A network approach for modeling and design of agile supply chains using a flexibility construct

Agility can be viewed as a need to encourage the enterprise-wide integration of flexible and core competent resources so as to offer value-added product and services in a volatile competitive environment. Since flexibility is considered a property that provides change capabilities of different enterprise-wide resources and processes in time and cost dimensions, supply chain flexibility can be considered a composite state to enterprise-wide resources to meet agility needs. Enterprise modeling frameworks depicting these composite flexibility states are difficult to model because of the complex and tacit interrelationship among system parameters and also because agility thrives on many business objectives. In view of this, the modeling framework presented in this paper is based on analytical network process (ANP) since this methodology can accommodate the complex and tacit interrelationship among factors affecting enterprise agility. The modeling framework forms a three-level network with the goal of attaining agility from the perspective of market, product, and customer as the actors. The goal depends on substrategies that address the characteristics of the three actors. Each of these substrategies further depends on manufacturing, logistic, sourcing, and information technology (IT) flexibility elements of the enterprise supply chain (SC). The research highlights that, under different environmental conditions, enterprises require synergy among appropriate supply chain flexibilities for practising agility. In the present research, the ANP modeling software tool Super Decisions? has been used for relative prioritization of the supply chain flexibilities. We demonstrate through sensitivity analysis that dynamic conditions do require adjustments in the enterprise-wide flexibility spectrum.     

A spatially explicit whole-system model of the lignocellulosic bioethanol supply chain: an assessment of decentralised processing potential

Background  

Lignocellulosic bioethanol technologies exhibit significant capacity for performance improvement across the supply chain through the development of high-yielding energy crops, integrated pretreatment, hydrolysis and fermentation technologies and the application of dedicated ethanol pipelines. The impact of such developments on cost-optimal plant location, scale and process composition within multiple plant infrastructures is poorly understood. A combined production and logistics model has been developed to investigate cost-optimal system configurations for a range of technological, system scale, biomass supply and ethanol demand distribution scenarios specific to European agricultural land and population densities.     

A multiscale simulation framework for the manufacturing facility and supply chain of autologous cell therapies

Background aimsAutologous cell therapy (AuCT) is an emerging therapeutic treatment that is undergoing transformation from laboratory- to industry-scale manufacturing with recent regulatory approvals. Various challenges facing the complex AuCT manufacturing and supply chain process hinder the scale out and broader application of this highly potent treatment.MethodsWe present a multiscale logistics simulation framework, AuCT-Sim, that integrates novel supply chain system modeling algorithms, methods, and tools. AuCT-Sim includes a single facility model and a system-wide network model. Unique challenges of the AuCT industry are analyzed and addressed in AuCT-Sim. Decision-supporting tools can be developed based on this framework to explore “what-if” manufacturing and supply chain scenarios of importance to various cell therapy stakeholder groups.ResultsTwo case studies demonstrate the decision-supporting capability of AuCT-Sim where one investigates the optimal reagent base stocking level, and the other one simulates a reagent supply disruption event. These case studies serve as guidelines for designing computational experiments with AuCT-Sim to solve specific problems in AuCT manufacturing and supply chain.DiscussionThis simulation framework will be useful in understanding the impact of possible manufacturing and supply chain strategies, policies, regulations, and standards informing strategies to increase patient access to AuCT.     

Modeling and Implementation of Cattle/Beef Supply Chain Traceability Using a Distributed RFID-Based Framework in China

In recent years, traceability systems have been developed as effective tools for improving the transparency of supply chains, thereby guaranteeing the quality and safety of food products. In this study, we proposed a cattle/beef supply chain traceability model and a traceability system based on radio frequency identification (RFID) technology and the EPCglobal network. First of all, the transformations of traceability units were defined and analyzed throughout the cattle/beef chain. Secondly, we described the internal and external traceability information acquisition, transformation, and transmission processes throughout the beef supply chain in detail, and explained a methodology for modeling traceability information using the electronic product code information service (EPCIS) framework. Then, the traceability system was implemented based on Fosstrak and FreePastry software packages, and animal ear tag code and electronic product code (EPC) were employed to identify traceability units. Finally, a cattle/beef supply chain included breeding business, slaughter and processing business, distribution business and sales outlet was used as a case study to evaluate the beef supply chain traceability system. The results demonstrated that the major advantages of the traceability system are the effective sharing of information among business and the gapless traceability of the cattle/beef supply chain.     

Biomass feedstock preprocessing and long‐distance transportation logistics

Biomass‐based biofuels have gained attention because they are renewable energy sources that could facilitate energy independence and improve rural economic development. As biomass supply and biofuel demand areas are generally not geographically contiguous, the design of an efficient and effective biomass supply chain from biomass provision to biofuel distribution is critical to facilitate large‐scale biofuel development. This study compared the costs of supplying biomass using three alternative biomass preprocessing and densification technologies (pelletizing, briquetting, and grinding) and two alternative transportation modes (trucking and rail) for the design of a four‐stage biomass–biofuel supply chain in which biomass produced in Illinois is used to meet biofuel demands in either California or Illinois. The BioScope optimization model was applied to evaluate a four‐stage biomass–biofuel supply chain that includes biomass supply, centralized storage and preprocessing (CSP), biorefinery, and ethanol distribution. We examined the cost of 15 scenarios that included a combination of three biomass preprocessing technologies and five supply chain configurations. The findings suggested that the transportation costs for biomass would generally follow the pattern of coal transportation. Converting biomass to ethanol locally and shipping ethanol over long distances is most economical, similar to the existing grain‐based biofuel system. For the Illinois–California supply chain, moving ethanol is Biomass‐based biofuels have gained attention because they are renewable energy sources that could facilitate energy independence and improve rural economic development. As biomass supply and biofuel demand areas are generally not geographically contiguous, the design of an efficient and effective biomass supply chain from biomass provision to biofuel distribution is critical to facilitate large‐scale biofuel development. This study compared the costs of supplying biomass using three alternative biomass preprocessing and densification technologies (pelletizing, briquetting, and grinding) and two alternative transportation modes (trucking and rail) for the design of a four‐stage biomass–biofuel supply chain in which biomass produced in Illinois is used to meet biofuel demands in either California or Illinois. The BioScope optimization model was applied to evaluate a four‐stage biomass–biofuel supply chain that includes biomass supply, centralized storage and preprocessing (CSP), biorefinery, and ethanol distribution. We examined the cost of 15 scenarios that included a combination of three biomass preprocessing technologies and five supply chain configurations. The findings suggested that the transportation costs for biomass would generally follow the pattern of coal transportation. Converting biomass to ethanol locally and shipping ethanol over long distances is most economical, similar to the existing grain‐based biofuel system. For the Illinois–California supply chain, moving ethanol is $0.24 gal^?1 less costly than moving biomass even in densified form over long distances. The use of biomass pellets leads to lower overall costs of biofuel production for long‐distance transportation but to higher costs if used for short‐distance movement due to its high capital and processing costs. Supported by the supply chain optimization modeling, the cellulosic‐ethanol production and distribution costs of using Illinois feedstock to meet California demand are $0.08 gal^?1 higher than that for meeting local Illinois demand.     

Sustainable development of global supply chains—part 1: sustainability optimization framework

In global industry supply chains, environmental sustainability optimization addresses the overall consumption of resources and energy, the reduction of carbon emissions and generated waste to name a few. In this paper, we propose a holistic sustainability optimization framework for strategic network design of industry supply chains under consideration of economic, social as well as ecologic objectives. The framework is flexible to incorporate multiple sustainability indicators, alternative sustainability optimization strategies as well as a variety of internal and external industry-specific factors which impact the sustainability of the entire industry supply chain in the long-term. The core of the framework is an end-to-end closed-loop value chain model consisting of process, transport and product-in-use modules. For the first time, the product-in-use impact (“use” vs. “make”) is integrated in one network design approach. In addition, the model fully closes the loop from sourcing of raw materials via manufacturing towards reverse value chain steps such as disposal and recycling. Finally, we propose the minimize-time-to-sustainability approach as new optimization strategy for long-term network design problems focusing on minimizing the time, industry supply chain structures need to transform into sustainability steady states for all defined sustainability indicators such as CO₂e emissions, costs or social indicators based on defined target values. In part 2 of this paper the application of the optimization framework to the European automotive industry is shown.     

Mass customization: manufacturing issues and taxonomic analyses

As the field of mass customization (MC) attains the status of a mature discipline, two significant research deficits stand out. First, a through metareview of the entire body of MC research that looks at the application value and rigorousness of research is overdue. Second, manufacturing issues, especially those pertaining to quality and the supply chain have been largely ignored. This issue is dedicated to both of these important areas of research. The conclusion with regards to the status of the MC field is that it is currently vibrant, with growing research volume and applications. The manufacturing issues dealt with in this issue are strategically important, dealing with quality and customization issues. The work on quality is the first of its kind: it seeks to generate a defect-tracking matrix consistent with product configurations, enabling agile identification of defects in a mass customization environment. The use of discrete event simulation to deal with the dynamically evolving customized demand so as to minimize cost and schedule disruption is innovative, timely, and profound.     

JXP4BIGI: a generalized, Java XML-based approach for biological information gathering and integration

MOTIVATION: In the post-genomic era, biologists interested in systems biology often need to import data from public databases and construct their own system-specific or subject-oriented databases to support their complex analysis and knowledge discovery. To facilitate the analysis and data processing, customized and centralized databases are often created by extracting and integrating heterogeneous data retrieved from public databases. A generalized methodology for accessing, extracting, transforming and integrating the heterogeneous data is needed. RESULTS: This paper presents a new data integration approach named JXP4BIGI (Java XML Page for Biological Information Gathering and Integration). The approach provides a system-independent framework, which generalizes and streamlines the steps of accessing, extracting, transforming and integrating the data retrieved from heterogeneous data sources to build a customized data warehouse. It allows the data integrator of a biological database to define the desired bio-entities in XML templates (or Java XML pages), and use embedded extended SQL statements to extract structured, semi-structured and unstructured data from public databases. By running the templates in the JXP4BIGI framework and using a number of generalized wrappers, the required data from public databases can be efficiently extracted and integrated to construct the bio-entities in the XML format without having to hard-code the extraction logics for different data sources. The constructed XML bio-entities can then be imported into either a relational database system or a native XML database system to build a biological data warehouse. AVAILABILITY: JXP4BIGI has been integrated and tested in conjunction with the IKBAR system (http://www.ikbar.org/) in two integration efforts to collect and integrate data for about 200 human genes related to cell death from HUGO, Ensembl, and SWISS-PROT (Bairoch and Apweiler, 2000), and about 700 Drosophila genes from FlyBase (FlyBase Consortium, 2002). The integrated data has been used in comparative genomic analysis of x-ray induced cell death. Also, as explained later, JXP4BIGI is a middleware and framework to be integrated with biological database applications, and cannot run as a stand-alone software for end users. For demonstration purposes, a demonstration version is accessible at (http://www.ikbar.org/jxp4bigi/demo.html).     

Integrated Modeling Framework for Manufacturing Systems: A Unified Representation of the Physical Process and Information System

The task of process modeling in a manufacturing environment centers around controlling and improving the flow of materials. This flow comprises a complicated web of control and physical systems. Despite a variety of manufacturing system modeling approaches, more rigorous process modeling is required. This paper presents an integrated modeling framework for manufacturing systems (IMF-M). Conceptual modeling of physical materials flow supported by a graphical representation facilitates improvement of operations in manufacturing environments. A declarative and executable representation of control information systems helps to improve information management by managing a variety of information models with improved readability and reusability. A unified representation of the physical process and information system provides a common modeling milieu in which efforts can be coordinated among several groups working in the different domains of scheduling, shop floor and logistics control, and information system. Since the framework helps adapt to the changes of the physical process and information system affecting each other in a consistent manner, the modeling output enhances integration of the manufacturing system.     

基于在线供应链调查的产品生命周期评价

产品生命周期评价(LCA)中的供应链数据收集非常困难而且效率低下,因而在产品LCA研究中常常近似处理、甚至忽略供应链实际生产过程,严重影响了LCA的数据质量与可信度。开发专门的供应链数据收集工具是解决这一问题的有效途径。介绍了可进行在线供应链调查、建模与计算分析的LCA系统e Footprint,基于该系统提出了在线的LCA工作方法,通过长虹美菱冰箱的应用案例验证了系统与方法的可行性,为提高产品LCA的效率和质量提供了新的解决方案。     

Collaborative fractal-based supply chain management based on a trust model for the automotive industry

In the supply chain of the automotive industry, where the procurement ratio of parts from partners is very high, the trustworthiness of partners should be considered during supply chain optimization. In this paper, to deal with the trust issue related to collaboration and reduce the computational load in production planning, we develop a collaborative fractal-based supply chain management framework for the automotive industry. In our framework, the relationships between the participants of a supply chain are modeled as a fractal. Each fractal has a goal model and generates a production plan of its participants based on the goal model. The goal model of each fractal is developed from an operational perspective to consider the trust value of participants during production planning. A fuzzy trust evaluation model is used to evaluate the trust value in terms of numerical value. To validate the developed framework in the automotive industry, simulations are conducted. The results of the simulations indicate that our framework can be useful in generating precise production plans.     

Fuzzy Multi-Objective Optimization of a Green Supply Chain Network with Risk Management that Includes Environmental Hazards

Among the leading environmental risks, global climate alteration has become one of the most important controversial issues. Greenhouse gas emissions (CO₂, methane, etc.) and air pollution have motivated a need to develop and improve environmental management strategies. As a consequence, environmental sanctions are forcing commercial enterprises to re-consider and re-design supply chain processes in a green way. This article provides a multi-objective model to design a closed-loop supply chain (CLSC) network in a green framework. Our first and second objectives are to minimize all the transportation costs for the supply chain's forward and reverse logistics; the third objective is to minimize total CO₂ emissions; the fourth objective is to encourage customers to use recyclable materials as an environmental practice. To provide more realistic modeling by treating the uncertainty in decision-makers’ objectives, fuzzy modeling is used in this study. The model is explained and tested via fulfilling a numerical example. In scenario analyses, analytic hierarchy process (AHP), fuzzy AHP (F-AHP), and fuzzy TOPSIS (F-TOPSIS) approaches were applied and compared to evaluate different objectives to guide decision-makers.     

A Proposed Framework for the Management of Water Quality in Arid-Region Lakes

For arid-region lakes, management conflicts are likely to occur between quantity and quality of water supplied: increasing quantity of water supply can lead to water quality deterioration. Such conflicts can best be resolved within an effective management program based on awareness and cooperation at all levels of water management from policy makers to experts. We propose a general framework for designing effective water resource management programs for lakes based on concrete definitions of management criteria such as water quality. The proposed system requires close interaction between policy makers, water resource managers, water suppliers and users, hydrological engineers and limnologists. The significance of mathematical modeling as a self-organizing tool of the management program is emphasized, especially with regards to designing limnological investigations directed toward lake management. We illustrate the application of this approach to water resource management in arid-region lakes (Lake Kinneret, Israel and Lake Sevan, Armenia), where artificial variability of lake morphometry due to water use is a forcing function affecting water quality.     

Augmentation of AM fungi fails to ameliorate the adverse effects of temporal resource variation on a lettuce crop

While agricultural research has traditionally focused on average environmental conditions, environmental variability, independent of the mean, can also have biological consequences. Using lettuce (Lactuca sativa) as a model system, we tested two hypotheses: (1) increased temporal variability in water supply impacts plant growth, yield, photosynthesis, water relations and nutrition and (2) arbuscular mycorrhizal AM fungal associations benefit this agricultural crop, especially when plants experience temporal variability in water supply. The experiment used a randomized complete block design with two blocks and three variables (each with two levels): ± mycorrhizal inoculation, high or low variability in watering intervals, and high or low total watering volume. Temporal variability in water supply, at a time scale similar to what is common in agricultural practices, had negative effects on lettuce production. Inoculation treatments were successful in doubling the extent of AM fungal infection in lettuce roots. There were no main effects of mycorrhizal inoculation on any measured variable, but augmented mycorrhizal associations interacted with variability in water supply to increase root/shoot ratios and decrease tissue concentrations of N and P. Successful application of AM fungi to sustainable agriculture probably requires a general theoretical framework for predicting when effects on plants will be beneficial versus neutral or even detrimental.     

RosettaRemodel: a generalized framework for flexible backbone protein design

We describe RosettaRemodel, a generalized framework for flexible protein design that provides a versatile and convenient interface to the Rosetta modeling suite. RosettaRemodel employs a unified interface, called a blueprint, which allows detailed control over many aspects of flexible backbone protein design calculations. RosettaRemodel allows the construction and elaboration of customized protocols for a wide range of design problems ranging from loop insertion and deletion, disulfide engineering, domain assembly, loop remodeling, motif grafting, symmetrical units, to de novo structure modeling.     

Phenotypic evolutionary models in stem cell biology: replacement, quiescence, and variability

Phenotypic evolutionary models have been used with great success in many areas of biology, but thus far have not been applied to the study of stem cells except for investigations of cancer. We develop a framework that allows such modeling techniques to be applied to stem cells more generally. The fundamental modeling structure is the stochastic kinetics of stem cells in their niche and of transit amplifying and fully differentiated cells elsewhere in the organism, with positive and negative feedback. This formulation allows graded signals to be turned into all or nothing responses, and shows the importance of looking beyond the niche for understanding how stem cells behave. Using the deterministic version of this framework, we show how competition between different stem cell lines can be analyzed, and under what circumstances stem cells in a niche will be replaced by other stem cells with different phenotypic characteristics. Using the stochastic version of our framework and state dependent life history theory, we show that the optimal behavior of a focal stem cell will involve long periods of quiescence and that a population of identical stem cells will show great variability in the times at which activity occurs; we compare our results with classic ones on quiescence and variability in the hematopoietic system.     

Analyzing Economic and Environmental Performance of Switchgrass Biofuel Supply Chains

This study optimized the net present value (NPV) of profit of various switchgrass-based ethanol supply chains and estimated associated greenhouse gas (GHG) emissions in west Tennessee. Three configurations of feedstock harvesting and storage, including a large round baler system, a large square baler system, and a chopping/densification system, were evaluated. A mixed-integer mathematical programming model incorporating high-resolution spatial data was used to determine the optimal locations and capacities of cellulosic ethanol plants and feedstock preprocessing facilities, and associated feedstock-draw areas by maximizing the NPV of profit over 20 years. The optimized outputs were then used to estimate the GHG emissions produced in the biofuel supply chain (BSC) per year. The study shows that BSC configurations have important implications for the economic and environmental performance of the system. The harvest and storage configurations affect the locations of conversion and preprocessing facilities, and associated feedstock-draw areas, hence impacting the cost and emissions of both feedstock and biofuels transportation. The findings suggest the BSC system that harvests feedstock with forage choppers and utilizes stretch-wrap balers to increase feedstock density has the highest NPV of profit. The BSC system that uses large square balers for harvest and storage emits the lowest amount of GHGs per year. In addition, the sensitivity analysis suggests that biofuel price and scaling factor of facility capital was influential to the economics of BSC systems. The breakeven price of biofuel for the three BSCs was around $0.97 L^?1.     

Molecular modeling and its experimental verification for the catalytic mechanism of Candida antarctica lipase B

Quantum mechanical and molecular dynamics simulation analysis has been performed on the model system for CALB (Candida antarctica lipase B) with esters to study the reaction mechanism and conformational preference of catalytic hydrolysis and the esterification reaction. Using quantum mechanical analysis, the ping-pong bi-bi mechanism was applied and energies and 3-dimensional binding configurations of the whole reaction pathways were calculated. Further molecular dynamics simulation analysis was performed on the basis of the transition state obtained from quantum mechanical study to observe the effect of structures of,the substrates. Calculation results using substrates of different chain length and chiral configurations were compared for conformational preference. The calculated results showed very small influence on chain length, whereas chiral conformation showed big differences. Calculated results from molecular modeling studies have been compared qualitatively with the experimental data using racemic mixtures of (+/-)-cis-4-acetamido-cyclopent-2-ene-1-ethyl acetate as substrates.     

The role of mass balance equations in growth mechanics illustrated in surface and volume dissolutions

Growth mechanics problems require the solution of mass balance equations that include supply terms and account for mass exchanges among constituents of a mixture. Though growth may often be accompanied by a variety of concomitant phenomena that increase modeling complexity, such as solid matrix deformation, evolving traction-free configurations, cell division, and active cell contraction, it is important to distinguish these accompanying phenomena from the fundamental growth process that consists of deposition or removal of mass from the solid matrix. Therefore, the objective of this study is to present a canonical problem of growth, namely, dissolution of a rigid solid matrix in a solvent. This problem illustrates a case of negative growth (loss of mass) of the solid in a mixture framework that includes three species, a solid, a solvent, and a solute, where the solute is the product of the solid dissolution. By analyzing both volumetric and surface dissolutions, the two fundamental modes of growth are investigated within the unified framework of mixture theory.     

Nonconjugate Bayesian analysis of variance component models

We consider the usual normal linear mixed model for variance components from a Bayesian viewpoint. With conjugate priors and balanced data, Gibbs sampling is easy to implement; however, simulating from full conditionals can become difficult for the analysis of unbalanced data with possibly nonconjugate priors, thus leading one to consider alternative Markov chain Monte Carlo schemes. We propose and investigate a method for posterior simulation based on an independence chain. The method is customized to exploit the structure of the variance component model, and it works with arbitrary prior distributions. As a default reference prior, we use a version of Jeffreys' prior based on the integrated (restricted) likelihood. We demonstrate the ease of application and flexibility of this approach in familiar settings involving both balanced and unbalanced data.