A tool for modeling strategic decisions in cell culture manufacturing

The development of a prototype tool for modeling manufacturing in a biopharmaceutical plant is discussed. A hierarchical approach to modeling a manufacturing process has been adopted to confer maximum user flexibility. The use of this framework for assessing the impact of manufacturing decisions on strategic technical and business indicators is demonstrated via a case study. In the case study, which takes the example of a mammalian cell culture process delivering a therapeutic for clinical trials, the dynamic modeling tool indicates how manufacturing options affect the demands on resources and the associated manufacturing costs. The example illustrates how the decision-support software can be used by biopharmaceutical companies to investigate the effects of working toward different strategic goals on the cost-effectiveness of the process, prior to committing to a particular option.     

Aligning demand and supply flexibility in custom product co-design

Flexibility of supply and demand is essential for successful implementation of a mass customization strategy that delivers sustained competitive advantage. Supply flexibility, i.e., a choice of alternative products designed to perform the same basic function, is made possible by the range of capabilities available in flexible and agile manufacturing systems and in supply chains. Demand flexibility is derived from the degree to which a customer is willing to compromise on product features or performance levels in order to meet budgetary (reflected in price) or schedule (reflected in delivery) constraints. Flexibility of both supply and demand can have significant strategic and financial value if they are properly aligned. However, customers are mostly unaware of mapping of demand flexibility on to supply flexibility and its impact on production cost and time. Recent advances in information technology have made it possible to co-design a product that involves customer on one end and the manufacturer on the other. This creates an aura and an opportunity where a middle ground between the supply and demand flexibility can be explored and a “deal” can be struck where both parties settle for a product that is beneficial to both through a negotiated settlement. In this paper, we develop a framework for such negotiations. The customer requirements are treated as a range of negotiable options instead of a set of fixed inputs. Demand and supply for customization is then matched by aligning the flexibility of manufacturing systems with customers’ requirement options. Based on this framework, a negotiation scheme is developed to assist customers and manufacturers in exploring and utilizing demand and supply flexibility information in co-design. The negotiation scheme is formulated using goal programming. Finally, an interactive problem-solving procedure is developed and implemented with an illustrative example.     

Business Process Reengineering and Flexibility: A Case for Unification

Business process reengineering (BPR) offers a radical approach to improving the performance of an organization. However, although there have been successes BPR is recognized as a high-risk activity, prone to failure. There are a variety of reasons for this and this paper highlights one which is argued to be the lack of attention that BPR pays to flexibility and its inability to cope with a changing environment. The purpose of this article is to raise the issue of flexibility within BPR and an approach is taken that examines flexibility in other business functional areas, such as manufacturing, architecture, information systems, and organizational strategy, where there is an extensive literature that indicates the importance of flexibility. The lessons from these other areas are identified and some of the implications for BPR are highlighted. A number of proposals are made including the suggestion that a form of “flexibility analysis” be adopted as a stage in BPR projects. It is argued that this would help to move the focus of a BPR project away from the current requirements toward a longer term, more flexible, enduring set of requirements. Flexibility analysis also ensures analysis of the kinds of changes that might be required over time, and how such change could be accommodated in the reengineered processes.     

Manufacturing capacity planning and the value of multi-stage stochastic programming under Markovian demand

Capacity planning is a crucial part of global manufacturing strategies in the automotive industry, especially in the presence of volatile markets with high demand uncertainty. Capacity adjustments in machining intensive areas, e.g. body shop, paint shop, or aggregate machining face lead times exceeding a year, making an elaborated decision support indispensable. In this regard, two-stage stochastic programming is a frequently used framework to support capacity and flexibility decisions under uncertainty. However, it does not anticipate future capacity adjustment opportunities in response to market demand developments. Motivated by empirical findings from the automotive industry, we develop a multi-stage stochastic dynamic programming approach where the evolution of demand is represented by a Markov demand model. An efficient multi-stage solution algorithm is proposed and the benefits compared to a rolling horizon application of a two-stage approach are illustrated for different generic manufacturing networks. Especially network structures with limited flexibility might significantly benefit from applying a multi-stage framework.     

On smoothing trends in population index modeling

In this article, we consider the U.K. Common Birds Census counts and their use in monitoring bird abundance. We use a state-space modeling approach within a Bayesian framework to describe population level trends over time and contribute to the alert system used by the British Trust for Ornithology. We account for potential overdispersion and excess zero counts by modeling the observation process with a zero-inflated negative binomial, while the system process is described by second-order polynomial growth models. In order to provide a biological motivation for the amount of smoothing applied to the observed series the system variance is related to the demographic characteristics of the species, so as to help the specification of its prior distribution. In particular, the available information on productivity and survival is used to formulate prior expectations on annual percentage changes in the population level and then used to constrain the variance of the system process. We discuss an example of how to interpret alternative choices for the degree of smoothing and how these relate to the classification of species, over time, into conservation lists.     

How do high-level specifications of the brain relate to variational approaches?

High-level specification of how the brain represents and categorizes the causes of its sensory input allows to link "what is to be done" (perceptual task) with "how to do it" (neural network calculation). In this article, we describe how the variational framework, which encountered a large success in modeling computer vision tasks, has some interesting relationships, at a mesoscopic scale, with computational neuroscience. We focus on cortical map computations such that "what is to be done" can be represented as a variational approach, i.e., an optimization problem defined over a continuous functional space. In particular, generalizing some existing results, we show how a general variational approach can be solved by an analog neural network with a given architecture and conversely. Numerical experiments are provided as an illustration of this general framework, which is a promising framework for modeling macro-behaviors in computational neuroscience.     

Finding New Order in Biological Functions from the Network Structure of Gene Annotations

The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to one another. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternative approach for grouping functional terms that captures intrinsic functional relationships that are not evident in the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our approach connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different sources. We show that grouping terms by this alternate scheme provides a new framework with which to describe and predict the functions of experimentally identified sets of genes.     

Concept of Intelligent Mechanical Design for Autonomous Mobile Robots

The concept of Intelligent Mechanical Design (IMD) is presented to show how a mechanical structure can be designed to affect robot controllability, simplification and task performance. Exploring this concept produces landmarks in the territory of mechanical robot design in the form of seven design principles. The design principles, which we call the Mecha-Telligence Principles (MTP), provide guidance on how to design mechanics for autonomous mobile robots. These principles guide us to ask the right questions when investigating issues concerning self-controllable, reliable, feasible, and compatible mechanics for autonomous mobile robots. To show how MTP can be applied in the design process we propose a novel methodology, named as Mecha-Telligence Methodology (MTM). Mechanical design by the proposed methodology is based on preference classification of the robot specification described by interaction of the robot with its environment and the physical parameters of the robot mechatronics. After defining new terms, we investigate the feasibility of the proposed methodology to the mechanical design of an autonomous mobile sewer inspection robot. In this industrial project we show how a passive-active intelligent moving mechanism can be designed using the MTM and employed in the field.     

Flexibility in manufacturing and services: achievements, insights and challenges

The concept of flexibility has attracted considerable interest in the last 25 years in the context of manufacturing. This paper develops a framework for thinking about flexibility in the context of making decisions about the design and operation of systems in either manufacturing or service environments. Three different aspects of flexibility are defined: prior flexibility, state flexibility and action flexibility, and the issues in the measurement of flexibility are discussed. The use of flexibility ideas by industry in manufacturing and services is reviewed and key contributions to the academic literature are summarized. Major issues and insights arising from a focus on flexibility are discussed. The paper concludes with some challenges for future research.     

Macroglossia: a review of the condition and a new classification

In studying macroglossia, no one has described normal tongue size or applied direct measurement to the pathologically enlarged tongue. Since macroglossia is primarily a condition that requires treatment on a symptomatic basis, definition of the problem in clinical terms is the most useful form for the practitioner. Based on published reports and our clinical experience, a classification scheme for macroglossia has been formulated. The concept of true macroglossia and relative macroglossia form a framework that categorizes acquired and congenital forms of tongue enlargement. True macroglossia exists when histologic abnormalities correlate with the clinical findings of tongue enlargement. Vascular malformations, muscular enlargement, and tumors are the most common forms of true macroglossia. Relative macroglossia includes those cases of apparent tongue enlargement in which the histology does not provide a pathologic explanation. Down's syndrome is a commonly quoted cause for tongue enlargement and is a form of relative macroglossia. The development of this classification system establishes an approach and common terminology on which therapy, research, and communication can be based.     

Importance of nonlinear and multivariable flexibility coefficients in the prediction of human cervical spine motion

The flexibility matrix currently forms the basis for multibody dynamics models of cervical spine motion. While studies have aimed to determine cervical motion segment behavior, their accuracy and utility have been limited by both experimental and analytical assumptions. Flexibility terms have been primarily represented as constants despite the spine's nonlinear stiffening response. Also, nondiagonal terms, describing coupled motions, of the matrices are often omitted. Currently, no study validates the flexibility approach for predicting vertebral motions; nor have the effects of matrix approximations and simplifications been quantified. Therefore, the purpose of this study is to quantify flexibility relationships for cervical motion segments, examine the importance of nonlinear components of the flexibility matrix, and determine the extent to which multivariable relationships may alter motion prediction. To that end, using unembalmed human cervical spine motion segments, a full battery of flexibility tests were performed for a neutral orientation and also following an axial pretorque. Primary and coupled matrix components were described using linear and piecewise nonlinear incremental constants. A third matrix approach utilized multivariable incremental relationships. Measured motions were predicted using structural flexibility methods and evaluated using RMS error between predicted and measured responses. A full set of flexibility relationships describe primary and coupled motions for C3-C4 and C5-C6. A flexibility matrix using piecewise incremental responses offers improved predictions over one using linear methods (p<0.01). However, no significant improvement is obtained using nonlinear terms represented by a multivariable functional approach (p<0.2). Based on these findings, it is suggested that a multivariable approach for flexibility is more demanding experimentally and analytically while not offering improved motion prediction.     

A wavelet optimization approach for ECG signal classification

Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accuracy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimination capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experimental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accuracy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet wavelets).     

The Application and Evaluation of Banker's Algorithm for Deadlock-Free Buffer Space Allocation in Flexible Manufacturing Systems

Deadlock-free operation is essential for operating highly automated manufacturing systems. The seminal deadlock avoidance procedure, Banker's algorithm, was developed for computer operating systems, an environment where very little information regarding the future resource requirements of executing processes is known. Manufacturing researchers have tended to dismiss Banker's algorithm as too conservative in the manufacturing environment where future resource requirements are well defined by part routes. In this work, we investigate this issue by developing variants of Banker's algorithm applicable to buffer space allocation in flexible manufacturing. We show that these algorithms are not overly conservative and that, indeed, Banker's approach can provide very good operational flexibility when properly applied to the manufacturing environment.     

Linking Microbial and Ecosystem Ecology Using Ecological Stoichiometry: A Synthesis of Conceptual and Empirical Approaches

Currently, one of the biggest challenges in microbial and ecosystem ecology is to develop conceptual models that organize the growing body of information on environmental microbiology into a clear mechanistic framework with a direct link to ecosystem processes. Doing so will enable development of testable hypotheses to better direct future research and increase understanding of key constraints on biogeochemical networks. Although the understanding of phenotypic and genotypic diversity of microorganisms in the environment is rapidly accumulating, how controls on microbial physiology ultimately affect biogeochemical fluxes remains poorly understood. We propose that insight into constraints on biogeochemical cycles can be achieved by a more rigorous evaluation of microbial community biomass composition within the context of ecological stoichiometry. Multiple recent studies have pointed to microbial biomass stoichiometry as an important determinant of when microorganisms retain or recycle mineral nutrients. We identify the relevant cellular components that most likely drive changes in microbial biomass stoichiometry by defining a conceptual model rooted in ecological stoichiometry. More importantly, we show how X-ray microanalysis (XRMA), nanoscale secondary ion mass spectroscopy (NanoSIMS), Raman microspectroscopy, and in situ hybridization techniques (for example, FISH) can be applied in concert to allow for direct empirical evaluation of the proposed conceptual framework. This approach links an important piece of the ecological literature, ecological stoichiometry, with the molecular front of the microbial revolution, in an attempt to provide new insight into how microbial physiology could constrain ecosystem processes.     

A Comparison Between Machine Flexibility and Routing Flexibility

In this paper, we evaluate two types of flexibility, machine flexibility and routing flexibility, in terms of manufacturing performance in various shop environments. A simulation-based investigation was conducted to analyze the impact of these types of flexibility on the average flow time of parts under various job flow pattern conditions, which characterize the shop nature from a random job shop to a flow shop, operation time variance, setup time, and shop load. The experimental results show how these types of flexibility affect the average flow time of parts and which type is superior under what conditions. Management can obtain better insight and guidelines for determining priorities or the scale, or scope, of various decision items relating to design standardization, process and operations improvement, investment in new equipment and tools, and the like.     

A Classification for Hoist Scheduling Problems

Electroplating lines are totally automated manufacturing systems that are used to cover parts with a coat of metal. They consist of a set of tanks between which the parts to be treated are transported by one or several hoists. Scheduling the movements of these hoists is commonly called a hoist scheduling problem (HSP) in the literature. But the assumptions and constraints that must be taken into account greatly depend on the production environment (physical system, manufacturing specifications, and management policies). Consequently, there exist several classes of HSPs. The systematic frameworks usually used to classify deterministic scheduling problems do not allow distinguishing between these various kinds of HSPs. Therefore, identifying the scope of each published work and comparing the various proposed scheduling methods turn out to be difficult. Thus, this article presents notation for scheduling problems in electroplating systems, to make the specification of problem types and the identification of studied problem instances easier. An associated typology gives a survey of the literature and demonstrates the usefulness of the proposed classification scheme.     

Axis specification in animal development

Axis specification is the first step in defining specific regions of the developing embryo. Embryos exploit asymmetries, either pre-existing in the egg or triggered by external cues, to establish embryonic axes. The axial information is then used to generate regional differences within the embryo. In this review, we discuss experiments in animals which address three questions: whether the unfertilized egg is constructed with pre-determined axes, what cues are used to specify the embryonic axes, and how these cues are interpreted to generate the initial regional differences within the embryo. Based on mapping the data onto an animal phylogeny, we then propose a scenario for how this primary developmental decision occurred in ancestral metazoans.     

Defining functional biomes and monitoring their change globally

Biomes are important constructs for organizing understanding of how the worlds’ major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30‐year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture‐limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function.     

Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.