Capacity flexibility of a maintenance service provider in specialized and commoditized system environments

In the last decades, after-sales services have become increasingly important since service is a source of differentiation as well as a lucrative business opportunity due to the substantial amount of revenue that can be generated from the products in use throughout their life cycle. Following this trend, many after-sales service providers have emerged in the market or evolved as semi-autonomous units within the OEM (Original Equipment Manufacturer) companies. In this thesis, we focus on the maintenance aspect of after-sales services. We assume that a maintenance service provider (MSP) is running a repair shop in an environment with numerous operating systems that are prone to failure. The MSP is responsible for keeping all systems in an environment up and working. We mainly focus on two types of environments: 1) Specialized System Environment 2) Commoditized System Environment. The systems in the first environment are highly customized. They are designed and built specifically following the owners’ precise requirements. Defense systems, specific lithography systems, mission aircrafts or other advanced/complex, engineer-to-order capital goods are examples of such specialized systems. Due to the diversity of owners’ requirements, each system develops many unique characteristics, which make it hard, if not impossible, to find a substitute for the system, in the market as a whole. In the second environment, the systems are more generic in terms of their functionality. Trucks, cranes, printers, copy machines, forklifts, computer systems, cooling towers, some common medical devices (i.e. anesthesia, x-ray and ultrasound machines, etc…), power systems are examples of such more commoditized systems. Due to the more generic features of the owners’ requirements, it is easier to find a substitute for a system in the market, with more or less the same functionality, for short-term hiring purposes. Upon a system breakdown, the defective unit (system/subsystem) is sent to the repair shop. MSP is responsible for the repair and also liable for the costs related to the down time. In order to alleviate the down-time costs, there are chiefly two different downtime service strategies that the MSP can follow, depending on the environment the repair shop is operating in. In the specialized system environment, the MSP holds a spare unit inventory for the critical subsystem that causes most of the failures. The downtime service related decision in such a case would be the inventory level of the critical spare subsystems. On the other hand, in the commoditized system environment, rather than keeping a spare unit inventory, the MSP hires a substitute system from an agreed rental store/3rd party supplier. The downtime service related decision in this case is the hiring duration. Next to the above downtime service related decisions, repair shop’s capacity level is the other primary determinant of the systems’ uptime/availability. Since maintenance is a labor-intensive industry, the capacity costs constitute a large portion of the total costs. Increasing pressure on profitability and the growing role of External Labor Supplier Agencies motivate service provider firms to scrutinize the prospects and possibilities of capacity flexibility by using contingent workforce. For various reasons, flexible capacity practices in real life are often periodic, and the period length is both a decision parameter and a metric for flexibility. A shorter period length implies more frequent adapting possibilities and a better tailoring of the capacity. On the other hand, the flexible capacity cost per unit time is higher for shorter period lengths due to the compensating wage differentials, which models the relation between the wage rate and the unpleasantness, risk or other undesirable attributes of the job. Certainly, short period length in this context is an undesirable attribute for the flexible capacity resource, as it mandates the resource to switch tasks and to be ready/available more frequently, without the guarantee that s/he will be actually employed. Therefore, we propose several empirically testable functional forms for the cost rate of a flexible capacity unit, which are decreasing with the period length and, in the limit, approaches to the cost rate of a permanent capacity unit from above. In the light of discussions above, we investigate three different capacity modes in this dissertation: ¿ Fixed Capacity Mode: In this mode, all of the capacity is permanent and ready for use in the repair shop. This mode serves as a reference point in order to assess the benefits of other flexible capacity modes. The relevant capacity decision in this mode is the single capacity level of the repair shop. ¿ Periodic Two-Level Capacity Mode: In this mode, we assume two levels of repair shop capacity: permanent and permanent plus contingent capacity levels. The permanent capacity is always available in the system, whereas the deployment of the contingent capacity is decided at the start of each period based on the number of units waiting to be repaired in the shop. The relevant capacity decisions in this mode are the permanent and contingent capacity levels, the period length and the states (in terms of number of defective units waiting) where the contingent capacity is deployed. ¿ Periodic Capacity Sell-Back Mode: In this mode, the failed units are sent to the repair shop at regular intervals in time. Due to this admission structure, when the repair of all the defective units in the repair shop are completed in a period, it is known that no new defective parts will arrive to the shop at least until the start of the next period. This certainty in idle times allows for a contract, where the repair shop capacity is sold at a reduced price to the capacity agency where it is assigned to other tasks until the start of the next period. The original cost of the multi-skilled repair shop capacity per time unit is higher than the permanent capacity cost that is mentioned in previous modes due to the compensation factors such as additional skills, frequent task switching and transportation/transaction costs. Similar to the previous capacity mode, the compensation decreases with the length of the period length. The relevant capacity decisions in this mode are the capacity level and the period length. The primary goal of this thesis is to develop quantitative models and methods for taking optimal capacity decisions for the repair shop in the presence of the capacity modes described above and to integrate these decisions with the other downtime service decisions of the MSP for two different types of system environments (specialized vs. commoditized). After the introduction of the problem, concepts and literature review are given in Chapters 1. In Chapter 2, we focus on the use of capacity flexibility in the repair operations of the MSP in specialized system environment. The capacity related decisions are integrated with the decision on the stock level of the spare unit inventory for all three capacity modes. In Chapter 3 we investigate the same three capacity modes in a (partially) commoditized system environment, where hiring a substitute system for a pre-determined, uniform duration becomes the conventional method upon a failure. In this chapter the decision on the hiring duration is integrated with the other capacity related decisions. Then we provide some preliminary analysis and give the early results on the hybrid strategy where both "keeping stock" and "hire substitute" strategies are followed. Finally in Chapter 4, we summarize our results, give the conclusion and discuss the topics covered in this thesis with a brief exploration on the future research. The numerical results reveal that, in both specialized and commoditized system environments, substantial cost savings (up to 70%) can be achieved under periodic two-level capacity and periodic capacity sell-back modes compared to the fixed capacity mode. However, both period length and the compensation scheme of the capacity resources greatly influence the savings, even in some cost instances, flexible modes (periodic two-level and capacity sell-back) become less economical compared to the fixed capacity mode. Cost parameter instances in which each of the 3 capacity modes becomes cost-optimal, the characteristics of the cost savings and the sensitivity analysis of cost/policy parameters are investigated in both of the system environments in Chapter 2 and Chapter 3, respectively. In the commoditized system environment, under the same cost parameter settings, the hiring substitute from an external supplier for a fixed duration causes a better, more refined and certain control compared to keeping an inventory. Hybrid strategy, in which a substitute is hired after a stock-out instance, is applicable in commoditized as well as commoditizing (previously specialized systems that are in the ongoing commoditization process) system environments. Hybrid strategy outperforms both "only keeping stock" and "only hiring substitute" alternatives; however, in the commoditized system environment, a MSP may still have a proclivity to employ the "hiring substitute" strategy only, because it does not require any initial investment, which is convenient for SMEs. These issues will be explicated further in Chapter 5. We believe that the framework, the design and analysis of the problems addressed as well as the results and the insights obtained in this dissertation can help and motivate other researchers/practitioners to further investigate the cost saving prospects from capacity flexibility in maintenance service operations. We also anticipate that the commoditization framework described in this thesis will be increasingly useful in the future, since the commoditization of the parts/machines will be much more widespread, pushing all the after-sales service providers to compete on the efficiency of their operatio

A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

The responsive reply chain: the influence of the positioning of decoupling points

Manufacturing supply chains have been challenged by high competition, dynamic, and stochastic conditions. They have to be constantly responsive in today’s ever-changing manufacturing environment. The proper positioning of decoupling points for material flow and information flow has a significant potential for increasing responsiveness in a supply chain. Positioning the material decoupling point as close to the end consumer as possible whilst the information decoupling point is positioned upstream is the key to the industries’ ability to reduce lead time and enhance performance in the dynamic behaviour of the supply chain. [Continues.

Queuing network model of uniformly distributed arrivals in a distributed supply chain using subcontracting

International audienceIn this paper, a supply chain (four-input three-stage queuing network) receives uniformly distributed orders from clients. An input order is represented by two stochastic variables, occurrence time and the quantity of items to be delivered. The objective of this work is to compute the minimum response time, and thus the average number of items (optimum capacity) that can be delivered with this response time. Performance measures such as average queue lengths, average response times, and average waiting times of the jobs in the supply chain, and in the equivalent single-server network are derived, plotted and discussed

Milk Run Design: Definitions, Concepts and Solution Approaches

Efficient inbound networks in the European automotive industry rely on a set of different transport concepts including milk runs - understood as regularly scheduled pickup tours. The complexity of designing such a mixed network makes decision support necessary: In this book we provide definitions, mathematical models and a solution method for the Milk Run Design problem and introduce indicators assessing the performance of established milk runs in relation to alternative transport concepts

Performance Analysis and Capacity Planning of Multi-stage Stochastic Order Fulfilment Systems with Levelled Order Release and Order Deadlines

Kundenorientierte Auftragsbearbeitungsprozesse in Logistik- und Produktionssystemen sind heutzutage mit einem kontinuierlich steigenden Auftragsvolumen zunehmend kleinvolumiger Aufträge, hohen Kundenanforderungen hinsichtlich kurzfristiger und individueller Lieferfristen und einer stark stochastisch schwankenden Kundennachfrage konfrontiert. Um trotz der volatilen Kundennachfrage eine effiziente Auftragsbearbeitung und die Einhaltung der kundenindividuellen Lieferfristen gewährleisten zu können, muss die Arbeitslast kundenorientierter Auftragsbearbeitungsprozesse auf geeignete Weise geglättet werden. Hopp und Spearman (2004) unterscheiden zur Kompensation von Schwankungen in Produktionssystemen zwischen den Dimensionen Bestand, Zeit und Kapazität. Diese stellen auch einen guten Ausgangspunkt für die Entwicklung von Glättungskonzepten für stochastische, kundenorientierte Bearbeitungsprozesse dar. In dieser Arbeit werden die Potentiale der Dimensionen Zeit und Kapazität in der Strategie der nivellierten Auftragseinlastung zusammengeführt, um die Arbeitslast mehrstufiger, stochastischer Auftragsbearbeitungsprozesse mit kundenindividuellen Fälligkeitsfristen auf taktischer Ebene zeitlich zu glätten. Ziel dieser Arbeit ist (1) die Entwicklung eines Glättungskonzeptes, der so genannten Strategie der nivellierten Auftragseinlastung, (2) die Entwicklung eines zeitdiskreten analytischen Modells zur Leistungsanalyse und (3) die Entwicklung eines Algorithmus zur Kapazitätsplanung unter Gewährleistung bestimmter Leistungsanforderungen für mehrstufige, stochastische Auftragsbearbeitungsprozesse mit nivellierter Auftragseinlastung und kundenindividuellen Fälligkeitsfristen. Die Strategie der nivellierten Auftragseinlastung zeichnet sich durch die Bereitstellung zeitlich konstanter Kapazitäten für die Auftragsbearbeitung und eine Auftragsbearbeitung gemäß aufsteigender Fälligkeitsfristen aus. Auf diese Weise wird der zeitliche Spielraum jedes Auftrags zwischen dessen Auftragseingang und dessen Fälligkeitsfrist systematisch zur Kompensation der stochastischen Nachfrageschwankungen genutzt. Die verbleibende Variabilität wird in Abhängigkeit der Leistungsanforderungen der Kunden durch die Höhe der bereitgestellten Kapazität kompensiert. Das analytische Modell zur Leistungsanalyse mehrstufiger, stochastischer Auftragsbearbeitungsprozesse mit nivellierter Auftragseinlastung und kundenindividuellen Fälligkeitsfristen bildet die Auftragsbearbeitung als zeitdiskrete Markov-Kette ab und berechnet verschiedene stochastische und deterministische Leistungskenngrößen auf Basis deren asymptotischer Zustandsverteilung. Diese Kenngrößen, wie beispielsweise Durchsatz, Servicegrad, Auslastung, Anzahl Lost Sales sowie Zeitpuffer und Rückstandsdauer eines Auftrags, ermöglichen eine umfassende und exakte Leistungsanalyse von mehrstufigen, stochastischen Auftragsbearbeitungsprozessen mit nivellierter Auftragseinlastung und kundenindividuellen Fälligkeitsfristen. Der Zusammenhang zwischen der bereitgestellten Kapazität und der damit erreichbaren Leistungsfähigkeit kann nicht explizit durch eine mathematische Gleichung beschrieben werden, sondern ist implizit durch das analytische Modell gegeben. Daher ist das Entscheidungsproblem der Kapazitätsplanung unter Gewährleistung bestimmter Leistungsanforderungen ein Blackbox-Optimierungsproblem. Die problemspezifischen Konfigurationen der Blackbox-Optimierungsalgorithmen Mesh Adaptive Direct Search und Surrogate Optimisation Integer ermöglichen eine zielgerichtete Bestimmung des minimalen prozessspezifischen Kapazitätsbedarfs, der zur Gewährleistung der Leistungsanforderungen der Kunden bereitgestellt werden muss. Diese werden anhand einer oder mehrerer Leistungskenngrößen des Auftragsbearbeitungsprozesses spezifiziert. Numerische Untersuchungen zur Beurteilung der Leistungsfähigkeit der Strategie der nivellierten Auftragseinlastung zeigen, dass in Systemen mit einer Auslastung größer als 0,6 durch den Einsatz der Strategie der nivellierten Auftragseinlastung ein deutlich höherer $\alpha$- und $\beta$-Servicegrad erreicht werden kann als mit First come first serve. Außerdem ist der Kapazitätsbedarf zur Gewährleistung eines bestimmten $\alpha$-Servicegrads bei Einsatz der Strategie der nivellierten Auftragseinlastung höchstens so hoch wie bei Einsatz von First come first serve