Simulation-based Optimization Approach to Clinical Trial Supply Chain Management

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Simulation-based Optimization Approach to Clinical Trial Supply Chain Management"

Transcription

1 20 th European Symposium on Computer Aided Process Engineering ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) 2010 Elsevier B.V. All rights reserved. Simulation-based Optimization Approach to Clinical Trial Supply Chain Management Ye Chen*, Linas Mockus, Seza Orcun, Gintaras V. Reklaitis Purdue University, West Lafayette, IN 47907, USA, Abstract The development activities required to bring a new drug to market involve considerable expense ($1+ Billion) and can take in excess of ten years. Clinical trials constitute a critically important and very expensive part of this development process as it encompasses producing, distributing and administering the candidate therapy to volunteer patients located in different geographic zones. A number of different approaches are being pursued to reduce clinical trial costs, including innovations in trial organization and patient pool selection. In this work, we focus our attention on improved management of the supply chain which provides the dosage required by the clinical sites. A simulation-based optimization approach is presented, which includes patient demand forecasting, mathematical programming based planning, and discrete event simulation. The objective is to enhance the robustness of the supply chain under different sources of uncertainties. A case study is reported which demonstrates the application of the proposed approach. Keywords: Clinical Trial, Supply Chain, Optimization, MILP, Simulation 1. Introduction New drug development follows an extended sequence of steps (discovery, animal trials, FDA application, product and process development, three phases of clinical trials, FDA filing and approval, and launch). As a result it takes many years and considerable expense ($1+ Billion) to bring a new drug to market. The clinical trials themselves constitute a very expensive part of this process. Normally, clinical trials with different test objectives (e.g. safety, efficacy, side effects) are conducted at the same time to expedite the new drug development process, which further complicates the clinical trial supply chain. While clinical trials are in progress, the development team also continues work towards improving the manufacturing processes. The clinical trial material supply chain management problem is composed of the planning and scheduling of all transactions, operations and organizations during a trial, beginning with active ingredient manufacturing, followed by drug manufacturing and distribution to the clinical sites, and ending with dispensing the drugs to patients at each clinical site. A substantial amount of work has been reported on process industry supply chain optimization, but only a limited literature has addressed the issues faced in the pharmaceutical industry. Shah (2004) presented a review paper, categorizing previous work and analyzing the key issues for pharmaceutical supply chain optimization. There have been research activities on management of the product development pipeline, capacity planning, risk management, process development and plant design, as well as production planning and scheduling, but the issue of materials management for clinical trials has not been studied. Monkhouse et al (2006) discussed the design and development of clinical trials in some detail, but they provided little information about the actual management of the clinical trials material supply chain.

2 Ye Chen, Linas Mockus, Seza Orcun, Gintaras V. Reklaitis Traditionally, the pharmaceutical industry uses batch processes in the manufacture of pharmaceutical products both at the pilot and the commercial scale. Since these batch facilities are usually shared across various products, especially for the quantities needed for clinical trials, it is necessary to decide on the order and timing of the products to be produced. These decisions can have a large economic impact on the company at the clinical trials stage, because missing the delivery of trial dosage to patients can significantly delay completion of the trial and hence delay the time to market which in turn can mean significant loss of revenue. Deterministic mixed integer linear programs (MILP) and mixed integer nonlinear programming (MINLP) optimization methods have been proposed and used to solve resource constrained project planning and scheduling problem. Floudas and Lin (2004) presented a comprehensive review of these approaches. Most of the work reported is confined to a deterministic context. While some approaches have addressed uncertainties to generate robust schedules and plans, none of them are equipped to deal with the uncertainties faced in clinical trial supply chains. The key technical challenges in managing a clinical trial materials supply chain are to meet the needs from clinical sites, so that patients are fully supplied once they are enrolled while minimizing oversupply since unused materials cannot be re-routed to other sites due to regulatory restrictions. Not only is patient enrollment highly variable, but uncertainties also arise in manufacturing and shipment lead times, in process failures and in production yields. Furthermore, the life of a clinical trial materials supply chain, which is around 1-2 years, is significantly shorter than that of a commercial supply chain, which usually exceeds 10 years. Therefore, the strategies utilized to buffer the uncertainties in commercial supply chains become ineffective as expected values cannot be effectively used as targets. Subramanian, Pekny & Reklaitis (2001) propose a computational architecture called Sim-Opt, which combines mathematical programming and discrete event system simulation to assess the uncertainty and control the risk present in the new product development pipeline problem. Simulation-based optimization methods were found to be efficient and effective alternatives to solving a large stochastic decision problem. In this work, we propose a simulation-based optimization approach combining mathematical programming-based planning, and discrete event simulation to deal with our clinical trial materials supply chain management problem where uncertainties cannot be modeled analytically in a computationally tractable way. 2. Problem definition and assumptions 2.1 Multi-echelon production-distribution supply chain The production of drug begins with active ingredient manufacturing (API), which normally involves either a series of chemical synthesis and separation processes, or fermentation and purification processes. The API is next converted to a new drug product (NDP) by adding excipients and conducting a series of additional processing steps, followed by packaging and labelling (PL) to obtain the final drug product form. In addition to the new drug product, a placebo (the product without the API) and a comparator (a form containing a commercial drug targeting the same disease) are also produced and used. To avoid psychological biases, the placebo and comparator undergo the same manufacturing, packaging and labelling stages as the target drug to make sure the appearance of these three types are the same to insure effectiveness in double blinded clinical trials. The finished drug product forms are shipped to various clinical sites worldwide. Therefore, a clinical trial materials supply chain can be treated as a

3 Simulation-based Optimization Approach to Clinical Trial Supply Chain Management multi-echelon production/distribution supply chain including the API-NDP-PL manufacturing stages and the product distribution network. For purposes of this study we assume there are no feed material constraints. The API, NDP and PL stages are conducted in the same facility and share the same inventory location in the US. Furthermore, shipment times between these three production stages are neglected. Since, compared to commercial drug manufacturing, the volume of drugs used in a clinical trial is small, we assume there is no inventory capacity limit. All finished drugs (target drug, placebo, and comparator) will be kept in the same distribution center with a certain shelf life, and must be disposed of after their expiration date. The distribution network starts at the US distribution center and covers various clinical sites used in the clinical trial located around the world. 2.2 Batch operation of manufacture process Traditionally, the pharmaceutical industry uses the batch-campaign mode. In our models, the batch manufacturing process is described by campaign start time, number of batches in each campaign, batch size, batch processing time, drug type (target drug, placebo and comparator), and yield. Uncertainties exist in processing time and yield. Within each stage, there are multiple production lines of processing units working in parallel, and each production line could be utilized for different products. API stage only produces the active ingredient for the target drug of the trial, but there will be multiple product types at the NDP stage: target drugs at different dosage levels, placebo and comparator. Since the clinical trials will be conducted all over the world, drugs sent to a certain country should satisfy its country specific packaging and labelling requirements. Therefore, the number of stock keeping units (SKU) can grow significantly, depending on the design and topology of the clinical trial. 3. Simulation-based optimization approach 3.1 Computational framework The framework proposed for this study consists of a simulation of demands (by forecasting methods), planning method, and a discrete event simulation for assessing the robustness of the supply chain under different sources of uncertainties as depicted in Fig. 1. The forecasting function uses a simulation model to determine the demand profile for each drug product. Given demand forecasts, a planning model is used to determine the manufacturing campaign details and shipping plans. The model is implemented as a Mixed-Integer-Linear-Programs (MILP) and solved using CPLEX. A simulation model of the entire supply chain, which is developed using the discrete event simulation software, ExtendSim, captures all activities, operations and processes involved in the clinical trial. The operational plans developed via the MILP planning models serve as drivers for the execution of supply chain simulation. The quality and robustness of the plans are assessed by replicated simulation runs. Upon convergence to appropriate statistical criteria, the supply chain performance is improved by adjusting the key system parameters and repeating the Simulation-Optimization cycle. Fig. 1 Clinical trial supply chain management computational framework

4 Ye Chen, Linas Mockus, Seza Orcun, Gintaras V. Reklaitis 3.2 Demand forecasting The demand of each product, which is non-stationary, is obtained from detailed clinical site simulations. The arrival of patients can be treated as a Poisson process, and every patient is randomly assigned to different clinical trial dosages: target drug, placebo or comparator. During the treatment period, patients are required to follow preset visit profiles, which also determine the drug dispensation schedules. However, some patients may drop out during the course of the treatment for various reasons, such as loss of interest, dissatisfaction due to no observed improvement, or changes in personal life. The mean and variance of demand for each drug SKU are obtained from these simulations and are in turn used in the other supply chain decision models. 3.3 Planning As noted above, the entire clinical trial materials supply chain is divided into the API, NDP, PL, and Distribution network components. Under typical industry practice, a global coordinator works within a decentralized control supply chain, which coordinates each stage towards to a common objective. The global objective of a clinical trial materials supply chain is to satisfy the patient demand with minimum cost. The downstream demands along with campaigning/shipping plans create the demands for the upstream stages in terms of material requirement. Eqn. 1 and Eqn. 2 represent objective functions of the production and distribution sub-models, respectively. Each sub-problem seeks to minimize an objective function representing the total expected cost, consisting of several sub-problem specific cost factors. Demand data obtained from detailed patient enrollment forecasts and their simulations are aggregated into three discrete demand profile scenarios, each with certain probability. With distribution objective and constraints, an optimal shipment plan is obtained by formulating the distribution process as an MILP model solved by CPLEX. The shipment plans generate the demands for the manufacturing stages. Due to the space limitation the complete model equations are not presented herein. Min Cost = expected (Waste cost + Production cost + Holding cost) (Eqn. 1) Min Cost = expected (Waste cost + Penalty cost + Fixed cost + Variable Cost) = (cost of destruction of material + cost of product + cost of packaging component)+ (Inventory opportunity cost) + (cost of direct labor for entering shipment + cost of direct labor for processing shipment + cold chain container cost) + (cost of direct labor of selecting and picking + shipment cost + container cost) (Eqn. 2) 3.4 Discrete event simulation To investigate the quality of the plans generated, we represent each batch as a single transaction with specific properties such as start time, duration, batch type and size. Five simulation sub-models: API, NDP, PL, Distribution and clinical sites have been implemented. These models can be assembled to define any clinical trials supply chain. Within each sub-model, the batch is the flowing entity, moving through the network model. A batch waits for a specified period (could be sampled from a distribution or predefined as a property) of simulation time before proceeding to the next block. Also, this model dynamically communicates with decisions models through Excel files storing the manufacturing and distribution plans. To capture the effects of uncertainties in this supply chain, the complete supply chain simulation is repeated many times for different sampled values of the uncertain parameters to generate the distribution data with which to verify and assess the efficiency and quality of the plans generated by the decision models. The simulation model records the number of missed patients, the number of patients who successfully

5 Simulation-based Optimization Approach to Clinical Trial Supply Chain Management finished the treatment, the number of patients who drop out, and the average inventory at each clinical site and distribution center. The simulation results are used to restart the planning model to produce revised production and distribution plans. The planning and simulation loop is continued until the performance of the entire supply chain improves and converges to a satisfactory level. 4. Case study The proposed approach is demonstrated by a case study outlined in this section. The topology of the case study is shown in Fig. 2. There is only one active ingredient produced in the API stage, but four SKU s need to be produced in the NDP stage: placebo, comparator, high dosage and low dosage target drug. Since this clinical trial will be conducted in two continents (US and European), two different types of packaging and labeling are used: one for the Americas (countries A and B) and the other for the European (countries C and D). Thus, there will be eight SKUs in the final distribution center to be shipped to various clinical sites. The shelf life of these drugs is 8 months, treatment lasts for 6 weeks, and the enrollment period of this clinical trial is 24 months. There are 75 clinical sites in total: 36% of them are in country A, 24% in country B, 21% in country C, and 19% in country D. Patients arriving at each clinical site will be assigned to take either placebo or high-dose target drug or low-dose target drug or comparator randomly following 1:2:2:2 enrolment ratio. Fig. 2 Network of clinical trial supply chain case study Fig. 3 is the demand profile obtained from the demand simulation (see section 2.3). The increasing nature of the demand is due to the fact that enrollment is low at the beginning since it takes time to generate patient awareness of this clinical trial. With advertisement more and more patients enroll to the clinical trial. However, the enrollment rate drops as the trial nears the end. The valley in the figure is as a result of a combination of factors such as promotional incentives offered and variability of the enrollment start in clinical sites. The dropout rate of patients is 45% in this scenario. A patient is missed if there are not enough drugs available in that clinical site at the time of the visit. The results of the approach described in section 3 are shown in Fig. 4 and Table 1. As Fig. 4 demonstrates the inventory profiles vary significantly over time. 5. Conclusion and future work The clinical trial materials supply chain management problem is discussed and a simulation-based optimization approach, which combines stochastic mathematical planning with discrete event simulation, has been proposed. The quality and robustness of the plans generated by the planning model are assessed by replicated simulation runs.

6 Ye Chen, Linas Mockus, Seza Orcun, Gintaras V. Reklaitis We demonstrated our approach with a case study: a worldwide operated clinical trial materials supply chain management problem. The proposed approach yielded a production and distribution plan with 90% service level (Table 1). Also from the simulation, we can generate the inventory information at each clinical site. This information will be used in continuing research utilizing risk pooling strategies (e.g. Vidyarthi et al (2007)) to further mitigate the risks in clinical trials materials supply chain operation Demand Mean value Mean+std mean-std Month Fig. 3 Drug demand profile from simulation Table 1 Simulation Result Fig. 4 Drug inventories of various countries Patient Number country A country B country C country D missed Placebo dropped successful missed Dose1 dropped successful missed Dose2 dropped successful missed Comparator dropped successful Acknowledgements The authors would like to thank Eli Lilly and Company for introducing us to this problem and for their encouragement and support to pursue its solution. References 1. N. Shah, 2004, Pharmaceutical supply chains: key issues and strategies for optimization, Computers and Chemical Engineering, 28, D. C. Monkhouse, C. F. Carney, J. L. Clark, 2006, Drug Products for Clinical Trials, Informa Health Care. 3. C. A. Floudas, X. Lin, 2004, Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review, Computers and Chemical Engineering, 28, N. Vindyarthi, E. Celebi, S. Elhedhili, E. Jewkes, 2007, Integrated Production-Inventory- Distribution System Design with Risk Pooling: Model Formulation and Heuristic Solution, Transportation Science, 41, 3, D. Subramanian, J. F. Pekny, G. V. Reklaitis, 2001, A Simulation-optimization Framework for Research and Development Pipeline Management, AIChE Journal, 47(10),

Minimize overspend by gaining visibility of total demand

Minimize overspend by gaining visibility of total demand Minimize overspend by gaining visibility of total demand Patti Seymour 9th Annual Clinical Trials Supplies and Packaging October 10-12, 2011 BioProcess Technology Consultants www.bptc.com Supply and Demand

More information

Biopharmaceutical Portfolio Management Optimization under Uncertainty

Biopharmaceutical Portfolio Management Optimization under Uncertainty Ian David Lockhart Bogle and Michael Fairweather (Editors), Proceedings of the 22nd European Symposium on Computer Aided Process Engineering, 17-20 June 2012, London. 2012 Elsevier B.V. All rights reserved

More information

Best Practice Approaches to Improving Clinical Supply Chain Management. Matthew Do Client Development Lead Almac Pharmaceutical Services

Best Practice Approaches to Improving Clinical Supply Chain Management. Matthew Do Client Development Lead Almac Pharmaceutical Services Best Practice Approaches to Improving Clinical Supply Chain Management Matthew Do Client Development Lead Almac Pharmaceutical Services A variety of challenges complicate trial supply management Multiple

More information

Investigational Drugs: Investigational Drugs and Biologics

Investigational Drugs: Investigational Drugs and Biologics : I. PURPOSE The purpose of this policy is to establish procedures for the proper control, storage, use and handling of investigational drugs and biologics to ensure that adequate safeguards are in place

More information

Streamlining clinical trial supply management

Streamlining clinical trial supply management Life Sciences Clinical Trial Supply Management Streamlining clinical trial supply management Driving faster, more effective clinical trials In this white paper A critical step of the development chain:

More information

Developing a Strategy to Optimize Clinical Trial Supplies

Developing a Strategy to Optimize Clinical Trial Supplies Developing a Strategy to Optimize Clinical Trial Supplies Medidata and other marks used herein are trademarks of Medidata Solutions, Inc. All other trademarks are the property of their respective owners.

More information

A joint control framework for supply chain planning

A joint control framework for supply chain planning 17 th European Symposium on Computer Aided Process Engineering ESCAPE17 V. Plesu and P.S. Agachi (Editors) 2007 Elsevier B.V. All rights reserved. 1 A joint control framework for supply chain planning

More information

Optimal Planning of Closed Loop Supply Chains: A Discrete versus a Continuous-time formulation

Optimal Planning of Closed Loop Supply Chains: A Discrete versus a Continuous-time formulation 17 th European Symposium on Computer Aided Process Engineering ESCAPE17 V. Plesu and P.S. Agachi (Editors) 2007 Elsevier B.V. All rights reserved. 1 Optimal Planning of Closed Loop Supply Chains: A Discrete

More information

Integer Programming Model for Inventory Optimization for a Multi Echelon System

Integer Programming Model for Inventory Optimization for a Multi Echelon System Journal of Advanced Management Science Vol, No, January 06 Integer Programming Model for Inventory Optimization for a Multi Echelon System Bassem H Roushdy Basic and Applied Science, Arab Academy for Science

More information

Balancing Risk and Costs to Optimize the Clinical Supply Chain A Step Beyond Simulation

Balancing Risk and Costs to Optimize the Clinical Supply Chain A Step Beyond Simulation J Pharm Innov (2009) 4:96106 DOI 10.1007/s12247-009-9063-5 CASE REPORT Balancing Risk and Costs to Optimize the Clinical Supply Chain A Step Beyond Simulation Chedia Abdelkafi & Benoît H. L. Beck & Benoit

More information

IMP management at site. Dmitry Semenyuta

IMP management at site. Dmitry Semenyuta IMP management at site Dmitry Semenyuta TOP 5 FDA inspections finding 1999-2009 Center of Drug Evaluation and Research (CDER) Failure to follow the protocol Failure to keep adequate and accurate records

More information

Development of a decision support tool for supply network planning: A case study from the chemical industry

Development of a decision support tool for supply network planning: A case study from the chemical industry The 7th International Symposium on Operations Research and Its Applications (ISORA 08) Lijiang, China, October 31 Novemver 3, 2008 Copyright 2008 ORSC & APORC, pp. 18 24 Development of a decision support

More information

How the Global Supply Chain Organization is Managing the Transition at Eli Lilly & Company. Ronald Bohl

How the Global Supply Chain Organization is Managing the Transition at Eli Lilly & Company. Ronald Bohl How the Global Supply Chain Organization is Managing the Transition at Eli Lilly & Company September 2009 Ronald Bohl bohl_ron@lilly.com global research based pharmaceutical company dedicated to creating

More information

Reliability Modeling Software Defined

Reliability Modeling Software Defined Reliability Modeling Software Defined Using Titan Reliability Modeling Software May 30, 2014 Prepared by: The Fidelis Group 122 West Way, Suite 300 Lake Jackson, TX 77566 Fidelis Group, LLC All Rights

More information

Forecasting in Pharmaceutical Domain

Forecasting in Pharmaceutical Domain Forecasting in Pharmaceutical Domain Sales forecasting in Pharmaceutical domain is a complex task. Each company has its own specific set of issues, some of which are discussed below. Large number of SKUs

More information

INTEGRATED OPTIMIZATION OF SAFETY STOCK

INTEGRATED OPTIMIZATION OF SAFETY STOCK INTEGRATED OPTIMIZATION OF SAFETY STOCK AND TRANSPORTATION CAPACITY Horst Tempelmeier Department of Production Management University of Cologne Albertus-Magnus-Platz D-50932 Koeln, Germany http://www.spw.uni-koeln.de/

More information

Optimizing the Clinical Trial Supply Chain

Optimizing the Clinical Trial Supply Chain white paper Optimizing the Clinical Trial Supply Chain Ensuring the right items arrive in the right place, in the right quantity, at the right time and within budget. Testing out new medicines around the

More information

Analysis of Various Forecasting Approaches for Linear Supply Chains based on Different Demand Data Transformations

Analysis of Various Forecasting Approaches for Linear Supply Chains based on Different Demand Data Transformations Institute of Information Systems University of Bern Working Paper No 196 source: https://doi.org/10.7892/boris.58047 downloaded: 16.11.2015 Analysis of Various Forecasting Approaches for Linear Supply

More information

Introduction to Engineering System Dynamics

Introduction to Engineering System Dynamics CHAPTER 0 Introduction to Engineering System Dynamics 0.1 INTRODUCTION The objective of an engineering analysis of a dynamic system is prediction of its behaviour or performance. Real dynamic systems are

More information

Companies often face nonstationary demand due to product life cycles and seasonality, and nonstationary

Companies often face nonstationary demand due to product life cycles and seasonality, and nonstationary MANUFACTURING & SERVICE OPERATIONS MANAGEMENT Vol. 14, No. 3, Summer 2012, pp. 414 422 ISSN 1523-4614 (print) ISSN 1526-5498 (online) http://dx.doi.org/10.1287/msom.1110.0373 2012 INFORMS Single-Stage

More information

Using IVRS in Clinical Trial Management

Using IVRS in Clinical Trial Management Using IVRS in Clinical Trial Management Bill Byrom Interactive voice response systems can work for project managers as an inventory management tool, a real-time project information tool, and a subject

More information

A simulation based optimization approach to supply chain management under demand uncertainty

A simulation based optimization approach to supply chain management under demand uncertainty Computers and Chemical Engineering 28 (2004) 2087 2106 A simulation based optimization approach to supply chain management under demand uncertainty June Young Jung a, Gary Blau a, Joseph F. Pekny a, Gintaras

More information

System-Dynamics modelling to improve complex inventory management in a batch-wise plant

System-Dynamics modelling to improve complex inventory management in a batch-wise plant European Symposium on Computer Arded Aided Process Engineering 15 L. Puigjaner and A. Espuña (Editors) 2005 Elsevier Science B.V. All rights reserved. System-Dynamics modelling to improve complex inventory

More information

Botticelli: A Supply Chain Management Agent

Botticelli: A Supply Chain Management Agent Botticelli: A Supply Chain Management Agent M. Benisch, A. Greenwald, I. Grypari, R. Lederman, V. Naroditskiy, and M. Tschantz Department of Computer Science, Brown University, Box 1910, Providence, RI

More information

EVERYTHING YOU NEED TO KNOW ABOUT INVENTORY

EVERYTHING YOU NEED TO KNOW ABOUT INVENTORY EVERYTHING YOU NEED TO KNOW ABOUT INVENTORY Introduction Inventory is considered the necessary evil of the supply chain. In fact, there has been a whole movement; lean manufacturing that has tried to reduce

More information

Information Sharing in Supply Chain Management: A Literature Review on Analytical Research

Information Sharing in Supply Chain Management: A Literature Review on Analytical Research Information Sharing in Supply Chain Management: A Literature Review on Analytical Research Hyun-cheol Paul Choi California State University, Fullerton, CA In this paper, we reviewed the area of upstream

More information

- A case study on its performance compared to the current inventory control system at Arriva DK

- A case study on its performance compared to the current inventory control system at Arriva DK Division of Production Management Lund University Faculty of Engineering, LTH Centralization of inventory management for spare parts - A case study on its performance compared to the current inventory

More information

Optimization of warehousing and transportation costs, in a multiproduct multi-level supply chain system, under a stochastic demand

Optimization of warehousing and transportation costs, in a multiproduct multi-level supply chain system, under a stochastic demand Int. J. Simul. Multidisci. Des. Optim. 4, 1-5 (2010) c ASMDO 2010 DOI: 10.1051/ijsmdo / 2010001 Available online at: http://www.ijsmdo.org Optimization of warehousing and transportation costs, in a multiproduct

More information

Resource grouping selection to minimize the maximum over capacity planning

Resource grouping selection to minimize the maximum over capacity planning 2012 International Conference on Industrial and Intelligent Information (ICIII 2012) IPCSIT vol.31 (2012) (2012) IACSIT Press, Singapore Resource grouping selection to minimize the maximum over capacity

More information

Model, Analyze and Optimize the Supply Chain

Model, Analyze and Optimize the Supply Chain Model, Analyze and Optimize the Supply Chain Optimize networks Improve product flow Right-size inventory Simulate service Balance production Optimize routes The Leading Supply Chain Design and Analysis

More information

Prescriptive Analytics. A business guide

Prescriptive Analytics. A business guide Prescriptive Analytics A business guide May 2014 Contents 3 The Business Value of Prescriptive Analytics 4 What is Prescriptive Analytics? 6 Prescriptive Analytics Methods 7 Integration 8 Business Applications

More information

STRATEGIC CAPACITY PLANNING USING STOCK CONTROL MODEL

STRATEGIC CAPACITY PLANNING USING STOCK CONTROL MODEL Session 6. Applications of Mathematical Methods to Logistics and Business Proceedings of the 9th International Conference Reliability and Statistics in Transportation and Communication (RelStat 09), 21

More information

The Utilization of Shared Demand Information in a Textile Supply Chain

The Utilization of Shared Demand Information in a Textile Supply Chain The Utilization of Shared Demand Information in a Textile Supply Chain Yatsai Tseng Department of Business Administration, Tunghai University, Taichung, 407, Taiwan. 88643590 ext 3506 yttseng@mail.thu.edu.tw

More information

Understanding When to Employ IVR and IWR independently or in Combination

Understanding When to Employ IVR and IWR independently or in Combination Understanding When to Employ IVR and IWR independently or in Combination Scott C. Wong Sr. Systems Analyst, IVRS Central Management Global Investigational Materials Supply Chain Celgene Agenda What are

More information

The retrofit of a closed-loop distribution network: the case of lead batteries

The retrofit of a closed-loop distribution network: the case of lead batteries 20 th European Symposium on Computer Aided Process Engineering ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) 2010 Elsevier B.V. All rights reserved. The retrofit of a closed-loop distribution network:

More information

Taking a Leap Toward Global Supply Chain Efficiency

Taking a Leap Toward Global Supply Chain Efficiency Taking a Leap Toward Global Supply Chain Efficiency 2 Taking a Leap Toward Global Supply Chain Efficiency INTRODUCTION PROBLEM STATEMENT Pharmaceutical manufacturers face a number of challenges to produce

More information

Abstract. 1. Introduction. Caparica, Portugal b CEG, IST-UTL, Av. Rovisco Pais, 1049-001 Lisboa, Portugal

Abstract. 1. Introduction. Caparica, Portugal b CEG, IST-UTL, Av. Rovisco Pais, 1049-001 Lisboa, Portugal Ian David Lockhart Bogle and Michael Fairweather (Editors), Proceedings of the 22nd European Symposium on Computer Aided Process Engineering, 17-20 June 2012, London. 2012 Elsevier B.V. All rights reserved.

More information

Energy Management for Heat Intensive Production Plants using Mixed Integer Optimization

Energy Management for Heat Intensive Production Plants using Mixed Integer Optimization 20 th European Symposium on Computer Aided Process Engineering ESCAPE20 S. Pierucci and G. Buzzi Ferraris (Editors) 2010 Elsevier B.V. All rights reserved. Energy Management for Heat Intensive Production

More information

Case study of a batch-production/inventory system E.M.M. Winands 1, A.G. de Kok 2 and C. Timpe 3

Case study of a batch-production/inventory system E.M.M. Winands 1, A.G. de Kok 2 and C. Timpe 3 Case study of a batch-production/inventory system E.M.M. Winands 1, A.G. de Kok 2 and C. Timpe 3 The plant of BASF under consideration consists of multiple parallel production lines, which produce multiple

More information

How to Utilize an IVRS for Efficient Management of Clinical Supplies

How to Utilize an IVRS for Efficient Management of Clinical Supplies How to Utilize an IVRS for Efficient Management of Clinical Supplies Scott C. Wong IVRS Senior Systems Analyst Investigational Material Supply Chain Celgene Corporation Objective Provide a basic understanding

More information

Supply Chain Design and Inventory Management Optimization in the Motors Industry

Supply Chain Design and Inventory Management Optimization in the Motors Industry A publication of 1171 CHEMICAL ENGINEERING TRANSACTIONS VOL. 32, 2013 Chief Editors: Sauro Pierucci, Jiří J. Klemeš Copyright 2013, AIDIC Servizi S.r.l., ISBN 978-88-95608-23-5; ISSN 1974-9791 The Italian

More information

Ted Quiroz Director, Global Clinical Supply Chain Amgen Inc.

Ted Quiroz Director, Global Clinical Supply Chain Amgen Inc. Ted Quiroz Director, Global Clinical Supply Chain Amgen Inc. to serve patients Enbrel (etanercept) EPOGEN (Epoetin alfa) Aranesp (Darbepoetin alfa) Sensipar (cinacalcet HCl) Vectibix (panitumumab) We aspire

More information

Are Your Inventory Management Practices Outdated?

Are Your Inventory Management Practices Outdated? Fulfillment March 1, 2005 Key Facts Traditional inventory management practices are being made obsolete by increasing global sourcing and contract manufacturing, more dynamic product life cycles, and multi-channel.

More information

Strategic Framework to Analyze Supply Chains

Strategic Framework to Analyze Supply Chains Strategic Framework to Analyze Supply Chains 1 Andy Guo A Strategic Framework for Supply Chain Design, Planning, and Operation Part I: Understand the supply chain Part II: Supply chain performance Part

More information

Equipping your Forecasting Toolkit to Account for Ongoing Changes

Equipping your Forecasting Toolkit to Account for Ongoing Changes Equipping your Forecasting Toolkit to Account for Ongoing Changes Presented by: Roger Parlett Supply Chain Manager January 23, 2014 Overview Forecast Set-up Objectives of Creating a Forecast Identify Critical

More information

Optimizing Stochastic Supply Chains via Simulation: What is an Appropriate Simulation Run Length?

Optimizing Stochastic Supply Chains via Simulation: What is an Appropriate Simulation Run Length? Optimizing Stochastic Supply Chains via Simulation: What is an Appropriate Simulation Run Length? Arreola-Risa A 1, Fortuny-Santos J 2, Vintró-Sánchez C 3 Abstract The most common solution strategy for

More information

Clinical Supply Chain Management Driving Operational Performance

Clinical Supply Chain Management Driving Operational Performance Clinical Supply Chain Management Driving Operational Performance July 2010 PwC Your presenters EER1 Ellen Reilly Managing Director Pharma & Life Sciences Advisory Services 400 Campus Drive Florham Park,

More information

End to end Clinical Trial Supply Management with SAP

End to end Clinical Trial Supply Management with SAP End to end Clinical Trial Supply Management with SAP by Infosys Lodestone CTSM is a leading factor in conducting clinical studies. Table of Contents A Leading Factor in conducting Clinical Studies is the

More information

A MILP Scheduling Model for Multi-stage Batch Plants

A MILP Scheduling Model for Multi-stage Batch Plants A MILP Scheduling Model for Multi-stage Batch Plants Georgios M. Kopanos, Luis Puigjaner Universitat Politècnica de Catalunya - ETSEIB, Diagonal, 647, E-08028, Barcelona, Spain, E-mail: luis.puigjaner@upc.edu

More information

SIMULATION-BASED ANALYSIS OF THE BULLWHIP EFFECT UNDER DIFFERENT INFORMATION SHARING STRATEGIES

SIMULATION-BASED ANALYSIS OF THE BULLWHIP EFFECT UNDER DIFFERENT INFORMATION SHARING STRATEGIES SIMULATION-BASED ANALYSIS OF THE BULLWHIP EFFECT UNDER DIFFERENT INFORMATION SHARING STRATEGIES Yuri A. Merkuryev and Julija J. Petuhova Rik Van Landeghem and Steven Vansteenkiste Department of Modelling

More information

2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering

2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering 2014-2015 The Master s Degree with Thesis Course Descriptions in Industrial Engineering Compulsory Courses IENG540 Optimization Models and Algorithms In the course important deterministic optimization

More information

Logistics. Drug Pooling in the Clinical Trial Supply Chain

Logistics. Drug Pooling in the Clinical Trial Supply Chain Drug Pooling in the Clinical Trial Supply Chain Abstract Global clinical trials require efficient and robust supply chain which can bring more transparency and can introduce risk mitigation strategies.

More information

Automated Scheduling Methods. Advanced Planning and Scheduling Techniques

Automated Scheduling Methods. Advanced Planning and Scheduling Techniques Advanced Planning and Scheduling Techniques Table of Contents Introduction 3 The Basic Theories 3 Constrained and Unconstrained Planning 4 Forward, Backward, and other methods 5 Rules for Sequencing Tasks

More information

Extemporaneously Prepared Early Phase Clinical Trial Materials

Extemporaneously Prepared Early Phase Clinical Trial Materials Extemporaneously Prepared Early Phase Clinical Trial Materials Richard Hoffman, MS, RAC Eli Lilly & Co. Regulatory Advisor International Consortium for Innovation & Quality in Pharmaceutical Development

More information

Impact of Formula-Based ERP Applications on Pharmaceutical Manufacturers

Impact of Formula-Based ERP Applications on Pharmaceutical Manufacturers Impact of Formula-Based ERP Applications on Pharmaceutical Manufacturers Executive Summary Whether you re in the process of re-evaluating your existing ERP application or looking to replace your spreadsheets,

More information

Technical University of Mombasa Faculty of Applied and Health Sciences

Technical University of Mombasa Faculty of Applied and Health Sciences Technical University of Mombasa Faculty of Applied and Health Sciences DEPARTMENT OF MEDICAL SCIENCES DIPLOMA IN PHARMACEUTICAL TECHNOLOGY (DPT 11M) APM 2210 : DRUG SUPPLY & MANAGEMENT II SPECIAL/SUPPLEMENTARY:

More information

Modeling Stochastic Inventory Policy with Simulation

Modeling Stochastic Inventory Policy with Simulation Modeling Stochastic Inventory Policy with Simulation 1 Modeling Stochastic Inventory Policy with Simulation János BENKŐ Department of Material Handling and Logistics, Institute of Engineering Management

More information

Modeling Multi-Echelon Multi-Supplier Repairable Inventory Systems with Backorders

Modeling Multi-Echelon Multi-Supplier Repairable Inventory Systems with Backorders J. Service Science & Management, 2010, 3, 440-448 doi:10.4236/jssm.2010.34050 Published Online December 2010 (http://www.scirp.org/journal/jssm) Modeling Multi-Echelon Multi-Supplier Repairable Inventory

More information

The 505(b)(2) Drug Development Pathway:

The 505(b)(2) Drug Development Pathway: The 505(b)(2) Drug Development Pathway: When and How to Take Advantage of a Unique American Regulatory Pathway By Mukesh Kumar, PhD, RAC and Hemant Jethwani, MS The 505(b)(2) regulation offers a less expensive

More information

A MANAGER S ROADMAP GUIDE FOR LATERAL TRANS-SHIPMENT IN SUPPLY CHAIN INVENTORY MANAGEMENT

A MANAGER S ROADMAP GUIDE FOR LATERAL TRANS-SHIPMENT IN SUPPLY CHAIN INVENTORY MANAGEMENT A MANAGER S ROADMAP GUIDE FOR LATERAL TRANS-SHIPMENT IN SUPPLY CHAIN INVENTORY MANAGEMENT By implementing the proposed five decision rules for lateral trans-shipment decision support, professional inventory

More information

Chapter 11. MRP and JIT

Chapter 11. MRP and JIT Chapter 11 MRP and JIT (Material Resources Planning and Just In Time) 11.1. MRP Even if MRP can be applied among several production environments, it has been chosen here as a preferential tool for the

More information

MSD Supply Chain Programme Strategy Workshop

MSD Supply Chain Programme Strategy Workshop MSD Supply Chain Programme Strategy Workshop Day 2 APPENDIX Accenture Development Partnerships Benchmarking MSD s Current Operating Supply Chain Capability 1.0 Planning 2.0 Procurement 3.0 Delivery 4.0

More information

Optimal Tank Farm Operation

Optimal Tank Farm Operation Optimal Tank Farm Operation Sebastian Terrazas-Moreno Ignacio E. Grossmann John M. Wassick EWO Meeting Carnegie Mellon University March 2011 In collaboration with The Dow Chemical Company A tank farm is

More information

Taking Strategic Partnerships to the Next Level: An Alternative Approach to Licensing Your Development Asset

Taking Strategic Partnerships to the Next Level: An Alternative Approach to Licensing Your Development Asset Taking Strategic Partnerships to the Next Level: An Alternative Approach to Licensing Your Development Asset Introduction In this era of strategic development deals, inventiv Health has significantly broadened

More information

GUIDE TO THE CERTIFICATION IN HUMANITARIAN MEDICAL LOGISTICS PRACTICES (MEDLOG)

GUIDE TO THE CERTIFICATION IN HUMANITARIAN MEDICAL LOGISTICS PRACTICES (MEDLOG) GUIDE TO THE CERTIFICATION IN HUMANITARIAN MEDICAL LOGISTICS PRACTICES (MEDLOG) Delivered By: Sponsored By: Awarding Organisation: LOGISTICS LEARNING ALLIANCE GUIDE TO THE HLA CERTIFICATION IN HUMANITARIAN

More information

Product Tracing in Food Systems Executive Summary

Product Tracing in Food Systems Executive Summary Product Tracing in Food Systems Executive Summary IFT examined traceability (product tracing) in food systems under contract with the US Food and Drug Administration Center for Food Safety and Applied

More information

Outsourcing Analysis in Closed-Loop Supply Chains for Hazardous Materials

Outsourcing Analysis in Closed-Loop Supply Chains for Hazardous Materials Outsourcing Analysis in Closed-Loop Supply Chains for Hazardous Materials Víctor Manuel Rayas Carbajal Tecnológico de Monterrey, campus Toluca victor.rayas@invitados.itesm.mx Marco Antonio Serrato García

More information

Understanding results of pilot implementation 1 2

Understanding results of pilot implementation 1 2 Research Reports Articles Managing a Fashion Supply Chain An Alternative to the Crystal Ball by Satyashri Mohanty Implementing pull based supply chain solutions of Theory of Constraints involves significant

More information

Design, synthesis and scheduling of multipurpose batch plants via an effective continuous-time formulation

Design, synthesis and scheduling of multipurpose batch plants via an effective continuous-time formulation Computers and Chemical Engineering 25 (2001) 665 674 www.elsevier.com/locate/compchemeng Design, synthesis and scheduling of multipurpose batch plants via an effective continuous-time formulation X. Lin,

More information

U.S. Contract Research Outsourcing Market: Trends, Challenges and Competition in the New Decade. N8B7-52 December 2010

U.S. Contract Research Outsourcing Market: Trends, Challenges and Competition in the New Decade. N8B7-52 December 2010 U.S. Contract Research Outsourcing Market: Trends, Challenges and Competition in the New Decade December 2010 Table of Contents Notes on Methodology 8 Market Introduction and Segmentation Introduction

More information

whitepaper Impact of Formula- on Pharmaceutical Manufacturers www.aptean.com

whitepaper Impact of Formula- on Pharmaceutical Manufacturers www.aptean.com whitepaper Impact of Formula- Based ERP Applications on Pharmaceutical Manufacturers WHITEPAPER Essentials for pharmaceutical manufacturers 2 about Whether you re in the process of re-evaluating your existing

More information

Introduction. VP, Sales in a Global Courier Company

Introduction. VP, Sales in a Global Courier Company ARTICLE AUGUST 2014 Abstract: Impact analysis of Just in Time packaging and labeling on clinical supply chain Clinical supply chain is in intense pressure for better management due to factors like globalization

More information

Introduction to Strategic Supply Chain Network Design Perspectives and Methodologies to Tackle the Most Challenging Supply Chain Network Dilemmas

Introduction to Strategic Supply Chain Network Design Perspectives and Methodologies to Tackle the Most Challenging Supply Chain Network Dilemmas Introduction to Strategic Supply Chain Network Design Perspectives and Methodologies to Tackle the Most Challenging Supply Chain Network Dilemmas D E L I V E R I N G S U P P L Y C H A I N E X C E L L E

More information

Areté Inc. Avail Fact Sheet. Production Scheduling. Syrup Scheduling. Raw Material Scheduling

Areté Inc. Avail Fact Sheet. Production Scheduling. Syrup Scheduling. Raw Material Scheduling Areté Inc. Avail Fact Sheet Avail is an integrated suite of Supply Chain planning tools created to work within the beverage industry. Avail is the latest in a long line of proven Areté products that have

More information

Targeting Cancer: Innovation in the Treatment of Chronic Myelogenous Leukemia EXECUTIVE SUMMARY. New England Healthcare Institute

Targeting Cancer: Innovation in the Treatment of Chronic Myelogenous Leukemia EXECUTIVE SUMMARY. New England Healthcare Institute Targeting Cancer: Innovation in the Treatment of Chronic Myelogenous Leukemia New England Healthcare Institute NEHI Innovation Series March 2004 Executive Summary From drugs and medical devices, to information

More information

Optimization applications in finance, securities, banking and insurance

Optimization applications in finance, securities, banking and insurance IBM Software IBM ILOG Optimization and Analytical Decision Support Solutions White Paper Optimization applications in finance, securities, banking and insurance 2 Optimization applications in finance,

More information

FIXED CHARGE UNBALANCED TRANSPORTATION PROBLEM IN INVENTORY POOLING WITH MULTIPLE RETAILERS

FIXED CHARGE UNBALANCED TRANSPORTATION PROBLEM IN INVENTORY POOLING WITH MULTIPLE RETAILERS FIXED CHARGE UNBALANCED TRANSPORTATION PROBLEM IN INVENTORY POOLING WITH MULTIPLE RETAILERS Ramidayu Yousuk Faculty of Engineering, Kasetsart University, Bangkok, Thailand ramidayu.y@ku.ac.th Huynh Trung

More information

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture - 36 Location Problems In this lecture, we continue the discussion

More information

Millipore Supply Chain change in a crisis context

Millipore Supply Chain change in a crisis context Millipore Supply Chain change in a crisis context March 11th 2010 Fabrice Schneider, Supply Chain Manager Labwater Introduction 2008 2010 ww financial crisis 2008 2010 Millipore Supply Chain deep mutation

More information

Choosing Planning & Scheduling solutions for Metals

Choosing Planning & Scheduling solutions for Metals Choosing Planning & Scheduling solutions for Metals White Paper The planning and scheduling of metals production presents special problems because of the complexity of the manufacturing process and the

More information

A CONCEPTUAL DESIGN INITIATION FOR PRODUCTION-INVENTORY SYSTEM BASED ON MACROECONOMICS

A CONCEPTUAL DESIGN INITIATION FOR PRODUCTION-INVENTORY SYSTEM BASED ON MACROECONOMICS A CONCEPTUAL DESIGN INITIATION FOR PRODUCTION-INVENTORY SYSTEM BASED ON MACROECONOMICS Marzieh Akhondi a and S. Nurmaya Musa b Department of Mechanical Engineering, Faculty of Engineering, University of

More information

WHITE PAPER: ADAPTIVE CLINICAL TRIALS

WHITE PAPER: ADAPTIVE CLINICAL TRIALS WHITE PAPER: ADAPTIVE CLINICAL TRIALS P. Ranganath Nayak, Chief Executive Officer, Cytel Inc. James A. Bolognese, Senior Director, Cytel Consulting The Adaptive Concept The clinical development of drugs

More information

Joint Location-Two-Echelon-Inventory Supply chain Model with Stochastic Demand

Joint Location-Two-Echelon-Inventory Supply chain Model with Stochastic Demand Joint Location-Two-Echelon-Inventory Supply chain Model with Stochastic Demand Malek Abu Alhaj, Ali Diabat Department of Engineering Systems and Management, Masdar Institute, Abu Dhabi, UAE P.O. Box: 54224.

More information

Supporting the Perfect Order: Collaborative S&OP and VMI

Supporting the Perfect Order: Collaborative S&OP and VMI Supporting the Perfect Order: Collaborative S&OP and VMI October 30, 2012 Frankfurt, Germany Gary Neights Director, Product Management The Multi-Echelon Supply Chain Plan Your Supplier s Suppliers Your

More information

Introduction. Chapter 1

Introduction. Chapter 1 Chapter 1 Introduction The success of Japanese companies in the second half of the 20th century has lead to an increased interest in inventory management. Typically, these companies operated with far less

More information

Practical Applications for Clinical Demand and Operations Planning

Practical Applications for Clinical Demand and Operations Planning Practical Applications for Clinical Demand and Operations Planning Randy Schwemmin Genentech Clinical Demand and Supply Planning Biotech Supply Chain Academy 8 November 2011 Agenda Vision for Clinical

More information

They try to discover what kinds of cells the BNV infects, how it kills those cells, and what happens to the surrounding cells.

They try to discover what kinds of cells the BNV infects, how it kills those cells, and what happens to the surrounding cells. A Vaccine s Journey 2 A Journey From the time an idea blooms in someone s mind, to the time a nurse says give me your arm, the development of a vaccine takes10 or more years. The stages of development

More information

SINGLE-STAGE MULTI-PRODUCT PRODUCTION AND INVENTORY SYSTEMS: AN ITERATIVE ALGORITHM BASED ON DYNAMIC SCHEDULING AND FIXED PITCH PRODUCTION

SINGLE-STAGE MULTI-PRODUCT PRODUCTION AND INVENTORY SYSTEMS: AN ITERATIVE ALGORITHM BASED ON DYNAMIC SCHEDULING AND FIXED PITCH PRODUCTION SIGLE-STAGE MULTI-PRODUCT PRODUCTIO AD IVETORY SYSTEMS: A ITERATIVE ALGORITHM BASED O DYAMIC SCHEDULIG AD FIXED PITCH PRODUCTIO Euclydes da Cunha eto ational Institute of Technology Rio de Janeiro, RJ

More information

Summary and general discussion

Summary and general discussion Chapter 7 Summary and general discussion Summary and general discussion In this thesis, treatment of vitamin K antagonist-associated bleed with prothrombin complex concentrate was addressed. In this we

More information

Realizing the Benefits of Finite Capacity Scheduling to Manage Batch Production Systems

Realizing the Benefits of Finite Capacity Scheduling to Manage Batch Production Systems Presented at the WBF North American Conference Baltimore, MD, USA 30 April - 4 May 2007 67 Alexander Drive PO Box 12277 Research Triangle Park, NC 27709 +1.919.314.3970 Fax: +1.919.314.3971 E-mail: info@wbf.org

More information

Balancing Responsiveness and Economics in Process Supply Chain Design with Multi-Echelon Stochastic Inventory

Balancing Responsiveness and Economics in Process Supply Chain Design with Multi-Echelon Stochastic Inventory Carnegie Mellon University Research Showcase @ CMU Department of Chemical Engineering Carnegie Institute of Technology 9-2009 Balancing Responsiveness and Economics in Process Supply Chain Design with

More information

Overview of Drug Development: the Regulatory Process

Overview of Drug Development: the Regulatory Process Overview of Drug Development: the Regulatory Process Roger D. Nolan, PhD Director, Project Operations Calvert Research Institute November, 2006 Adapted from course taught by Cato Research Background: Roger

More information

CLINICAL DEVELOPMENT OPTIMIZATION

CLINICAL DEVELOPMENT OPTIMIZATION PAREXEL CLINICAL RESEARCH SERVICES CLINICAL DEVELOPMENT OPTIMIZATION Enhancing the clinical development process to achieve optimal results ADVANCED TECHNOLOGY COMBINED WITH INTELLIGENT THINKING CAN HELP

More information

Keywords: Single-vendor Inventory Control System, Potential Demand, Machine Failure, Participation in the Chain, Heuristic Algorithm

Keywords: Single-vendor Inventory Control System, Potential Demand, Machine Failure, Participation in the Chain, Heuristic Algorithm Indian Journal of Fundamental and Applied Life Sciences ISSN: 31 63 (Online) An Open Access, Online International Journal Available at www.cibtech.org/sp.ed/jls/01/03/jls.htm 01 Vol. (S3), pp. 1781-1790/Valiporian

More information

INVENTORY FLOW MANAGEMENT PROCESS: - FMCG (BEVERAGES) SECTOR

INVENTORY FLOW MANAGEMENT PROCESS: - FMCG (BEVERAGES) SECTOR INVENTORY FLOW MANAGEMENT PROCESS: - FMCG (BEVERAGES) SECTOR Shweta Rai Lecturer, Dept of Computer Science (CS)/ Information Technology (IT) E-mail- shweta.ajay.aditi@gmail.com United College of Engineering

More information

EVALUATING REFINERY SUPPLY CHAIN POLICIES AND INVESTMENT DECISIONS THROUGH SIMULATION-OPTIMIZATION. Arief Adhitya

EVALUATING REFINERY SUPPLY CHAIN POLICIES AND INVESTMENT DECISIONS THROUGH SIMULATION-OPTIMIZATION. Arief Adhitya Proceedings of the 2006 Winter Simulation Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds. EVALUATING REFINERY SUPPLY CHAIN POLICIES AND INVESTMENT DECISIONS

More information

A Stability Program for the Distribution of Drug Products

A Stability Program for the Distribution of Drug Products A Stability Program for the Distribution of Drug Products Teresa I. Lucas*, Rafik H. Bishara, and Robert H. Seevers Drug products must be transported in a manner that ensures products will be maintained

More information

A Continuous-Time Formulation for Scheduling Multi- Stage Multi-product Batch Plants with Non-identical Parallel Units

A Continuous-Time Formulation for Scheduling Multi- Stage Multi-product Batch Plants with Non-identical Parallel Units European Symposium on Computer Arded Aided Process Engineering 15 L. Puigjaner and A. Espuña (Editors) 2005 Elsevier Science B.V. All rights reserved. A Continuous-Time Formulation for Scheduling Multi-

More information

Analysis of a Production/Inventory System with Multiple Retailers

Analysis of a Production/Inventory System with Multiple Retailers Analysis of a Production/Inventory System with Multiple Retailers Ann M. Noblesse 1, Robert N. Boute 1,2, Marc R. Lambrecht 1, Benny Van Houdt 3 1 Research Center for Operations Management, University

More information