Models for capacity acquisition decisions considering operational costs

In this article, we study a capacity acquisition problem by considering technology choice and operational factors in a stochastic environment. The motivation for our work comes from developments in modern flexible technologies and a problem encountered in a real industrial setting. We study the impact of operational factors such as setup times, demand patterns, and inventory/back order costs on the decisions of capacity acquisition and technology choice. We consider three alternatives in capacity and technology decisions: (i) a flexible system, (ii) a dedicated system, and (iii) a combination of these two systems. For each system, we develop a model that integrates investment decisions and operational decisions to determine an optimal amount of capacity to purchase and the time and the types of parts to produce. The objective is to minimize the capacity acquisition cost at the beginning of the planning horizon and the total expected operational costs over an infinite planning horizon. To solve the problem in this article, a solution procedure is proposed. Managerial insights are also derived from extensive computational results.     

Flexible versus dedicated technology: A capacity expansion model

Over the past two decades, flexible manufacturing systems have been adopted in a variety of industries. Shorter product lives have necessitated acquiring flexible facilities. Further, the economies of scope that can be derived from producing products with different seasonality have made flexible facilities attractive. At the same time, dedicated facilities (such as transfer lines) have not disappeared because they offer economies of scale beneficial in high volume manufacturing environments. Hence, when producing a wide variety of products with different seasonality and demand growth rates, firms need to consider both economies of scale and economies of scope in deciding whether to acquire capacity of flexible or dedicated technology or both. In this paper, we consider the problem of making capacity acquisition decisions, when there is a choice of flexible and dedicated technologies available in a deterministic multi-product manufacturing environment with demand assumed to be nondeclining over time. We present a novel formulation of the problem and show the equivalence of the formulation to the uncapacitated plant location formulation. This enables us to use a very efficient solution procedure (Erlenkotter 1978) to solve even large problems optimally. We then present the results of an extensive computational study performed to evaluate the impact of key problem parameters on the proportion of flexible and dedicated capacity acquired. An interesting result of the study is that the proportion of flexible capacity acquired decreases with increasing scale economies. We also find that investment in flexible technology is significant even when it is relatively more expensive than dedicated technology.     

Analysis of flexibility with rationing for a mix of manufacturing facilities

A major competitive advantage of a flexible manufacturing facility is its ability to cope with uncertainties in demand. At a strategic level, capacity-size decisions for a mix of flexible facilities (each not necessarily producing the same combination of products) are made based on aggregates of product types. Such an approach overlooks possible capacity-devouring by some products, arising at the operational level, when the aggregate demand for the period exceeds the available capacity. A rationing policy is required to ensure that the available aggregate capacity of the facilities is shared equitably. In this article, it is shown that such a rationing policy has an impact on the required capacity size and, therefore, must be integrated with the decisions at the strategic level. Several properties indicating the relative preferences of certain facility strategies are also established.     

An innovative SNP genotyping method adapting to multiple platforms and throughputs

Key message

An innovative genotyping method designated as semi-thermal asymmetric reverse PCR (STARP) was developed for genotyping individual SNPs with improved accuracy, flexible throughputs, low operational costs, and high platform compatibility.

Abstract

Multiplex chip-based technology for genome-scale genotyping of single nucleotide polymorphisms (SNPs) has made great progress in the past two decades. However, PCR-based genotyping of individual SNPs still remains problematic in accuracy, throughput, simplicity, and/or operational costs as well as the compatibility with multiple platforms. Here, we report a novel SNP genotyping method designated semi-thermal asymmetric reverse PCR (STARP). In this method, genotyping assay was performed under unique PCR conditions using two universal priming element-adjustable primers (PEA-primers) and one group of three locus-specific primers: two asymmetrically modified allele-specific primers (AMAS-primers) and their common reverse primer. The two AMAS-primers each were substituted one base in different positions at their 3′ regions to significantly increase the amplification specificity of the two alleles and tailed at 5′ ends to provide priming sites for PEA-primers. The two PEA-primers were developed for common use in all genotyping assays to stringently target the PCR fragments generated by the two AMAS-primers with similar PCR efficiencies and for flexible detection using either gel-free fluorescence signals or gel-based size separation. The state-of-the-art primer design and unique PCR conditions endowed STARP with all the major advantages of high accuracy, flexible throughputs, simple assay design, low operational costs, and platform compatibility. In addition to SNPs, STARP can also be employed in genotyping of indels (insertion–deletion polymorphisms). As vast variations in DNA sequences are being unearthed by many genome sequencing projects and genotyping by sequencing, STARP will have wide applications across all biological organisms in agriculture, medicine, and forensics.

     

Process flexibility: design, evaluation, and applications

One of the most effective ways of minimizing supply/demand mismatch costs, with little increase in operational costs, is to deploy valuable resources in a flexible and timely manner to meet the realized demand. This notion of flexible processes has significantly changed operations in many manufacturing and service companies. For example, a flexible production system is now commonly used by automobile manufacturers, and a workforce cross-training system is now a common practice in many service industries. However, there is a trade-off between the level of flexibility available in the system and the associated complexity and operating costs. The challenge is to have the “right” level of flexibility to capture the bulk of the benefits from a fully flexible system, while controlling the increase in implementation costs. This paper reviews developments in process flexibility over the past decade. In particular, we focus on the phenomenon, often observed in practice, that a slight increase in process flexibility can lead to a significant improvement in system performance. This review explores the issues from three perspectives: design, evaluation, and applications. We also discuss how the concept of process flexibility has been deployed in several manufacturing and service systems.     

Batch sizes and lead-time performance in flexible manufacturing systems

Production lead-time performance in flexible manufacturing systems is influenced by several factors which include: machine groupings, demand rates, machine processing rates, product batching, material handling system capacity, and so on. Hence, control of lead-time performance can be affected through the manipulation of one or more of these variables. In this article, we investigate the potential of batch sizing as a control variable for lead-time performance through the use of a queueing network model. We establish a functional relationship between the two variables, and incorporate the relationship in an optimization model to determine the optimal batch size(s) which minimizes the sum of annual work-in-process inventory and final inventory costs. The nonlinear batch sizing problem which results is solved by discrete optimization via marginal analysis. Results show that batch sizing can be a cheap and effective variable for controlling flexible manufacturing system throughput.     

An Evaluative Study of Operation Grouping Policies in an FMS

The increased use of flexible manufacturing systems to provide customers with diversified products efficiently has created a significant set of operational challenges for managers. This technology poses a number of decision problems that need to be solved by researchers and practitioners. In the literature, there have been a number of attempts to solve design and operational problems. Special attention has been given to machine loading problems, which involve the assignment of job operations and allocation of tools and resources to optimize specific measures of productivity. Most existing studies focus on modeling the problem and developing heuristics in order to optimize certain performance metrics rather than on understanding the problem and the interaction between the different factors in the system. The objective of this paper is to study the machine loading problem. More specifically, we compare operation aggregation and disaggregation policies in a random flexible manufacturing system (FMS) and analyze its interaction with other factors such as routing flexibility, sequencing flexibility, machine load, buffer capacity, and alternative processing-time ratio. For this purpose, a simulation study is conducted and the results are analyzed by statistical methods. The analysis of results highlights the important factors and their levels that could yield near-optimal system performance.     

Flexibility and Pricing Decisions for High-Volume Products with Short Life Cycles

We examine the competitive implications of a firm's ability to change over its facility for the manufacture of successive generations of high-volume products with short life cycles. This ability is known as changeover flexibility. The model introduced extends the existing literature in several directions. First, the model offers explicit treatment of the critical relationships between market entry time, changeover flexibility, product life cycles, and profit. Second, the model explicitly considers the effect of early market entry on the accumulation of manufacturing experience (learning), which reduces the unit production cost. Third, the product's optimal selling price is determined and its relation to the firm's changeover flexibility is examined. Last, facility flexibility is permitted to vary over a continuum. Therefore, we are able to capture decision making concerning the optimal degree of changeover flexibility. Both analytic and numerical results are reported, demonstrating the link between the operations and marketing domains in the context of a firm's optimal entrance and exit strategies. Among the key findings are (1) a firm more capable of reducing operating costs through learning over short life cycles optimally invests in more changeover flexibility, charges higher prices, and obtains greater profit; and (2) as the cost of flexible technologies decrease, a firm optimally increases its investment in changeover flexibility, enters markets earlier, and charges higher average prices over the product's life cycle.     

Comparison between centralized and decentralized supply chains of autologous chimeric antigen receptor T-cell therapies: a UK case study based on discrete event simulation

Background aimsDecentralized, or distributed, manufacturing that takes place close to the point of care has been a manufacturing paradigm of heightened interest within the cell therapy domain because of the product's being living cell material as well as the need for a highly monitored and temperature-controlled supply chain that has the potential to benefit from close proximity between manufacturing and application.MethodsTo compare the operational feasibility and cost implications of manufacturing autologous chimeric antigen receptor T (CAR T)-cell products between centralized and decentralized schemes, a discrete event simulation model was built using ExtendSIM 9 for simulating the patient-to-patient supply chain, from the collection of patient cells to the final administration of CAR T therapy in hospitals. Simulations were carried out for hypothetical systems in the UK using three demand levels—low (100 patients per annum), anticipated (200 patients per annum) and high (500 patients per annum)—to assess resource allocation, cost per treatment and system resilience to demand changes and to quantify the risks of mix-ups within the supply chain for the delivery of CAR T treatments.ResultsThe simulation results show that although centralized manufacturing offers better economies of scale, individual facilities in a decentralized system can spread facility costs across a greater number of treatments and better utilize resources at high demand levels (annual demand of 500 patients), allowing for an overall more comparable cost per treatment. In general, raw material and consumable costs have been shown to be one of the greatest cost drivers, and genetic modification-associated costs have been shown to account for over one third of raw material and consumable costs. Turnaround time per treatment for the decentralized scheme is shown to be consistently lower than its centralized counterpart, as there is no need for product freeze-thaw, packaging and transportation, although the time savings is shown to be insignificant in the UK case study because of its rather compact geographical setting with well-established transportation networks. In both schemes, sterility testing lies on the critical path for treatment delivery and is shown to be critical for treatment turnaround time reduction.ConclusionsConsidering both cost and treatment turnaround time, point-of-care manufacturing within the UK does not show great advantages over centralized manufacturing. However, further simulations using this model can be used to understand the feasibility of decentralized manufacturing in a larger geographical setting.     

Average-Cost Optimal Policies for an Unreliable Flexible Multiproduct Machine

This paper is concerned with optimal production planning on a single failure-prone flexible machine that produces N distinct part types. The machine is flexible in the sense that no setup is required for switching from production of one part type to another. We consider the problem of controlling production rates to minimize the expected long-run average cost of product surpluses over time. We assume constant unit holding and shortage costs and constant demand rates for the part types. Moreover, the costs are assumed to be the same for all products. We provide an explicit optimal solution for the problem.     

Clustering algorithms to optimize the tool handling system management in an FMS

Tool management is recognized as a critical issue in flexible manufacturing facilities management. This article addresses the issue of tool management in a flexible system installed in an avionics components factory. The system is composed of two machining centers equipped with local tool magazines of limited capacity. A tool handling system is in charge of tool movements between the tool room and the two machines. Each machine is able to perform any operation, provided that it is equipped with the suitable tool. In this kind of installation, tool allocation must be determined, and tool movements must be synchronized in order to minimize operating costs, or, equivalently, maximize the productivity of the system. We propose an approach to production planning based on a clustering algorithm, which takes into account the tool requirements of each part program in the production batch. We also propose two different heuristics for the scheduling problem. A case study was conducted on the facility mentioned above. Two conflicting objectives can be identified for this kind of production system: the reduction of tools to be shared among machines and the reduction of workload unbalance. The tests and comparison made demonstrate how the proposed procedure leads to superior results in terms of both objectives.     

Management of product variety in cellular manufacturing systems

In today’s markets, non-uniform, customized products complicate the manufacturing processes significantly. In this paper, we propose a cellular manufacturing system design model to manage product variety by integrating with the technology selection decision. The proposed model determines the product families and machine groups while deciding the technology of each cell individually. Hedging against changing market dynamics leads us to the use of flexible machining systems and dedicated manufacturing systems at the same facility. In order to integrate the market characteristics in our model, we proposed a new cost function. Further, we modified a well known similarity measure in order to handle the operational capability of the available technology. In the paper, our hybrid technology approach is presented via a multi-objective mathematical model. A filtered-beam based local search heuristic is proposed to solve the problem efficiently. We compare the proposed approach with a dedicated technology model and showed that the improvement with the proposed hybrid technology approach is greater than 100% in unstable markets requiring high product varieties, regardless of the volumes of the products.     

Operational Changes in a Shared Resource Laboratory with the Use of a Product Lifecycle Management Approach: A Case Study

Shared Resource Laboratories (SRLs) provide investigators access to necessary scientific and resource expertise to leverage complex technologies fully for advancing high-quality biomedical research in a cost-effective manner. At the University of Nebraska Medical Center, the Flow Cytometry Research Facility (FCRF) offered access to exceptional technology, but the methods of operation were outdated and unsustainable. Whereas technology has advanced and the institute has expanded, the operations at the facility remained unchanged for 35 yr. To rectify this, at the end of 2013, we took a product lifecycle management approach to affect large operational changes and align the services offered with the SRL goal of education, as well as to provide service to researchers. These disruptive operational changes took over 10 mo to complete and allowed for independent end-user acquisition of flow cytometry data. The results have been monitored for the past 12 mo. The operational changes have had a positive impact on the quality of research, increased investigator-facility interaction, reduced stress of facility staff, and increased overall use of the resources. This product lifecycle management approach to facility operations allowed us to conceive of, design, implement, and monitor effectively the changes at the FCRF. This approach should be considered by SRL management when faced with the need for operationally disruptive measures.     

Good Manufacturing Practices (GMP) manufacturing of advanced therapy medicinal products: a novel tailored model for optimizing performance and estimating costs

Background aimsAdvanced therapy medicinal products (ATMP) have gained considerable attention in academia due to their therapeutic potential. Good Manufacturing Practice (GMP) principles ensure the quality and sterility of manufacturing these products. We developed a model for estimating the manufacturing costs of cell therapy products and optimizing the performance of academic GMP-facilities.MethodsThe “Clean-Room Technology Assessment Technique” (CTAT) was tested prospectively in the GMP facility of BCRT, Berlin, Germany, then retrospectively in the GMP facility of the University of California-Davis, California, USA. CTAT is a two-level model: level one identifies operational (core) processes and measures their fixed costs; level two identifies production (supporting) processes and measures their variable costs. The model comprises several tools to measure and optimize performance of these processes. Manufacturing costs were itemized using adjusted micro-costing system.ResultsCTAT identified GMP activities with strong correlation to the manufacturing process of cell-based products. Building best practice standards allowed for performance improvement and elimination of human errors. The model also demonstrated the unidirectional dependencies that may exist among the core GMP activities. When compared to traditional business models, the CTAT assessment resulted in a more accurate allocation of annual expenses. The estimated expenses were used to set a fee structure for both GMP facilities. A mathematical equation was also developed to provide the final product cost.ConclusionsCTAT can be a useful tool in estimating accurate costs for the ATMPs manufactured in an optimized GMP process. These estimates are useful when analyzing the cost-effectiveness of these novel interventions.     

Mercury Free Microscopy: An Opportunity for Core Facility Directors

Mercury Free Microscopy (MFM) is a new movement that encourages microscope owners to choose modern mercury free light sources to replace more traditional mercury based arc lamps. Microscope performance is enhanced with new solid state technologies because they offer a more stable light intensity output and have a more uniform light output across the visible spectrum. Solid state sources not only eliminate mercury but also eliminate the cost of consumable bulbs (lifetime ∼200 hours), use less energy, reduce the instrument down time when bulbs fail and reduce the staff time required to replace and align bulbs. With lifetimes on the order of tens of thousands of hours, solid state replacements can pay for themselves over their lifetime with the omission of consumable, staff (no need to replace and align bulbs) and energy costs. Solid state sources are also sustainable and comply with institutional and government body mandates to reduce energy consumption, carbon footprints and hazardous waste. MFM can be used as a mechanism to access institutional financial resources for sustainable technology through a variety of stakeholders to defray the cost to microscope owners for the initial purchase of solid state sources or the replacement cost of mercury based sources. Core facility managers can take a lead in this area as “green” ambassadors for their institution by championing a local MFM program that will save their institution money and energy and eliminate mercury from the waste stream. Managers can leverage MFM to increase the visibility of their facility, their impact within the institution, and as a vital educational resource for scientific and administrative consultation.     

Impact of ramp-up on the optimal capacity-related reconfiguration policy

This paper presents an optimal solution, based on Markov decision theory, for the problem of optimal capacity-related reconfiguration of manufacturing systems, under stochastic market demand. Both capacity expansion and reduction are considered. The solution quantitatively takes into account the effect of the ramp-up phenomenon, following each reconfiguration, on the optimal policy. A closed-form solution is presented for when product demand is independently and generally distributed over time. A real case concerning a flexible manufacturing line in the automotive sector is shown, to prove that ignoring the ramp-up effect in the decision process can lead to significant increases in overall costs.     

An overview of energy efficiency techniques in cluster computing systems

Two major constraints demand more consideration for energy efficiency in cluster computing: (a) operational costs, and (b) system reliability. Increasing energy efficiency in cluster systems will reduce energy consumption, excess heat, lower operational costs, and improve system reliability. Based on the energy-power relationship, and the fact that energy consumption can be reduced with strategic power management, we focus in this survey on the characteristic of two main power management technologies: (a) static power management (SPM) systems that utilize low-power components to save the energy, and (b) dynamic power management (DPM) systems that utilize software and power-scalable components to optimize the energy consumption. We present the current state of the art in both of the SPM and DPM techniques, citing representative examples. The survey is concluded with a brief discussion and some assumptions about the possible future directions that could be explored to improve the energy efficiency in cluster computing.     

Economic and practical challenges to the formulation of vaccines against endemic infectious diseases such as malaria

Herein, we analyze in general the current vaccine market and identify potential factors driving and modulating supply and demand for vaccines. An emphasis is placed on changes in regulation in the last 20 years which have led to increased indirect costs of production, and which can create a barrier against the timely use of technological advances to reduce direct costs. Other defining industry characteristics, such as firm numbers and sizes, cost and pricing strategies, nature extent and impact of Government involvement and international regulation are noted. These considerations, far from being removed from basic vaccine research, influence its ability to achieve aims that can be then progressed into effective vaccine products. We discuss specifically the development of particulate vaccines against malaria, a major lethal disease and health problem prevalent in Africa, including some key economic and methodological challenges and opportunities. We note some practical issues blocking the development of effective particulate vaccines for the Third World, mainly driven by the regulatory spiral noted above.     

Optimal Locations for Methanol and CHP Production in Eastern Finland

Finland considers energy production from woody biomass as an efficient energy planning strategy to increase the domestic renewable energy production in order to substitute fossil fuel consumption and reduce greenhouse gas emissions. Consequently, a number of developmental activities are implemented in the country, and one of them is the installation of second generation liquid biofuel demonstration plants. In this study, two gasification-based biomass conversion technologies, methanol and combined heat and power (CHP) production, are assessed for commercialization. Spatial information on forest resources, sawmill residues, existing biomass-based industries, energy demand regions, possible plant locations, and a transport network of Eastern Finland is fed into a geographically explicit Mixed Integer Programming model to minimize the costs of the entire supply chain which includes the biomass supply, biomass and biofuel transportation, biomass conversion, energy distribution, and emissions. The model generates a solution by determining the optimal number, locations, and technology mix of bioenergy production plants. Scenarios were created with a focus on biomass and energy demand, plant characteristics, and cost variations. The model results state that the biomass supply and high energy demand are found to have a profound influence on the potential bioenergy production plant locations. The results show that methanol can be produced in Eastern Finland under current market conditions at an average cost of 0.22??/l with heat sales (0.34??/l without heat sales). The introduction of energy policy tools, like cost for carbon, showed a significant influence on the choice of technology and CO₂ emission reductions. The results revealed that the methanol technology was preferred over the CHP technology at higher carbon dioxide cost (>145??/t_CO2). The results indicate that two methanol plants (360?MW_biomass) are needed to be built to meet the transport fuel demand of Eastern Finland.     

Cost modelling of Pseudomonas fluorescens and Pseudomonas chlororaphis as biocontrol for competitive exclusion of Salmonella enterica on tomatoes

Published research on process-based models for biocontrol of foodborne pathogens on produce is limited. The aim of this research was to develop cost model estimates for competitive exclusion (CE) process using Pseudomonas fluorescens and Pseudomonas chlororaphis (non-plant pathogenic and non-human pathogen) as biocontrol against Salmonella enterica on tomatoes. Cost estimates were based on material inputs, equipment, facilities, and projected processing conditions of post-harvest packaging of tomatoes. The microbiological data for inactivation of S. enterica was based on published papers. The small-scale processing facility was assumed to have a processing capacity of 2000 kg of tomatoes/hour for 16 h per day, operational 6 days a week, and for 3-months /year. The large-scale facility was assumed to have a processing capacity of 100,000 kg of tomatoes/hour. Estimated initial capital investment costs for small and large-scale models (production facility) were US$391,000 and US$2.1 million. Application of CE for biocontrol of S. enterica on tomatoes was estimated at US$0.0058–0.073/kg of tomatoes during commercial processing operations. This exceeds chlorine wash technology estimated at US$0.00046/kg and is competitive with gaseous chlorine dioxide at US$0.02–0.21/kg. For high-value produce, CE may complement existing technologies increase food safety, reduce storage loses, and extend shelf life of produce.