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.     

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.     

Glycosylation: impact,control and improvement during therapeutic protein production

Abstract

The emergence of the biopharmaceutical industry represented a major revolution for modern medicine, through the development of recombinant therapeutic proteins that brought new hope for many patients with previously untreatable diseases. There is a ever-growing demand for these therapeutics that forces a constant technological evolution to increase product yields while simultaneously reducing costs. However, the process changes made for this purpose may also affect the quality of the product, a factor that was initially overlooked but which is now a major focus of concern. Of the many properties determining product quality, glycosylation is regarded as one of the most important, influencing, for example, the biological activity, serum half-life and immunogenicity of the protein. Consequently, monitoring and control of glycosylation is now critical in biopharmaceutical manufacturing and a requirement of regulatory agencies. A rapid evolution is being observed in this context, concerning the influence of glycosylation in the efficacy of different therapeutic proteins, the impact on glycosylation of a diversity of parameters/processes involved in therapeutic protein production, the analytical methodologies employed for glycosylation monitoring and control, as well as strategies that are being explored to use this property to improve therapeutic protein efficacy (glycoengineering). This work reviews the main findings on these subjects, providing an up-to-date source of information to support further studies.     

Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems

This paper presents a hierarchical approach to scheduling flexible manufacturing systems (FMSs) that pursues multiple performance objectives and considers the process flexibility of incorporating alternative process plans and resources for the required operations. The scheduling problem is solved at two levels: the shop level and the manufacturing system level. The shop level controller employs a combined priority index developed in this research to rank shop production orders in meeting multiple scheduling objectives. To overcome dimensional complexity and keep a low level of work-in-process inventory, the shop controller first selects up to three production orders with the highest ranking as candidates and generates all possible release sequences for them, with or without multitasking. These sequences are conveyed to the manufacturing system controller, who then performs detailed scheduling of the machines in the FMS using a fixed priority heuristic for routing parts of multiple types while considering alternative process plans and resources for the operations. The FMS controller provides feedback to the shop controller with a set of suggested detailed schedules and projected order completion times. On receiving these results, the shop controller further evaluates each candidate schedule using a multiple-objective function and selects the best schedule for execution. This allows multiple performance objectives of an FMS to be achieved by the integrated hierarchical scheduling approach.     

Logical Cell Formation in FMS, Using Flexibility-Based Criteria

Flexible manufacturing systems often are organized into a cellular architecture for ease of operation. The formation of these cells sometimes has been treated as an extension of the conventional cell-formation problem. This paper argues that, owing to the existence of flexible routing and transfer capabilities, the cell-formation problem in FMSs should be treated as quite distinct from that in conventional manufacturing systems and shows that a flexibility-based procedure is apt for overcoming the deficiencies of earlier forays into this area. Manufacturing cell flexibility is defined as a composite of three flexibility measures: producibility,processivity , and transferability. The problem of cell formation is modeled as flexibility maximization, and a procedure is developed for the simultaneous formation of machine cells and part families, while heuristically maximizing within-cell flexibility.     

Fully integrated downstream process to enable next-generation manufacturing

Next-generation manufacturing (NGM) has evolved over the past decade to a point where large biopharmaceutical organizations are making large investments in the technology and considering implementation in clinical and commercial processes. There are many well-considered reasons to implement NGM. For the most part, organizations will not fund NGM unless the implementation benefits the funding organization by providing reduced costs, reduced time, or additional needed capabilities. Productivity improvements gained from continuous purification are shown in this work, which used a new system that fully integrates and automates several downstream unit operations of a biopharmaceutical process to provide flexibility and easy implementation of NGM. The equipment and automation needed to support NGM can be complicated and expensive. Biopharmaceutical Process Development considered two options as follows: (1) design its own NGM system or (2) buy a prebuilt system. PAK BioSolutions offers a turn-key automated and integrated system that can operate up to four continuous purification stages simultaneously, while maintaining a small footprint in the manufacturing plant. The system provides significant cost benefits (~10× lower) compared with the alternative—integration of many different pieces of equipment through a Distributed Control System that would require significant engineering time for design, automation, and integration. Integrated and Continuous Biomanufacturing can lead to significant reductions in facility size, reduced manufacturing costs, and enhanced product quality when compared with the traditional batch mode of operation. The system uses new automation strategies that robustly link unit operations. We present the optimized process fit, sterility and bioburden control strategy, and automation features (such as pH feedback control and in-line detergent addition), which enabled continuous operation of a 14-day end-to-end monoclonal antibody purification process at the clinical manufacturing scale.     

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.     

Operational flexibility quantification in a make-to-order assembly system

Manufacturing flexibility is becoming a fundamental production objective, along with cost, quality, and delivery time. Current production systems face quick changes in market conditions and they need to adapt in this environment. The supply chain and industrial globalization give an important role for assembly systems. Placed at the end of the value chain, assembly systems must face those quick changes successfully to reach the expected performance. The key performance indicators are normally based on cost, quality, and delivery time objectives. Reducing costs and improving quality are almost universal goals. Delivery time is typically determined by customer demand in the supply chain, planning from make-to-stock to make-to-order, and aspiring to reach a just-in-time manufacturing system. In this context, flexibility could be the differential advantage to tackle uncertainty. Closely related to the rest of production objectives and the overall performance of the system, flexibility must be integrated in the system for successful decision-making in operations. This work presents this approach of flexibility. A brief review of flexibility concepts and measurements in the literature precedes an introduction to flexibility, defined based on the function of utility. This function represents the expectations of system performance. This approach allows the formulation of the taxonomy of operational flexibility in agreement with the classical types identified in former works. Next, an integer model is programmed to simulate the basic behavior of task planning in a make-to-order assembly system. This first application illustrates flexibility quantification based on utility evolution. The use of common industrial parameters to quantify operational flexibility will finally facilitate an integrated interpretation of system performance trends.     

Definition and Classification of Manufacturing Flexibility Types and Measures

Flexibility is one of the most sought-after properties in modern manufacturing systems. Despite this interest, flexibility remains poorly understood in theory and poorly utilized in practice. One reason for this is the lack of general agreement on how to define flexibility: over 70 terms (types and measures) can be found in the literature. This paper concerns developing a framework and classification scheme for use in defining and classifying the various terms regarding flexibility found in manufacturing. The framework consists of six attributes: level of manufacturing requirements specification, manufacturing system specification, manufacturing environment specification, flexibility dimension, flexibility measurement approach, and time frame. A six-field hybrid classification scheme is developed based on this framework. The framework serves as a guide for developing new flexibility terms, whereas the classification scheme provides a mechanism for summarizing the important aspects of and assumptions behind a given term. The approach is demonstrated by using the classification scheme to classify over 50 existing flexibility terms. The results indicate that the classification scheme is an effective tool to aid in understanding different flexibility terms and how they compare to one another. At the same time, the difficulty of the classification exercise indicates the need for a suitable framework when defining such terms.     

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.     

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.     

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.     

Next generation non-vacuum, maskless, low temperature nanoparticle ink laser digital direct metal patterning for a large area flexible electronics

Flexible electronics opened a new class of future electronics. The foldable, light and durable nature of flexible electronics allows vast flexibility in applications such as display, energy devices and mobile electronics. Even though conventional electronics fabrication methods are well developed for rigid substrates, direct application or slight modification of conventional processes for flexible electronics fabrication cannot work. The future flexible electronics fabrication requires totally new low-temperature process development optimized for flexible substrate and it should be based on new material too. Here we present a simple approach to developing a flexible electronics fabrication without using conventional vacuum deposition and photolithography. We found that direct metal patterning based on laser-induced local melting of metal nanoparticle ink is a promising low-temperature alternative to vacuum deposition- and photolithography-based conventional metal patterning processes. The "digital" nature of the proposed direct metal patterning process removes the need for expensive photomask and allows easy design modification and short turnaround time. This new process can be extremely useful for current small-volume, large-variety manufacturing paradigms. Besides, simple, scalable, fast and low-temperature processes can lead to cost-effective fabrication methods on a large-area polymer substrate. The developed process was successfully applied to demonstrate high-quality Ag patterning (2.1 μΩ·cm) and high-performance flexible organic field effect transistor arrays.     

Effectiveness of flexible routing control

Flexibility in part process representation and in highly adaptive routing algorithms are two major sources for improvement in the control of flexible manufacturing systems (FMSs). This article reports the investigation of the impact of these two kinds of flexibilities on the performance of the system. We argue that, when feasible, the choices of operations and sequencing of the part process plans should be deferred until detailed knowledge about the real-time factory state is available. To test our ideas, a flexible routing control simulation system (FRCS) was constructed and a programming language for modeling FMS part process plans, control strategies, and environments of the FMS was designed and implemented. In addition, a scheme for implementing flexible process routing called data flow dispatching rule (DFDR) was derived. The simulation results indicate that flexible processing can reduce mean flow time while increasing system throughput and machine utilization. We observed that this form of flexibility makes automatic load balancing of the machines possible. On the other hand, it also makes the control and scheduling process more complicated and calls for new control algorithms.     

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.     

Industrialization of mAb production technology: The bioprocessing industry at a crossroads

Manufacturing processes for therapeutic monoclonal antibodies (mAbs) have evolved tremendously since the first licensed mAb product (OKT3) in 1986. The rapid growth in product demand for mAbs triggered parallel efforts to increase production capacity through construction of large bulk manufacturing plants as well as improvements in cell culture processes to raise product titers. This combination has led to an excess of manufacturing capacity, and together with improvements in conventional purification technologies, promises nearly unlimited production capacity in the foreseeable future. The increase in titers has also led to a marked reduction in production costs, which could then become a relatively small fraction of sales price for future products which are sold at prices at or near current levels. The reduction of capacity and cost pressures for current state-of-the-art bulk production processes may shift the focus of process development efforts and have important implications for both plant design and product development strategies for both biopharmaceutical and contract manufacturing companies.     

Models for first-pass FMS investment analysis

Consider a firm that could choose either an inexpensive product-specific (dedicated) facility or a costly flexible facility, or a combination of the two, in order to satisfy demand for two product groups. Flexible technology offers benefits of scale and scope economies. However, it may have added operational costs due to the need to have excess capacity (to permit changeovers) and to maintain cycle stocks. As a consequence, the economic viability of flexible technology is significantly affected by the choice of the operating doctrine. This article presents first-pass decision models to help the firm choose the optimum sizes of facilities and the degree of flexibility for the flexible facility such that the contingent operational costs are simultaneously optimized. Two variations of the problem are considered. Whereas, the first applies to a situation in which the demand rates are constant and predictable, the second considers random demands. In each case, this study provides a formulation of the problem, structural results, and the sensitivity of the results to cost parameters. These results have modest data and computational requirements, making them suitable for first-cut attempts at narrowing available choices.     

A Dynamic Tool Requirement Planning Model for Flexible Manufacturing Systems

Despite their strategic potential, tool management issues in flexible manufacturing systems (FMSs) have received little attention in the literature. Nonavailability of tools in FMSs cuts at the very root of the strategic goals for which such systems are designed. Specifically, the capability of FMSs to economically produce customized products (flexibility of scope) in varying batch sizes (flexibility of volume) and delivering them on an accelerated schedule (market response time) is seriously hampered when required tools are not available at the time needed. On the other hand, excess inventory of tools in such systems represents a significant cost due to the expensive nature of FMS tool inventory. This article constructs a dynamic tool requirement planning (DTRP) model for an FMS tool planning operation that allows dynamic determination of the optimal tool replenishments at the beginning of each arbitrary, managerially convenient, discrete time period. The analysis presented in the article consists of two distinct phases: In the first phase, tool demand distributions are obtained using information from manufacturing production plans (such as master production schedule (MPS) and material requirement plans (MRP)) and general tool life distributions fitted on actual time-to-failure data. Significant computational reductions are obtained if the tool failure data follow a Weibull or Gamma distribution. In the second phase, results from classical dynamic inventory models are modified to obtain optimal tool replenishment policies that permit compliance with such FMS-specific constraints as limited tool storage capacity and part/tool service levels. An implementation plan is included.     

Performance management of manufacturing system networks

Performance management of communication networks is critical for speed, reliability, and flexibility of information exchange between different components, subsystems, and sectors (e.g., factory, engineering design, and administration) of production process organizations in the environment of computer integrated manufacturing (CIM). Essential to this distributed total manufacturing system is the integrated communications network over which the information leading to process interactions and plant management and control is exchanged. Such a network must be capable of handling heterogeneous traffic resulting from intermachine communications at the factory floor, CAD drawings, design specifications, and administrative information. The objective is to improve the efficiency in handling various types of messages, e.g., control signals, sensor data, and production orders, by on-line adjustment of the parameters of the network protocol. This paper presents a conceptual design, development, and implementation of a network performance management scheme for CIM applications including flexible manufacturing. The performance management algorithm is formulated using the concepts of: (1) Perturbation analysis of discrete event dynamic systems; (2) stochastic approximation; and (3) learning automata. The proposed concept for performance management can also serve as a general framework to assist design, operation, and management of flexible manufacturing systems. The performance management procedure has been tested via emulation on a network test bed that is based on the manufacturing automation protocol (MAP) which has been widely used for CIM networking. The conceptual design presented in this paper offers a step forward to bridging the gap between management standards and users' demands for efficient network operations since most standards such as ISO and IEEE address only the architecture, services, and interfaces for network management.     

Scheduling of FMSs with information delays: A simulation study

Practitioners and academicians throughout the world recognize the crucial role played by flexibility within manufacturing organizations, especially those engaged in small batch manufacture. However, although the concept of flexibility has begun to attract increased attention, its interaction with information integration and automation has not captured due attention. For example, it almost always has been assumed that a real-time control mechanism is available for exploiting routing flexibility on the shop floor. While this may be true for FMSs, it generally is not so for the vast majority of conventional manufacturing systems with varying levels of information integration and automation. The lack of a fully integrated and automated control mechanism within such semi-automated flexible manufacturing systems (SAFMSs) would eventually cause delays in the availability of shop status information. In this paper, we study the impact that defined modes of information delay have on the performance of a hypothetical SAFMS through detailed simulation experiments. Given that the level of routing flexibility is a controllable design parameter, our interest is in determining the impact that information delays have on decisions pertaining to the selection of appropriate levels of routing flexibility. To highlight the impact of information delays within the SAFMS, the Taguchi experimental design procedure is adopted as a performance evaluation and analysis vehicle, using makespan as a measure of performance. Simulation results indicate the presence of a system specific tolerance limit, operation below which minimizes performance loss.