Capacity planning for batch and perfusion bioprocesses across multiple biopharmaceutical facilities

Production planning for biopharmaceutical portfolios becomes more complex when products switch between fed‐batch and continuous perfusion culture processes. This article describes the development of a discrete‐time mixed integer linear programming (MILP) model to optimize capacity plans for multiple biopharmaceutical products, with either batch or perfusion bioprocesses, across multiple facilities to meet quarterly demands. The model comprised specific features to account for products with fed‐batch or perfusion culture processes such as sequence‐dependent changeover times, continuous culture constraints, and decoupled upstream and downstream operations that permit independent scheduling of each. Strategic inventory levels were accounted for by applying cost penalties when they were not met. A rolling time horizon methodology was utilized in conjunction with the MILP model and was shown to obtain solutions with greater optimality in less computational time than the full‐scale model. The model was applied to an industrial case study to illustrate how the framework aids decisions regarding outsourcing capacity to third party manufacturers or building new facilities. The impact of variations on key parameters such as demand or titres on the optimal production plans and costs was captured. The analysis identified the critical ratio of in‐house to contract manufacturing organization (CMO) manufacturing costs that led the optimization results to favor building a future facility over using a CMO. The tool predicted that if titres were higher than expected then the optimal solution would allocate more production to in‐house facilities, where manufacturing costs were lower. Utilization graphs indicated when capacity expansion should be considered. © 2013 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 30:594–606, 2014     

Beyond make-or-buy: cross-company short-term capacity backup in semiconductor industry ecosystem

Since the uncertainty involved in demand forecast is increasingly amplified with the forecast lead-time, high-tech companies often suffer the risks of oversupply and shortage of capacity that will affect the profitability and growth. High-tech industries including semiconductor and TFT-LCD industries are capital-intensive, in which the capacity plan and corresponding capital investment decisions are critical due to demand fluctuation. Once the capacity is planned, the company may suffer the risks of either low capital-effectiveness due to low capacity utilization and capacity oversupply, or poor customer satisfaction caused by the capacity shortage. Most of the existing studies focused on solving the long-term capacity shortage issue through optimizing the capacity investment plan, or medium-term capacity plan to allocate demands among the wafer fabrication facilities (fabs) to balance the loading and product mix. Focusing on a real setting, this study proposed a systematic decision method to analyze short-term solutions of cross-company capacity backup between the companies in the semiconductor industry ecosystem. In particular, a game theory and decision tree analysis model was developed to support this decision. A case study was conducted with real data of semiconductor manufacturing companies in Taiwan for validation. The results have demonstrated practical viability of this approach. The approach suggested has been implemented in this company.     

Trends in capacity utilization for therapeutic monoclonal antibody production

The administration of high doses of therapeutic antibodies requires large-scale, efficient, cost effective manufacturing processes. An understanding of how the industry is using its available production capacity is important for production planning, and facility expansion analysis. Inaccurate production planning for therapeutic antibodies can have serious financial ramifications. In the recent 5th Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and Production, 434 qualified respondents from 39 countries were asked to indicate, among other manufacturing issues, their current trends and future predictions with respect to the production capacity utilization of monoclonal antibodies in mammalian cell culture systems. While overall production of monoclonals has expanded dramatically since 2003, the average capacity utilization for mammalian cell culture systems, has decreased each year since 2003. Biomanufacturers aggressively attempt to avoid unanticipated high production demands that can create a capacity crunch. We summarize trends associated with capacity utilization and capacity constraints which indicate that biopharmaceutical manufacturers are doing a better job planning for capacity. The results have been a smoothing of capacity use shifts and an improved ability to forecast capacity and outsourcing needs. Despite these data, today, the instability and financial constraints caused by the current global economic crisis are likely to create unforeseen shifts in our capacity utilization and capacity expansion trends. These shifts will need to be measured in subsequent studies.     

Medium term planning of biopharmaceutical manufacture using mathematical programming

Regulatory pressures and capacity constraints are forcing the biopharmaceutical industry to consider employing multiproduct manufacturing facilities running on a campaign basis. The need for such flexible and cost-effective manufacture poses a significant challenge for planning and scheduling. This paper reviews the problem of planning and scheduling of biopharmaceutical manufacture and presents a methodology for the planning of multiproduct biopharmaceutical manufacturing facilities. The problem is formulated as a mixed integer linear program (MILP) to represent the relevant decisions required within the planning process and is tested on two typical biopharmaceutical industry planning problems. The proposed formulation is compared with an industrial rule based approach, which it outperforms in terms of profitability. The results indicate that the developed formulation offers an effective representation of the planning problem and would be a useful decision tool for manufacturers in the biopharmaceutical industry particularly at times of limited manufacturing capacity.     

Trends in capacity utilization for therapeutic monoclonal antibody production

The administration of high doses of therapeutic antibodies requires large-scale, efficient, cost effective manufacturing processes. An understanding of how the industry is using its available production capacity is important for production planning, and facility expansion analysis. Inaccurate production planning for therapeutic antibodies can have serious financial ramifications. In the recent 5th Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and Production, 434 qualified respondents from 39 countries were asked to indicate, among other manufacturing issues, their current trends and future predictions with respect to the production capacity utilization of monoclonal antibodies in mammalian cell culture systems. While overall production of monoclonals has expanded dramatically since 2003, the average capacity utilization for mammalian cell culture systems, has decreased each year since 2003. Biomanufacturers aggressively attempt to avoid unanticipated high production demands that can create a capacity crunch. We summarize trends associated with capacity utilization and capacity constraints which indicate that biopharmaceutical manufacturers are doing a better job planning for capacity. The results have been a smoothing of capacity use shifts and an improved ability to forecast capacity and outsourcing needs. Despite these data, today, the instability and financial constraints caused by the current global economic crisis are likely to create unforeseen shifts in our capacity utilization and capacity expansion trends. These shifts will need to be measured in subsequent studies.Key words: capacity, production, monoclonal antibody, survey, biopharmaceutical, manufacturing, constraints, facilityBuilding new capacity and improving existing systems to meet the demand for new monoclonal antibody (mAb) therapeutics, whether through in-house manufacturing or out-sourced contract manufacturing, has long-term cost implications for biotechnology firms. Bringing new capacity on line requires accurate market knowledge, lead-time, large capital expenditures and careful planning, and understanding trends in capacity utilization for the manufacture of mAbs can be critical to the planning process.For the first three quarters of the twentieth century, the traditional and most efficient way of producing antibodies was to immunize a large vertebrate, bleed the animal and, from the serum, collect the polyclonal protein. Because of their hardy binding specificity, the value of immunoglobulins, especially as a potential therapeutic tool, was evident from the time of their discovery because scientists envisioned them as probes and transporters of therapeutic tools. Limitations of using polyclonals were also evident from early on. Therapeutic antibodies needed to be delivered in very high concentration, and polyclonals, a heterogeneous group of molecules, directed against many different epitopes of an antigenic source, could only be extracted from serum in tiny quantities.A solution to at least some of the problems seemed to appear in 1984 when Kohler and Milstein¹ described a novel method for producing antibodies from an immortalized cell line capable of continually producing a virtually unlimited amount of a single antibody, directed at a single epitope. The medical and scientific communities realized that this hybridoma technology could produce sufficient quantities of antibodies for therapy, and in 1986, the first mAb for human use (Orthoclone OKT3—Ortho Pharmaceuticals) was approved for the prevention of kidney transplant rejection.As hybridoma technology evolved, it was clear that there were obstacles to overcome. The first mAbs were murine, but therapeutic candidates needed to be less immunogenic in order to avoid transplantation incompatibility. So chimeric antibodies and humanized mAbs (part murine, part human), and fully human mab production technologies were developed. The OKT3 approval was followed by a wave of mostly anti-cancer mAbs through the 1990''s, and since then, these proteins have become a dominant component of the biopharmaceutical market, representing approximately 20% of all biologic products, with combined revenues of over $20 billion in 2006.²Administration of high doses of therapeutic antibodies requires large-scale, efficient, cost effective manufacturing processes. Over the past few years, improvements have been made in cell line generation, expression vectors, transfection technology and large-scale cell culture production, allowing biotech firms to successfully move candidates through the pipeline. Today''s technologies are enabling five times the concentration of antibody produced by technologies just 5 years ago.³ Biotechnology drugs, including mAbs, now make up more than one-quarter of the FDA filings for approval, and over 40% of preclinical trials are now large molecule candidates, and as a result, planning for biologics manufacturing will continue to require strategic approaches to avoid potentially disruptive production bottlenecks. The long lead-time required to successfully launch a mAb requires pre-planning for capacity. This planning demands a new level of partnership between manufacturers and suppliers to develop novel technologies that will keep pace with industry''s need for capacity.     

Sustainability in Supply Chain Management: Aggregate Planning from Sustainability Perspective

Supply chain management that considers the flow of raw materials, products and information has become a focal issue in modern manufacturing and service systems. Supply chain management requires effective use of assets and information that has far reaching implications beyond satisfaction of customer demand, flow of goods, services or capital. Aggregate planning, a fundamental decision model in supply chain management, refers to the determination of production, inventory, capacity and labor usage levels in the medium term. Traditionally standard mathematical programming formulation is used to devise the aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard aggregate planning formulation to account for additional environmental and social criteria to incorporate triple bottom line consideration of sustainability. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in aggregate planning. We analyze the revised models and interpret the results on a case study from real life that would be insightful for decision makers.     

城市滨水空间生态系统服务供需匹配的空间智慧

当前生态系统服务空间性研究大多在区域或城市尺度上开展,风景园林生态实践的指导性较为有限,研究聚焦场地尺度,旨在从生态系统服务供需视角看待城市滨水空间的发展与规划设计实践。在已有生态系统服务供需方面文献爬梳的基础上,提出影响需求的三大因素及其需求类型的划分方法,甄别影响城市滨水空间生态系统服务供给能力的核心指标,选取上海苏州河市区段进行实证研究,采取田野调查法、文献研究法,并基于GIS进行需求目标与供给能力的空间制图。通过拟合分析比对创新性地提出指导城市滨水空间生态实践的三大空间智慧：1）基于需求目标研判制定发展策略的空间智慧;2）分段分类型供给满足存量更新需求的空间智慧;3）生态系统服务供需平衡转向匹配的空间智慧,为城市滨水空间的生态实践提出理论依据、评价标准及技术支撑。     

A two-stage stochastic programming approach for new tape-out allocation decisions for demand fulfillment planning in semiconductor manufacturing

Demand fulfillment and capacity utilization directly affects customer satisfaction, market growth, and the profitability of the company in the semiconductor industry. These characteristics boost the significance of allocating various customer demands to a number of wafer fabrication facilities (fabs) with different capacity configurations. Before volume production, the introduction of new semiconductor product, namely new tape-out (NTO), requires extremely sophisticated and lengthy qualification with high-cost masks and pilot runs in the qualified fabs. Thus, the NTO allocation will affect future product mix of the qualified fabs, and the flexibility to fulfill the volume demands of the allocated NTOs in the corresponding fabs. This research aims to construct a two-stage stochastic programming (2-SSP) demand fulfillment model. The first stage considers NTO allocation decisions to a number of qualified fabs before the corresponding demand volume is realized. The second stage allocates the capacity to fulfill demand requirements based on the results of four options of capacity reconfiguration: (1) qualifying a product to more than one fab (share); (2) physically transferring a set of masks for a product from one fab to another, where a requalification is required (transfer); (3) moving tools from under-loaded fabs to over-utilized fabs (backup); and (4) utilizing different technologies to capacity inside a fab to support the technology with insufficient capacities (exchange). Both the share and transfer options require long lead time for qualification, whereas the backup and exchange options can be accomplished within a planned timeframe. A numerical study based on real settings is conducted to estimate the validity of the proposed 2-SSP model via values of stochastic solution (VSS) and expected values of perfect information (EVPI). The results showed the benefits of adopting 2-SSP models, especially in an environment with high-demand fluctuation. Furthermore, the proposed 2-SSP can provide near-optimal solutions similar to those of deterministic models with perfect information.     

Effect of reconfiguration costs on planning for capacity scalability in reconfigurable manufacturing systems

In a globally competitive market for products, manufacturers are faced with an increasing need to improve their flexibility, reliability, and responsiveness to meet the demands of their customers. Reconfigurable manufacturing systems (RMS) have become an important manufacturing paradigm, because they broadly encompass the ability to react efficiently to this environment by providing the exact capacity and functionality needed when needed. This paper studies how such new systems can manage their capacity scalability planning in a cost effective manner. An approach for modeling capacity scalability planning is proposed. The development of the model is based on set theory and the regeneration point theorem which is mapped to the reconfigurable manufacturing paradigm as the capacity scalability points of that system. The cost function of the model incorporates both the physical capacity cost based on capacity size and costs associated with the reconfiguration process which referred to as the scalability penalty cost and scalability effort cost. A dynamic programming (DP) approach is manipulated for the development of optimal capacity scalability plans. The effect of the reconfiguration costs on the capacity scalability planning horizon and overall cost is investigated. The results showed the relation between deciding on the optimal capacity scalability planning horizon and the different reconfiguration costs. Results also highlighted the fact that decreasing costs of reconfiguration will lead to cost effective implementation of reconfigurable manufacturing systems.     

A modeling approach for evaluating capacity flexibilities in uncertain markets

The flexibility of production capacities is a means for coping with the challenges in today’s market environment, especially when dealing with strong fluctuations in customers’ demands. The reliable planning and evaluation of these capacities and their inherent flexibilities are considered an important task for many companies. This paper presents a capacity/cost model that considers the impact of market uncertainties and the corresponding capacity flexibilities. It proposes a demand forecasting method, a modeling approach for capacity-related flexibilities and the analysis of the economical correlation between available and required capacities. Based on this, capacity planning can be optimized using this model. The different steps of applying this modeling approach are illustrated with the aid of an example.     

Medium term planning of biopharmaceutical manufacture with uncertain fermentation titers

The growing trend of employing multiproduct manufacturing facilities along with the randomness inherent in the biopharmaceutical manufacturing environment is creating significant scheduling and planning challenges for the biopharmaceutical industry. This work focuses on capturing the effect of uncertainty in fermentation titers when optimizing the planning of biopharmaceutical manufacturing campaigns. A mixed integer linear programming (MILP) model based on previous work is derived via chance constrained programming (CCP). The methodology is applied to two illustrative examples, and the results are compared with those from the deterministic model and a multiscenario model accompanied by an iterative construction algorithm. The computational results indicate that the proposed methodology offers significant improvements in solution quality over the compared approaches and presents an opportunity for biopharmaceutical manufacturers to make better medium term planning decisions, particularly under uncertain manufacturing conditions.     

生态系统服务需求、供给和消费研究进展

生态系统服务的持续供给是社会和自然可持续发展的基础,人类通过对生态系统服务的消费来满足需求和提高自身福祉。研究人类对生态系统服务的需求、消费,分析生态系统服务的供给与需求关系,对生态系统的管理、土地发展规划和资源的合理有效配置具有重要意义。首先分析了生态系统服务需求、供给和消费的内涵与特征,辨析了生态系统服务的有效需求和潜在需求、有效供给与潜在供给,进而提出了生态系统服务需求与供给研究框架,指出应研究生态系统服务供给和需求的空间关联以及生态系统服务需求弹性差异,进而因地制宜地调控生态系统服务供给使得生态系统服务效用最大化。梳理并比较了对生态系统服务供给和需求的量化指标和研究方法,包括基于土地利用和土地覆被的生态系统服务供需关系矩阵法、生态足迹法、公众参与法、模型计算法等。提出了未来的相关研究主要应从不同群体的需求弹性差异、跨学科与多源数据结合的需求定量评估方法、供需关系的多尺度分析、生态系统服务需求与人类福祉的耦合机制拓展等方面展开,为生态系统服务调控管理和社会的可持续发展提供切实可行的科学依据。     

Mapping ecosystem services demand: A review of current research and future perspectives

Mapping the demand for ecosystem services (ES) has received increased attention in scientific research and is seen as a relevant tool to inform conservation planning, land use planning and management. Yet, there is a varying understanding of the concept of ES demand, which has implications on how and where ES demand is being mapped. In this paper we review the current conceptual understanding of ES demand, indicators to measure demand and the approaches used to quantify and map demand. We identified four distinct “demand types”, which relate to different ecosystem service categories. These demand types include demand expressed in terms of (1) risk reduction, (2) preferences and values, (3) direct use or (4) consumption of goods and services. Each of the demand types was linked to specific methods applied in the reviewed literature. We found that operationalization of ES demand in policy, planning and management requires a more consistent understanding and definition of ES demand, its drivers and its temporal dynamics. Furthermore, the impact of demands for multiple ecosystem services on land use change needs to be investigated. This will allow for the consideration of temporal and cross-level interactions between supply and demand of ecosystem services and its impacts in land use change modelling.     

Software architecture definition for on-demand cloud provisioning

Cloud computing is a promising paradigm for the provisioning of IT services. Cloud computing infrastructures, such as those offered by the RESERVOIR project, aim to facilitate the deployment, management and execution of services across multiple physical locations in a seamless manner. In order for service providers to meet their quality of service objectives, it is important to examine how software architectures can be described to take full advantage of the capabilities introduced by such platforms. When dealing with software systems involving numerous loosely coupled components, architectural constraints need to be made explicit to ensure continuous operation when allocating and migrating services from one host in the Cloud to another. In addition, the need for optimising resources and minimising over-provisioning requires service providers to control the dynamic adjustment of capacity throughout the entire service lifecycle. We discuss the implications for software architecture definitions of distributed applications that are to be deployed on Clouds. In particular, we identify novel primitives to support service elasticity, co-location and other requirements, propose language abstractions for these primitives and define their behavioural semantics precisely by establishing constraints on the relationship between architecture definitions and Cloud management infrastructures using a model denotational approach in order to derive appropriate service management cycles. Using these primitives and semantic definition as a basis, we define a service management framework implementation that supports on demand cloud provisioning and present a novel monitoring framework that meets the demands of Cloud based applications.     

Current capacity,bottlenecks, and future projections for offsetting habitat loss using Mitigation and Conservation banking in the United States

Habitat banking in its many iterations is an established and popular mechanism to deliver environmental offsets. The United States can look back at over 30 years of banking experience with the underlying framework and policies being consistently updated and improved. Given the increased demand in habitat banking, we provide insights into how bank area capacity is distributed across the United States for four different bank targets (wetlands, streams, multiple ecosystems, species) based on information extracted from the Regulatory In-lieu Fee and Bank Information Tracking System, as well as, estimating future capacities and area reserves through a predictive modeling approach based on data from the past 26 years. Future predictions indicate a decrease in available reserves for banks targeting wetlands or multiple ecosystems, with potential bottlenecks relating to large reserves being limited to the southeast and release schedules not catching up to the current and anticipated demand. Banks targeting species or streams are predicted to meet future demand, with species banks (conservation banks) following a different legislative and operational approach based on the listing of endangered species and pro-active approaches with anticipated future demand. Most current reserves for all four bank types are restricted to very few service areas with around one-third of all bank areas still awaiting release, limiting their availability on a broader scale. Strategic planning networks are necessary to meet future demand on a national scale and to identify areas suitable for banking or likely to experience future environmental or developmental stress.     

Assessing capacity scalability policies in RMS using system dynamics

This paper presents a model for assessing different capacity scalability policies in Reconfigurable Manufacturing System (RMS) for different changing demand scenarios. The novelty of this approach is two fold: (1) it is the first attempt to explore different capacity scalability policies in RMS based on multiple performance measures, mainly scaling rate, Work In Process level, inventory level and backlog level; and (2) the dynamic scalability process in RMS is modeled for the first time using System Dynamics. Different policies for capacity scalability for various demand scenarios were assessed. Numerical simulation results obtained using the developed capacity scalability model showed that the best capacity scalability policy to be adopted for RMS is dependent on the anticipated demand pattern as well as the various manufacturing objectives. The presented assessment results will help the capacity scalability planners better decide the different tradeoffs between the competing strategic and operational objectives of the manufacturing enterprise, before setting the suitable capacity scalability plan parameters.     

Effect of Community Engagement Interventions on Patient Safety and Risk Reduction Efforts in Primary Health Facilities: Evidence from Ghana

Background

Patient safety and quality care remain major challenges to Ghana’s healthcare system. Like many health systems in Africa, this is largely because demand for healthcare is outstripping available human and material resource capacity of healthcare facilities and new investment is insufficient. In the light of these demand and supply constraints, systematic community engagement (SCE) in healthcare quality assessment can be a feasible and cost effective option to augment existing quality improvement interventions. SCE entails structured use of existing community groups to assess healthcare quality in health facilities. Identified quality gaps are discussed with healthcare providers, improvements identified and rewards provided if the quality gaps are closed.

Purpose

This paper evaluates whether or not SCE, through the assessment of health service quality, improves patient safety and risk reduction efforts by staff in healthcare facilities.

Methods

A randomized control trail was conducted in 64 primary healthcare facilities in the Greater Accra and Western regions of Ghana. Patient risk assessments were conducted in 32 randomly assigned intervention and control facilities. Multivariate multiple regression test was used to determine effect of the SCE interventions on staff efforts towards reducing patient risk. Spearman correlation test was used to ascertain associations between types of community groups engaged and risk assessment scores of healthcare facilities.

Findings

Clinic staff efforts towards increasing patient safety and reducing risk improved significantly in intervention facilities especially in the areas of leadership/accountability (Coef. = 10.4, p<0.05) and staff competencies (Coef. = 7.1, p<0.05). Improvement in service utilization and health resources could not be attributed to the interventions because these were outside the control of the study and might have been influenced by institutional or national level developments between the baseline and follow-up period. Community groups that were gender balanced, religious/faith-based, and had structured leadership appeared to be better options for effective SCE in healthcare quality assessment.

Conclusion

Community engagement in healthcare quality assessment is a feasible client-centered quality improvement option that should be discussed for possible scale-up in Ghana and other resource poor countries in Africa.     

基于景感生态学理念的乡村社会-生态系统供给服务研究

乡村社会-生态系统的稳定和可持续发展为农业农村现代化的实现奠定着基础,在稳定社会经济发展、提高城乡居民福祉方面发挥着重要作用。服务性公共设施作为乡村社会-生态系统供给服务的重要组成部分,代表了乡村的发展水平与该地区居民生活质量水平。研究公共服务设施的空间分布特征和综合评价其供给服务可改善乡村社会-生态系统结构,为城乡居民提供高质量的社会生态系统服务。以厦门市岛外城市化乡村为研究对象,通过厦门市岛外POI数据和乡村社会经济统计数据,以公共服务设施为景感载体,分析其空间分布和测度其供给服务水平,采用核密度分析方法与熵值分析法,分析厦门市乡村社会-生态系统供给服务空间分布特征,结合人口、经济、距岛内核心区距离等指标对各村庄社会-生态系统供给服务能力进行皮尔逊相关性分析。研究发现厦门乡村公共设施供给服务能力表现为集美区 > 海沧区 > 同安区 > 翔安区;各村庄公共设施供给服务能力水平影响因素是多元且具有多样性的;提出从城乡居民的认知心理与物质需求出发对公共设施进行景感生态营造以提高村庄社会-生态系统供给服务能力,更好满足人们的需求。本文对公共服务设施的空间优化提出对策建议,研究结果可为乡村社会-生态系统供给服务提升和乡村可持续发展提供理论研究依据。     

What to expect when you are consolidating: effective prediction models of application performance on multicores

Consolidation of multiple applications with diverse and changing resource requirements is common in multicore systems as hardware resources are abundant. As opportunities for better system usage become ample, so are opportunities to degrade individual application performances due to unregulated performance interference between applications and system resources. Can we predict a performance region within which application performance is expected to lie under different consolidations? Alternatively, can we maximize resource utilization while maintaining individual application performance targets? In this work we provide a methodology that offers answers to the above difficult questions by constructing a queueing-theory based tool that can be used to accurately predict application scalability on multicores. The tool can also provide the optimal consolidation suggestions to maximize system resource utilization while meeting application performance targets. The proposed methodology is based on asymptotic analysis that can quickly provide a range of performance values that the user should expect under various consolidation scenarios. In addition, when more accurate performance forecasting is needed, the methodology can provide more accurate predictions using approximate mean value analysis. The methodology is light-weight as it relies on capturing application resource demands using standard system monitoring, via non-intrusive low-level measurements. We evaluate our approach on an IBM Power7 system using the DaCapo and SPECjvm2008 benchmark suites. From 900 different consolidations of application instances, our tool accurately predicts the average iteration time of collocated applications with an average error below 9 per cent. Experimental and analytical results are in excellent agreement, confirming the robustness of the proposed methodology in suggesting the best consolidations that meet given performance objectives of individual applications while maximizing system resource utilization.     

Decisional tool to assess current and future process robustness in an antibody purification facility

Increases in cell culture titers in existing facilities have prompted efforts to identify strategies that alleviate purification bottlenecks while controlling costs. This article describes the application of a database‐driven dynamic simulation tool to identify optimal purification sizing strategies and visualize their robustness to future titer increases. The tool harnessed the benefits of MySQL to capture the process, business, and risk features of multiple purification options and better manage the large datasets required for uncertainty analysis and optimization. The database was linked to a discrete‐event simulation engine so as to model the dynamic features of biopharmaceutical manufacture and impact of resource constraints. For a given titer, the tool performed brute force optimization so as to identify optimal purification sizing strategies that minimized the batch material cost while maintaining the schedule. The tool was applied to industrial case studies based on a platform monoclonal antibody purification process in a multisuite clinical scale manufacturing facility. The case studies assessed the robustness of optimal strategies to batch‐to‐batch titer variability and extended this to assess the long‐term fit of the platform process as titers increase from 1 to 10 g/L, given a range of equipment sizes available to enable scale intensification efforts. Novel visualization plots consisting of multiple Pareto frontiers with tie‐lines connecting the position of optimal configurations over a given titer range were constructed. These enabled rapid identification of robust purification configurations given titer fluctuations and the facility limit that the purification suites could handle in terms of the maximum titer and hence harvest load. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 28: 1019–1028, 2012