outs. Lastly, we integrate the proposed degradation model with Markov process models to derive structured replacement and spare parts ordering policies. In particular, we show that the optimal maintenance policy for our problem setting is a monotonically non-decreasing control limit type policy. We validate our methodology using real-world data from monitoring a piece of rotating machinery using vibration accelerometers. We also demonstrate that the proposed sense-and-respond decision methodology results in better decisions and reduced costs compared to other traditional approaches.

Major drivetrain components of the Epiroc machines like axles, gearboxes, transmission, torqueconverter, clutch, and bearings are critical components which are focused upon in this thesis. Failure of these major component results in breakdown of the vehicle and if early fault detection is not existing, then this causes engineers and technicians to spend a substantial amount of time to identify the root cause of the failure. Due to this, the machine stands still until the problem is identified and repaired creating a negative impact on customer satisfaction. Thus, failure of these components results in costly downtime. Ways of improving the uptime of the machine have been an ongoing discussion due to costly downtime subjected to component failure. To improve uptime, reduce MTTR, and create acompetitive edge, a drivetrain sensor system is suggested to implement in this thesis that will monitor real-time operating data from these drivetrain components. In this way, the health of the drivetrain will be continuously monitored and if there is any degrade planned maintenance can be scheduled well in advance and spare parts inventory can be managed more efficiently. In addition, this will generate competitiveness for Epiroc products in the market. Epiroc being in competitive business, continuously aims to improve its products and services to satisfy customer needs, improve total cost of ownership, life cycle cost, and increase sales and profit. Epiroc is a leading productivity partner for the mining, infrastructure, and natural resource industries. It develops and manufactures innovative drilling rigs, quarrying, and construction equipment with state-of-the-art technology and provides world-class service and consumables. / Epiroc bedriver konkurenskraftiv verksamhet. Målet är att kontinuerligt förbättra sina produkter och tjänster för att tillgodose kundernas behov, förbättra den totala ägandekostnaden, livscykelkostnaden samt att öka försäljningen och vinsten. Epiroc är en ledandeproduktivitetspartner för gruv-, infrastruktur- och naturresursindustrin. Det utvecklar och tillverkar innovativa borriggar, stenbrott- och byggutrustning med toppmodern teknik och tillhandahåller service och förbrukningsvaror i världsklass. Att förbättra maskinens drifttid har alltid varit en kontinuerlig diskussion på grund av kostsamma stopp som orsakats av komponentfel. Om det uppstår större komponentfel som leder till att fordonet går sönder ochtidig feldetektering inte existerar, leder detta till att ingenjörer och tekniker måste spendera mycket tid på att identifiera orsaken till felet. På grund av detta står maskinen stilla tills problemet identifieras och repareras vilket skapar en negativ inverkan på kundnöjdheten. Viktiga drivkomponenter som axlar, växellådor, transmission, vridmomentomvandlare, koppling och lager är exempel på sådana kritiska komponenter vars fel resulterar i kostnadstopp. För att förbättra drifttiden, minska MTTR och skapa en konkurrensfördel för Epiroc-produkter, presenteras ett drivsystemsensorsystem i denna rapport som möjliggör övervakning av realtidsdata från dessa drivlinekomponenter. På detta sätt övervakas drivlinanshälsa kontinuerligt och om det uppstår någon försämring kan planerat underhåll planeras i godtid och reservdelslager hanteras effektivare. Dessutom kommer detta att skapa konkurrenskraft för Epiroc-produkter på marknaden.