Controlling the order pool in make-to-order production systems

Voor ‘Make-To-Order’ (MTO, oftewel klantordergestuurde) productiesystemen is de tijd die orders moeten wachten op beschikbare productiecapaciteit cruciaal. Het beheersen van die wachttijd is van groot belang om zowel korte als betrouwbare doorlooptijden te realiseren. Daarom analyseerde en ontwierp Remco Germs regels voor orderacceptatie en ordervrijgave, om daarmee de wachttijden te beheersen. Orderacceptatie en -vrijgave zijn de twee belangrijkste mechanismen om de lengte van wachttijden te beïnvloeden en zodoende de productie te sturen. De logistieke prestatie hangt in grote mate af van specifieke kenmerken van MTO-systemen, zoals routing variabiliteit, beperkte productiecapaciteit, omsteltijden, strikte leveringsvoorwaarden en onzekerheid in het aankomstpatroon van orders. Om een beter begrip te krijgen van de afwegingen die MTO-bedrijven in dit opzicht moeten maken richt het proefschrift zich op de modellering van de belangrijkste kenmerken van MTO-systemen. De inzichten die dat oplevert worden vervolgens gebruikt om orderacceptatie- en ordervrijgaveregels te ontwikkelen die eenvoudig te begrijpen en daarom makkelijk in praktijksituaties te implementeren zijn. Deze relatief eenvoudige beslissingsregels kunnen al leiden tot significante verbeteringen in de logistieke prestaties van MTO-bedrijven. The thesis of Remco Germs analyses and develops order acceptance and order release policies to control queues in make-to-order (MTO) production systems. Controlling the time orders spend waiting in queues is crucial for realizing short and reliable delivery times, two performance measures which are of strategic importance for many MTO com-panies. Order acceptance and order release are the two most important production con-trol mechanisms to influence the length of these queues. Their performance depends on typical characteristics of MTO systems, such as random (batch) order arrival, routing variability, fixed capacities, setup times and (strict) due-dates. To better understand the underlying mechanisms of good order acceptance and order release policies the models in this thesis focus on the main characteristics of MTO systems. The insights obtained from these models are then used to develop order acceptance and order release policies that are easy to understand and thereby easy to implement in practice. The results show that these relatively simple policies may already lead to significant performance improvements for MTO companies.

Combined make-to-order and make-to-stock in a food production system

The research into multi-product production/inventory control systems has mainly assumed one of the two strategies: Make-to-Order (MTO) or Make-to-Stock (MTS). In practice, however, many companies cater to an increasing variety of products with varying logistical demands (e.g. short due dates, specific products) and production characteristics (e.g. capacity usage, setup) to different market segments and so they are moving to more MTO-production. As a consequence they operate under a hybrid MTO-MTS strategy. Important issues arising out of such situations are, for example, which products should be manufactured to stock and which ones on order and, how to allocate capacity among various MTO-MTS products. This paper presents the state-of-the-art literature review of the combined MTO-MTS production situations. A variety of production management issues in the context of food processing companies, where combined MTO-MTS production is quite common, are discussed in details. The authors propose a comprehensive hierarchical planning framework that covers the important production management decisions to serve as a starting point for evaluation and further research on the planning system for MTO-MTS situations.

Supply Chain Management with Online Customer Selection

We consider new online variants of supply chain management models, where in addition to production decisions, one also has to actively decide on which customers to serve. Specifically, customers arrive sequentially during a selection phase, and one has to decide whether to accept or reject each customer upon arrival. If a customer is rejected, then a lost-sales cost is incurred. Once the selection decisions are all made, one has to satisfy all the accepted customers with minimum possible production cost. The goal is to minimize the total cost of lost sales and production. A key feature of the model is that customers arrive in an online manner, and the decision maker does not require any information about future arrivals. We provide two novel algorithms for online customer selection problems, which are based on repeatedly solving offline subproblems that ignore previously made decisions. For many important settings, our algorithms achieve small constant competitive ratio guarantees. That is, for any sequence of arriving customers, the cost incurred by the online algorithm is within a fixed constant factor of the cost incurred by the respective optimal solution that has full knowledge upfront on the sequence of arriving customers. Finally, we provide a computational study on the performance of these algorithms when applied to the economic lot sizing and joint replenishment problems with online customer selection.National Science Foundation (U.S.) (CMMI-0846554)United States. Air Force Office of Scientific Research (FA9550-11-1-0150)United States. Air Force Office of Scientific Research (FA9550-08-1-0369

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

Bid price-based revenue management approaches in manufacturing industries

Whenever demand exceeds capacity, the available resources must be allocated to the incoming demand. A simple first-come-first-served allocation can lead to rather poor results if the demand is heterogeneous, i.e., if orders differ in their strategic importance for a company, their willingness to pay, their lead time requirements, etc. In addition, the decisions must be made under uncertainty because demand is often stochastic. If too many low value orders are rejected, capacity might remain unused. On the other hand, if too many low value orders are accepted, there might be not enough capacity to accept all high value orders. Apart from the order acceptance decision, it might also be preferable to delay the order release of already accepted orders, even if capacity is available, to reserve capacity for future high value orders. This thesis considers the demand management decisions in different order-driven production environments. Orders are accepted or rejected immediately upon arrival, and at the beginning of each planning period, the set of orders to be released is selected. As exact solution methods are computationally intractable, bid price-based revenue management approaches are applied to maximize the contribution margin gained by accepted orders minus holding and backlog costs. The first essay considers a deterministic single-stage assembly process, where both capacity and intermediate materials are scarce resources. In this essay, for each accepted order a due date is quoted and the accepted orders are scheduled such that the quoted due dates are kept. The second essay considers a deterministic multi-stage make-to-order production system. In this study, intermediate materials are assumed to be unlimited available. The third essay considers a multi-stage make-to-order production system with stochastic influences. To take into account that in these production systems, lead times depend non-linearly on the load of the system, clearing functions are used to model the production system

A review of revenue management : recent generalizations and advances in industry applications

Originating from passenger air transport, revenue management has evolved into a general and indispensable methodological framework over the last decades, comprising techniques to manage demand actively and to further improve companies’ profits in many different industries. This article is the second and final part of a paper series surveying the scientific developments and achievements in revenue management over the past 15 years. The first part focused on the general methodological advances regarding choice-based theory and methods of availability control over time. In this second part, we discuss some of the most important generalizations of the standard revenue management setting: product innovations (opaque products and flexible products), upgrading, overbooking, personalization, and risk-aversion. Furthermore, to demonstrate the broad use of revenue management, we survey important industry applications beyond passenger air transportation that have received scientific attention over the years, covering air cargo, hotel, car rental, attended home delivery, and manufacturing. We work out the specific revenue management-related challenges of each industry and portray the key contributions from the literature. We conclude the paper with some directions for future research