Novel Governance Model for Planning in Pharmaceutical Quality Control Laboratories

Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Information Control Problems in Manufacturing Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Information Control Problems in Manufacturing Available online at www.sciencedirect.com Bergamo, Italy, June 11-13, 2018 Information Control Problems in Information Control Problems in Manufacturing Manufacturing Proceedings,16th IFAC Symposium on Bergamo, Italy, June 11-13, 2018 Bergamo, Italy, Italy, JuneProblems 11-13, 2018 2018 Bergamo, June 11-13, Information Control in Manufacturing Bergamo, Italy, June 11-13, 2018

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IFAC PapersOnLine 51-11 (2018) 484–489

Novel Governance Model for Planning in Novel Novel Governance Governance Model Model for for Planning Planning in in Pharmaceutical Quality Pharmaceutical Quality Control Novel Governance Model for Control Planning in Pharmaceutical Quality Control Laboratories Laboratories Pharmaceutical Quality Control Laboratories ∗ Laboratories ∗ Miguel R. Lopes ∗,∗∗ ∗,∗∗ Andrea Costigliola ∗ Rui M. Pinto ∗ Miguel Miguel Miguel Miguel

R. Lopes ∗,∗∗ Andrea Rui M. ∗ ∗ ∗∗ Costigliola ∗∗ Pinto ∗,∗∗Vieira ∗Sousa R. Lopes Andrea Costigliola Rui Susana M. C. ∗∗ Joao ∗∗ Pinto R. LopesM. Andrea Costigliola Rui M. M. Pinto ∗ Susana M. Vieira Joao M. C. Sousa ∗∗ ∗∗ ∗,∗∗ ∗ ∗∗ Joao ∗∗ Pinto ∗ Susana M. Vieira Vieira M. C. C. Sousa Sousa R. LopesM. Andrea Costigliola Rui M. Susana Joao M. ∗Susana M. Vieira ∗∗ Joao M. C. Sousa ∗∗ ∗ Hovione FarmaCiencia S.A, Lisbon, Portugal ∗ Hovione FarmaCiencia S.A, Lisbon, Portugal ∗∗ Hovione Superior FarmaCiencia S.A, Lisbon, Portugal Portugal Tecnico, University of Lisbon, Portugal ∗∗ IDMEC, ∗Instituto Hovione FarmaCiencia S.A, Lisbon, ∗∗ IDMEC, ∗Instituto Superior Tecnico, University of Lisbon, Portugal ∗∗ IDMEC, Tecnico, University of Hovione Superior FarmaCiencia S.A, Lisbon, Portugal IDMEC, Instituto Instituto Superior Tecnico, University of Lisbon, Lisbon, Portugal Portugal ∗∗ IDMEC, Instituto Superior Tecnico, University of Lisbon, Portugal Abstract: Abstract: A A simulation simulation model model of of pharmaceutical pharmaceutical quality quality control control laboratories laboratories was was employed employed as as Abstract: A simulation model of pharmaceutical quality control laboratories was employed as a benchmarking platform to estimate the performance of a new facility under alternative govAbstract: A simulation model of pharmaceutical quality control laboratories was employed as a benchmarking platform to estimate the performance of a new facility under alternative gova benchmarking benchmarking platform to estimate estimate the performance performance of control aprocessing new facility facility under alternative governance models. Key performance metrics, such as sample time and utilization rates Abstract: A simulation model of pharmaceutical quality laboratories was employed as a platform to the of a new under alternative governance models. Key performance metrics, such as sample processing time and utilization rates ernance models. Key performance metrics, such as as sample sample processing time and utilization utilization rates of analytical staff and equipment were computed and analyzed under alternative governance a benchmarking platform to estimate the performance of a new facility under alternative governance models. Key performance metrics, such processing time and rates of analytical staff and equipment were computed and analyzed under alternative governance of staff and equipment were and under alternative governance scenarios, in to evaluate solutions to be in Two were ernance models. Key metrics, as sample processing time andframeworks utilization rates of analytical analytical staff and equipment were computed computed and analyzed analyzed under alternative governance scenarios, in order order toperformance evaluate solutions to such be implemented implemented in practice. practice. Two frameworks were scenarios, in order to evaluate solutions to be implemented in practice. Two frameworks were compared, leading the conclusion that higher efficiency can be achieved under free-for-all of analytical staff and equipment were computed and analyzed under alternative governance scenarios, inleading order to solutions to higher be implemented in practice. Two under frameworks were compared, to evaluate the conclusion that efficiency can be achieved free-for-all compared, inleading leading theneed conclusion that higher efficiency can be achieved achieved under free-for-all governance, without the to procure additional resources. scenarios, order to evaluate solutions to be implemented in practice. Two frameworks were compared, the conclusion that higher efficiency can be under free-for-all governance, without the need to procure additional resources. governance,leading withouttothe the need to procure procure additional resources. compared, theneed conclusion thatadditional higher efficiency can be achieved under free-for-all governance, without to resources. © 2018, IFACwithout (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. governance, the need to procure additional resources. Keywords: Keywords: Quality Quality Control Control Laboratory, Laboratory, Process Process Modelling, Modelling, Planning, Planning, Discrete Discrete Event Event Keywords: Quality Control Laboratory, Process Modelling, Planning, Discrete Simulation, Performance Evaluation Keywords: Quality Control Laboratory, Process Modelling, Planning, Discrete Event Event Simulation, Performance Evaluation Simulation, Performance Evaluation Keywords: Control Laboratory, Process Modelling, Planning, Discrete Event Simulation,Quality Performance Evaluation Simulation, Performance Evaluation 1. higher 1. INTRODUCTION INTRODUCTION higher efficiency efficiency without without the the need need to to procure procure additional additional 1. higher efficiency efficiency without without the the need need to to procure procure additional additional resources. 1. INTRODUCTION INTRODUCTION higher resources. resources. 1. INTRODUCTION higher efficiency without the need to procure additional resources. Pharma 4.0, the extension of the fourth industrial revoPharma 4.0, the extension of the fourth industrial revo- resources. 2. Pharma extension the industrial revolution to 4.0, the the pharmaceutical realm, promises to deliver a Pharma 4.0, the extension of ofrealm, the fourth fourth industrial revo2. QC QC LABORATORY LABORATORY MANAGEMENT MANAGEMENT lution to the pharmaceutical promises to deliver a 2. QC LABORATORY MANAGEMENT MANAGEMENT 2. QC LABORATORY lution to the pharmaceutical promises deliver aa productivity leap across keyofrealm, focus areas: drugto discovery, Pharma 4.0, the extension the fourth industrial revolution to the pharmaceutical realm, promises to deliver productivity leap across key focus areas: drug discovery, 2. QC LABORATORY Laboratory managers face the challenges productivity leap across key focus areas: drug discovery, development, manufacturing and marketing. With blocklution to the leap pharmaceutical realm, promises todiscovery, deliver a Laboratory managers face the MANAGEMENT productivity across key and focusmarketing. areas: drug challenges of of assembling assembling development, manufacturing With blockLaboratory managers face the challenges of assembling a team composed of the appropriate number analysts Laboratory managers face the challenges of of assembling development, manufacturing and marketing. With blockbuster drugs nearing their patent expiration date and productivity leap across key focus areas: drug discovery, development, and marketing. With team composed of the appropriate number of analysts buster drugs manufacturing nearing their patent expiration dateblockand aLaboratory a team composed of the appropriate number of analysts and ensuring that the available equipment is sufficient managers face the challenges of assembling a team composed ofthe theavailable appropriate number isof analysts buster drugs nearing their patent expiration date and and declining R&Dmanufacturing productivity, the marketing. pharmaceutical Supply development, and With blockbuster drugs nearing their patent expiration date and ensuring that equipment sufficient declining R&D productivity, the pharmaceutical Supply and ensuring that the available equipment is sufficient to process incoming samples in a timely manner, whilst a team composed of the appropriate number of and ensuring that the available equipment is sufficient declining R&D productivity, the pharmaceutical Supply Chain (SC) hasnearing developed into a surging researchdate topic, as to process incoming samples in a timely manner,analysts buster drugs their patent expiration and declining R&D productivity, the pharmaceutical Supply whilst Chain (SC) has developed into a surging research topic, as to process incoming samples in a timely manner, whilst abiding to the industry standard Good Manufacturing and ensuring that the available equipment is sufficient to process incoming samples in a timely manner, whilst Chain (SC) has developed into a surging research topic, as innovative companies pursue new standards of operational declining R&D productivity, the pharmaceutical Supply Chain (SC) has developed into a surging research topic, as abiding to the industry standard Good Manufacturing innovative companies pursue new standards of operational abiding to the industry standard Good Manufacturing Practices. However, given the basic tools employed by to process incoming samples in a timely manner, whilst abiding to the industry standard Good Manufacturing innovative companies pursue new standards of operational excellence. As noted by Shah (2004), modelling and optiChain (SC) has developed into a surging research topic, as innovative companies pursue standards of operational Practices. However, given the basic tools employed by excellence. As noted by Shahnew (2004), modelling and optiPractices. given the basic tools employed by QCL managers, planning and of QC work reabiding to However, the industry Manufacturing Practices. However, givenstandard thescheduling basicGood tools employed by excellence. As (2004), modelling and optimization ofcompanies SCnoted agentsby areShah emerging trends inofthe industry, innovative pursue new standards operational excellence. As noted by Shah (2004), modelling and optiQCL managers, planning and scheduling of QC work remization of SC agents are emerging trends in the industry, QCL managers, planning and scheduling of QC work remains a manual, time consuming task with ample room Practices. However, given the basic tools employed by QCL managers, planning and scheduling of QC work remization of SC agents are emerging trends in the industry, propelled by applied Operations Research methodologies excellence. As noted by Shah (2004), modelling and optimization of SC agents are emerging trends in the industry, mains a manual, time consuming task with ample room propelled by applied Operations Research methodologies mains a manual, time consuming task with ample room for improvement. The issue is magnified in Contract DeQCL managers, planning and scheduling of QC work remains a manual, time consuming task with ample room propelled by applied Operations Research methodologies that leverage the advent of smart manufacturing facilities mization of SC agents are emerging trends in the industry, propelled by applied Operations Research methodologies for improvement. The issue is magnified in Contract Dethat leverage the advent of smart manufacturing facilities improvement. The is in Development and Organizations (CDMOs), mains a manual, time consuming task with ample room for improvement. The issue issue is magnified magnified in Contract Contract Dethat leverage the smart manufacturing facilities and the dissemination ofof the Internet of Things in the for propelled by applied Operations Research methodologies that the leverage the advent advent ofthe smart manufacturing facilities velopment and Manufacturing Manufacturing Organizations (CDMOs), and dissemination of Internet of Things in the velopment and Manufacturing Organizations (CDMOs), where the complexity of these tasks is compounded by the for improvement. The issue is magnified in Contract Deand Manufacturing Organizations (CDMOs), and the dissemination of the Internet of Things Thingsfacilities in the the velopment strive towards process optimization. that leverage the advent of smart manufacturing and the dissemination of the Internet of in where the complexity of these tasks is compounded by the strive towards process optimization. where the complexity of these tasks is compounded by the multitude of concurrent projects. As noted by Maslaton velopment and Manufacturing Organizations (CDMOs), where the complexity of these tasks is compounded by the strive towards process optimization. and the dissemination of the Internet of Things in the multitude of concurrent projects. As noted by Maslaton strive towards process optimization. Quality Control (QC) is of paramount importance in the where multitude of concurrent concurrent projects. As noted by Maslaton Maslaton (2012), a dedicated solution is required to efficiently plan the complexity of these tasks is compounded by the multitude of projects. As noted by Quality Control (QC) is of paramount importance in the strive towards process optimization. (2012), a dedicated solution is required to efficiently plan Quality (QC) paramount importance the pharmaceutical industry, with companies having to in follow Quality Control Control industry, (QC) is is of of paramount importance in the (2012), aa dedicated solution is required to efficiently plan and schedule QCL resources, increasing the accuracy of multitude of concurrent projects. As noted by Maslaton (2012), dedicated solution is required to efficiently plan pharmaceutical with companies having to follow schedule QCL resources, increasing the accuracy of pharmaceutical with companies having to follow strict guidelines enforced by regulatory agencies. The Quality Control industry, (QC) is of paramount importance the and pharmaceutical industry, with companies having to in follow and QCL resources, accuracy of estimated number resources under uncertain demand (2012), a dedicated solution is increasing required tothe efficiently plan and schedule schedule QCL of resources, increasing the accuracy of strict guidelines enforced by regulatory agencies. The estimated number of resources under uncertain demand strict guidelines enforced by regulatory agencies. The demand for QC work is dependent on other operational pharmaceutical industry, with companies having to follow strict guidelines enforced by regulatory agencies. The and estimated number of resources under uncertain demand for service and reducing the time spent by supervisors. schedule QCL resources, increasing the accuracy of estimated number of resources under uncertain demand demand for QC work is dependent on other operational for service and reducing the time spent by supervisors. demand for is other operational areas, fluctuating qualitatively andon quantitatively over strict guidelines enforced by regulatory agencies. The estimated demand for QC QC work work is dependent dependent onquantitatively other operational for service service number and reducing reducing the time timeunder spentuncertain by supervisors. supervisors. of resources demand for and the spent by areas, fluctuating qualitatively and over areas, fluctuating qualitatively over time. These services are carried and outonquantitatively inother Quality Control demand for QC work is dependent operational areas, fluctuating qualitatively quantitatively over 2.1 Related Work for service reducing the time spent by supervisors. time. These services are carried and out in Quality Control Relatedand Work time. These services are out in Quality Control Laboratories (QCLs), keycarried entities that can constitute a 2.1 areas, fluctuating qualitatively and quantitatively over 2.1 Related Work time. These services are carried out in Quality Control 2.1 Related Work Laboratories (QCLs), key entities that can constitute a Laboratories (QCLs), key entities that constitute bottleneck the level. time. Theseto are in can Quality Controlaa 2.1 Laboratories (QCLs), keycarried entities that can constitute Related Work Janse and Kateman (1984) first established the use of bottleneck toservices the SC SC service service level.out and Kateman (1984) first established the use of bottleneck to the SC service level. Laboratories (QCLs), key entities that can constitute a Janse Janse and Kateman first established the of bottleneck to the SC service level. queueing theory based simulation aa viable approach Janse and Kateman (1984) first as established the use use to of Considering a new state of the art facility as a case study, a queueing theory based(1984) simulation as viable approach to Considering a new state of the art facility as a case study, a bottleneck to the SC service level. queueing theory based simulation as a viable approach to model QCLs; later on, Klaessens et al. (1988) presented Janse and Kateman (1984) first established the use of queueing theory based simulation as a viable approach to Considering a new state of the art facility as a case study, a simulation study was conducted to estimate key laboratory model QCLs; later on, Klaessens et al. (1988) presented Consideringstudy a newwas state of the art as key a case study, a model QCLs; later on, Klaessens et al. (1988) presented simulation conducted tofacility estimate laboratory a decision support system that combined historical data queueing theory based simulation as a viable approach to model QCLs; later on, Klaessens et al. (1988) presented simulation conducted to estimate key laboratory performance metrics and assist laboratory managers in thea a decision support system that combined historical data Considering ametrics newwas state ofassist the art asmanagers a case study, simulation study study was conducted tofacility estimate key laboratory performance and laboratory in the model a decision decision support system that combined historical data with aa rule-based compiled from expert knowlQCLs; laterframework on, Klaessens et al. (1988) presented support system that combined historical data performance metrics and assist laboratory managers in task of resource planning. Alternative Governance Models simulation study was conducted to estimate key laboratory rule-based framework compiled from expert knowlperformance metrics and assist laboratory managers in the the awith task of resource planning. Alternative Governance Models with a rule-based framework compiled from expert knowledge to derive, test and compare laboratory organization a decision support system that combined historical data with a rule-based framework compiled from expert knowltask of resource planning. Alternative Governance Models (GMs) were metrics benchmarked, resulting inGovernance the proposal ofthea edge to derive, test and compare laboratory organization performance and assist laboratory managers inof task of resource planning. Alternative Models (GMs) were benchmarked, resulting in the proposal aa with edge to derive, test and compare laboratory organization structures. The most comprehensive work in this field a rule-based framework compiled from expert knowledge to derive, test and compare laboratory organization (GMs) were benchmarked, resulting in the proposal of novel free-for-all resource allocation policy that achieves task of resource planning. Alternative Governance Models structures. The most comprehensive work in this (GMs)free-for-all were benchmarked, resulting in the proposal of a structures. The most comprehensive work in this field novel resource allocation policy that achieves field was conducted by Costigliola et al. (2017). The author edge to derive, test and compare laboratory organization structures. The most comprehensive work in this field novel resource policy that (GMs) were benchmarked, resulting in the proposal of a was conducted by Costigliola et al. (2017). The author novel free-for-all free-for-all resource allocation allocation policy that achieves achieves was conducted by Costigliola et al. (2017). The author developed a simulation model of a pharmaceutical QCL structures. The most comprehensive work in this field was conducted by Costigliola et al. (2017). The author  novel resource allocation that achieves developed a simulation model of a pharmaceutical QCL This free-for-all work was supported by FCT, throughpolicy IDMEC, under project  work was supported by FCT, through IDMEC, under project developed aa simulation model of aa pharmaceutical QCL covering the entire analytical workflow, reinstating was conducted by Costigliola et al. (2017). The author  This developed simulation model of pharmaceutical QCL  (UID/EMS/50022/2013). S. M. Vieira acknowledges the support This work was supported by FCT, through IDMEC, under project covering the entire analytical workflow, reinstating disdisThis work was supportedS.byM. FCT, through IDMEC, under project (UID/EMS/50022/2013). Vieira acknowledges the support covering the entire workflow, reinstating discrete as the tool to developed asimulation simulation of a pharmaceutical QCL covering the entire analytical analytical workflow, dis by Program Investigador (IF/00833/2014) from co(UID/EMS/50022/2013). S.FCT M. Vieira acknowledges theFCT, support crete event event simulation as model the preferred preferred tool reinstating to model model QCLs QCLs (UID/EMS/50022/2013). S. M. Vieira acknowledges the support This work was supported by FCT, through IDMEC, under project by Program Investigador FCT (IF/00833/2014) from FCT, cocrete event simulation as the preferred tool to model QCLs and paving the way for future operational studies such as covering the entire analytical workflow, reinstating funded by the European Social Fund (ESF) through the Operational crete event simulation as the preferred tool to model QCLs by Program Investigador FCT (IF/00833/2014) from FCT, coby Program (IF/00833/2014) FCT, co(UID/EMS/50022/2013). S.FCT M. Vieira acknowledges support and paving the way for future operational studies suchdisas funded by the Investigador European Social Fund (ESF) through from the the Operational and paving the way for future operational studies such as Program Potential (POPH). the one presented in this paper. crete event simulation as the preferred tool to model QCLs funded byHuman the Investigador European Social Fund (ESF) through through from the Operational Operational and paving the way for future operational studies such as funded by the European Social Fund (ESF) the by Program FCT (IF/00833/2014) FCT, coProgram Human Potential (POPH). the one presented in this paper. Program Human Potential (POPH). the one presented in this paper. and paving the way for future operational studies such as Program Potential (POPH). the one presented in this paper. funded byHuman the European Social Fund (ESF) through the Operational Program Human Potential (POPH). the one presented in this paper. 2405-8963 © 2018 2018, IFAC IFAC (International Federation of Automatic Control) Copyright 491 Hosting by Elsevier Ltd. All rights reserved. Copyright © 2018 IFAC 491 Peer review© responsibility of International Federation of Automatic Copyright 2018 491 Copyright © under 2018 IFAC IFAC 491 Control. 10.1016/j.ifacol.2018.08.365 Copyright © 2018 IFAC 491

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3. QC LABORATORY SIMULATION MODEL 3.1 Analytical Work Overview Analytical work performed in pharmaceutical QCLs encompasses samples of several types. In addition to analysis of raw materials, intermediates and final products, QCLs execute tests to monitor ongoing production batches (inprocess control), attest the cleanliness of manufacturing systems (change of line) and conduct stability studies. The types of work considered in this study are listed below. • • • •

Change of Line (COL) Product Stability (TE) Fast Analysis (FA) In-process Control (IPC)

• • • •

Raw Material (RM) Intermediates (IN) Final Product (FP) Miscellaneous (Misc.)

Samples compete for the same resources (analysts, equipment) and, depending on the type, can have different priority degrees. Aside from requiring its own specific equipment, each analytical test follows the procedure stated in the analytical method, a document containing relevant information concerning the analytical procedure, such as: safety and handling precautions, equipment operating conditions, procedure steps and values to control. The six most frequently performed analytical techniques, amounting to the critical mass of work to be carried out in the QCL considered in scope of this study are: • • • • • •

Differential Scanning Calorimetry (DSC) Gas Cromatography (GC) Karl Fischer Titration (KF) High Performance Liquid Cromatography (HPLC) Particle Size Analysis (PSA) X-Ray Powder Diffraction (XRPD)

Samples must be prepared before being analyzed. Additionally, the equipment must be configured before conducting the procedure; this requires the analyst to calibrate the parameters according to information specified in the analytical method and, in the case of equipment requiring its suitability to be validated, to allocate the solutions used for this purpose. This step can be rather time-consuming, but does not require the analyst to be present during its execution. Once the system’s suitability has been checked by the analyst, the equipment is deemed available to analyse samples according to the ratified method. Once the analysis is finished, the analyst collects the sample and processes the results. A representation of the generic analytical workflow in Business Process Modelling Notation (BPMN) notation in Figure 1.

Quality Control Laboratory

System Preparation

Suitability Required? Equipment Setup

Sample arrives at the Laboratory Wait for Sample

Input: Analytical Method Parameters

Schedule Report Review

Sample Preparation

System Suitability

Input: Analytical Method Parameters

Await Suitability / Sample Preparation

Analysis

Data Processig

Check System Suitability

Output: Suitability Checklist

Output: Analytical Report

3.2 Simulation Study Scope QC services at the CDMO considered in this study are divided in branches, referred to as A, B, C and S. Undeterred by the fact that the pool of resources could theoretically be shared between branches, as the analysts share the same competences and certifications, they operate contiguously under proprietary resource allocation policies. This structured regime fails to capitalize on possible benefits that a free-for-all approach could entail. A scenariobased approach was thus devised to assess the impact of (1) breakdown by branches, (2) varying analyst schedule configurations and (3) high-level sample allocation and scheduling policies on system performance under the two governance models (structured vs. free-for-all ). The review of related work conducted in the context of this study divulged Discrete Event Systems (Cassandras and Lafortune (2009)) as the methodology of choice to model QCLs for simulation purposes. The discrete event simulation paradigm implemented in modern commercial software revolves around the definition of entities, that flow through the system along steps of an underlying logic framework. Entities seize the capacity of resources for given periods of time, as they undergo some process. Under this agent-based architecture, and in the context of QC laboratories, samples are modelled as entities, with equipment and analysts being treated as resources. Relevant model parameters include: Demand Forecasting and Sample Arrival Rate: the demand for analytical work can be quantified as the timevarying volume of incoming samples. Data procured from the existing Laboratory Information Management System (LIMS) was processed to develop a data-driven Sample Generation Framework (SGF) that accurately emulates the arrival of samples, considering important factors such as the effect of seasonality, the actual inter-arrival times between consecutive samples and whether the samples arrive one at the time or grouped in a batch (Table 1). Table 1. Arrival process per sample type Sample Type

Yes

Update LIMS

BPMN was chosen as the graphical framework to model the analytical workflows considered in this study. Adopted across several industries, including the health sector (Rol´on (2008)), BPMN is becoming one of the process mapping tools of choice when it comes to bridging the gap between process design, implementation and monitoring. A comprehensive overview of BPMN can be found in Silver and Richard (2009).

Arrival Process

Quality Control Laboratory Scheduling Platform Scheduled Sample for Analysis

485

Update LIMS

Grouping

Weekday Pattern

Hourly Pattern

Workload Levels

Change of Line

Batch

Fast Analysis

Single

7 days/week

24 h/day

3

M on. - F ri.

24 h/day

Final Product

Batch

M on. - F ri. 8:00 - 17:00h

3 2

Intermediate

Single

7 days/week

24 h/day

1

In-process Control

Single

7 days/week

24 h/day

3

Miscellaneous

Batch

7 days/week 8:00 - 17:00h

Raw Materials

Batch

7 days/week 8:00 - 17:00h

3

Stability

Batch

1 day/week

2

8:00 - 17:00h

3

Additionally, the SGF should be capable of creating sets of samples with similar incidence of sample types, analytical

Fig. 1. Generic Analysis - BPMN Workflow 492

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techniques and methods to that of historical records. To this end, the relative frequency of each occurring combination of analytical tests performed on unique samples was computed, resulting in lookup tables for the probability of a given sample of type t being subjected to a specific mix of analytical tests, a, from amongst the set of possible combinations for that type, {At }: P (At = a | T = t). The assignment of methods to each technique was captured in analogous fashion, computing the relative frequency of each recorded method (m ∈ M ) per sample type (t) ↔ analytical test (a) pairing, yielding lookup tables for the probabilities P (M = m | {t, a}). To account for seasonality, the K-means clustering algorithm was used to group months of the year into sets of similar workload, quantified by the number of received samples. This analysis was performed for each sample type, considering three workload classes: low, moderate and high. The effect of seasonality on the incoming samples was found be prevalent across all types (Figure 2), implying that reducing the arrival rate of each type to a yearly summary measure would neglect important system dynamics.

Summary analysis of the inter-arrival times of each sample type revealed the arrival rate to be reasonably constant over the arrival windows listed in Table 1, when adjusted for the monthly workload level. Due to this property, the arrival process of samples was deemed suitable to be modelled as a Poisson process. Under the formalism detailed in Law (1991), the expected number of arrivals in any interval of length 1, λ, is referred to as the the rate of the process; the corresponding inter-arrival times A1 , A2 , ... are IID exponential random variables, with mean 1/λ. The arrival of samples grouped in batches was modelled as a compound Poisson process, again following the methodology presented by Law. Given that the interarrival times of successive batches were found to be IID exponential random variables, this variant of the Poisson process formalism relies on the sampling of a second distribution that maps the relative frequency of occurring batch sizes upon the arrival event. Having hypothesized on theoretical and empirical grounds that the arrival of samples follows a Poisson process with exponentially distributed inter-arrival times, the rate λ of each process (per type ↔ monthly workload level pairing) was estimated using the maximum likelihood estimator,

as described in Law (1991). The ”quality” of the fitted parameters was evaluated by means of two heuristic procedures: Density-Histogram and Q−Q & P −P probability plots. Detailed results for Change of Line (batch arrival) samples are presented in Figure 3. An illustration of the underlying logic process of the SGF is presented in Figure 4. Under this representation, λk {l, m, h} denotes the arrival rate of sample type k for months of low, moderate or high workload. This notation is extensible to the batch size Bk . For illustrative purposes, under Figure 4, sample types {1, n} arrive as single entities, whereas samples of type i arrive grouped in batches. Analyst Staff: An appropriate number of analysts, possessing the required GMP training, must be allocated to meet the time-varying amount of incoming samples. Demand forecasting conducted by laboratory managers tends to be inaccurate, resulting in either under or overstaffed teams. Both scenarios have negative repercussions, contributing to longer sample time-in-system (understaffing) or higher scheduled utilization of human resources (overstaffing). The three work-shifts in place at the CDMO and therefore considered in this study are detailed in Table 2. The field Teams refers to the number of distinct analyst teams operating under each regime, that alternate to comply with the mandatory resting periods between shifts. Table 2. Analyst work-shifts variants Work-shift

Weekdays

Hours

#1

daily

#2

M on. − F ri.

#3

M on. − F ri.

08:00h 20:00h 08:00h 17:00h 08:00h

-

Teams

20:00h 08:00h 17:00h 24:00h 17:00h

Q−Q plot

● ●● ●●● ●●●● ●● ●● ●● ●● ●● ●●● ●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0 20000

40000

60000

80000

100000

120000

140000

0

Month-1 Month-2 Month-3 Month-4 Month-5 Month-6 Month-7 Month-8 Month-9 Month-10 Month-11Month-12 Month Monthly Workload

Low

Moderate

0

20000

40000

60000

80000

Fitted Distribution 100000

120000

Inter−arrival Times [time units]

140000

1.0 0.8 0.6 0.4

●

Empirical probabilities

●

●● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ●●● ● ● ● ●●●● ●● ● ● ● ●● ●● ●●● ● ●●● ●●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ●

0.2

0.4 0.2 0.0

CL

●

100000

150000

P−P plot ●

● ● ●

0.0

CDF

0.6

0.8

FP

●●

Theoretical quantiles

Empirical and theoretical CDFs ●● ● ● ●●● ●● ●● ●● ●● ● ● ●●● ●● ●● ● ●●● ● ● ● ● ● ● ●●● ● ●● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

50000

Inter−arrival Times [time units]

IN

●

●

80000

Empirical quantiles

120000

● ●

● ●

40000

4e−05 Density

2e−05 1e−05 0

IP

1.0

Sample Type

0e+00

MS

●

Fitted Distribution

3e−05

TE

FA

2 n.a.

Analytical Equipment: Analytical equipment are fundamental resources in QC laboratories. Drawing a parallel between classic manufacturing systems theory and QCL operations, a strong duality is discernible amidst job shop machines (Pinedo (2005)) and analytical equipment: each device serves its own designated purpose, in the form of the analytical test it was designed to perform; additionally, similar equipment tend to be grouped according to the specific analysis they execute. Histogram and theoretical densities

RM

4

0.0

0.2

● ●● ●● ● ● ● ● ● ●● ● ● ● ●● ●●● ●● ●● ●● ● ● ● ● ● ● ● ●● ●● ●● ●● ● ●● ●● ● ● ●● ●● ● ● ● ●● ●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ●● ● ● ● ●●● ●● ● ●●● ● ● ● ●● ● ● ● ●● ●●● ● ●● ●● ● ●● ●● ●● ● ● ● ●●●● ●●● ● ●● ● ● ● ● ● ●●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ●● ●● ● ● ● ●●● ●

0.4

0.6

0.8

1.0

Theoretical probabilities

High

Fig. 3. COL samples,high monthly workload: density histogram,

Fig. 2. Monthly workload levels, per sample type

Q − Q & P − P plots

493

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the maximum number allowed, the sample is allocated to that equipment; if no match is found, the scope of the search algorithm is widened to available but invalidated equipment, i.e., equipment where the validation routine is yet to be performed. If no such equipment is found, the incoming sample is retained at the equipment group buffer, awaiting a change of state that enables it to be assigned.

Simulation Clock Source

𝜆𝜆1

Arrival Triggered

Look-up Analytical Tests

Look-up Analytical Methods

Assign Sample Properties

Arrival Triggered

Look-up Batch size

Look-up Analytical Tests

Look-up Analytical Methods

Look-up Analytical Tests

Look-up Analytical Methods

Assign Sample Properties

Sample Type 1

⁞

Source

𝜆𝜆𝑖𝑖

Assign Sample Properties

Two sample priority levels were considered: high, awarded to IPC samples, and regular, attributed to the remaining types; this binary decision variable was used as the primary entity sequencing rule. As for the allocation of analysts to specific samples, two alternative GM frameworks were compared: a structured policy, replicating the breakdown of sample types per QC branch currently in place (Table 3) and a free-for-all paradigm, under which the entire analyst staff present in the laboratory at a given time was granted permission to process every sample, regardless of its type.

Sample Type i

⁞

Source

𝜆𝜆𝑛𝑛

Arrival Triggered

Sample Type n

Look-up Tables Type i

Look-up Tables Type 1

𝜆𝜆1 {l,m,h} Tests

𝜆𝜆𝑖𝑖 {l,m,h} Tests

Methods

Look-up Tables Type n

𝜆𝜆𝑛𝑛 {l,m,h}

𝐵𝐵𝑖𝑖 {l,m,h}

Methods

Tests

487

Methods

Fig. 4. High-level Sample Generator Framework representation

Table 3. Sample types processed per QC branch

3.3 Model Framework Overview

QC Branch

The QCL simulation model framework proposed by Costigliola et al. (2017) was adapted and expanded to fulfil the requirements of this work. Data concerning the processing times of sample preparation, equipment setup, analysis runtime and data processing activities gathered by the author was retrieved and used this study. The scope of the model covers the entire sample flow within the laboratory, across 3 stages: (1) arrival (event triggered by the SGF), (2) allocation to an equipment of the appropriate variant required to perform the analytical test, according to the scheduling policy implemented at equipment group level and (3) the actual analytical workflow, consisting of the steps detailed in Figure 1. The high-level model flowchart implemented in SIMIO (ver. 8.139) is depicted in Figure 5. Sample sequencing and allocation policies were specified at equipment group level. Given its relevance, a custom heuristic rule focused on reducing the impact of system suitability on the sample’s time in system was implemented: upon the arrival of a sample, the equipment pool is scanned for devices whose last validated method matches that of the sample to be scheduled and, provided that such equipment exists and its queue contains less samples than

Branch Branch Branch Branch

A B C S

Processed Sample types Change of Line, IP C, Intermediates Raw M aterials, F inal P roduct, F ast Analysis, M isc. Raw M aterials, F inal P roduct, F ast Analysis, M isc. Stability

3.4 Model Performance Metrics To assess the behaviour of the model and estimate the realworld performance of the new laboratory under alternative GMs, key QCL performance metrics (listed below) were compared between the devised scenarios. Time in System (TiS): Total time taken to process a given sample, form the moment of its arrival until the analysis and subsequent data processing has finished. Throughput rate: A measure of the capacity of the QCL to process incoming samples; computed as the ratio between processed and total number of incoming samples. Equipment usage rate: A measure of the fraction of time a given equipment spent performing active work, computed over the total simulated period. Analyst scheduled utilization: A measure of the fraction of time that the analysts spend working, calculated over the corresponding total shift-time for each employee.

Sample Generator Framework COL

FA

FP

IN

IPC

RM

Misc.

4. SIMULATION STUDY

Stability

Validation of input parameters was performed by comparing the number of incoming samples generated by the SGF with historical data of the previous year. Results presented in Figure 6 were logged over twenty year-long simulation runs, where the upper and lower bounds of one standard deviation of the mean are also presented to convey the extent of variability between simulations (axis tick marks were removed to conceal the actual number). This data, coupled with the goodness-of-fit tests, attests the decision of modelling the arrival of samples as a Poisson process, conveying that the SGF is capable of consistently creating accurate volumes of incoming samples, providing a solid foundation for simulation input data.

Analytical Test?

Buffer KF

Buffer GC

Buffer HPLC

Buffer PSA

Buffer XRPD

Group Scheduling Node

Group Scheduling Node

Group Scheduling Node

Group Scheduling Node

Group Scheduling Node

Group Scheduling Node

GC_1

HPLC_1

HPLC_n

PSA_n

XRPD_1

⁞

KF_n

PSA_1

⁞

⁞

KF_1

GC_n

⁞

DSC_n

⁞

⁞

DSC_1

Buffer DSC

XRPD_n

Allocation Policy

⁞

Analyst_1

Analyst_n

Analyst Staff

A scenario-based approach was devised by benchmarking a set of six alternative GMs, resulting from different

Fig. 5. High-level QCL simulation model flowchart 494

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therefore, staff of these two branches was predominantly assigned to work-shift #2. Lastly, given its relatively lower priority, analysts performing stability work were appointed only to work-shift #3. Given that the members of the analyst staff considered in this study possess the same GMP qualifications, free-for-all can be readily adopted.

Stability

Sample Type

Raw Materials

In-process Control

Intermediates

Final Product

Fast Analysis

Mean relative time-savings achieved under free-for-all per analyst workforce tier are presented in Table 5, where the baseline structured values are omitted for comparison.

Change of Line

Actual Data

Simulation Input Data

Table 5. Mean relative difference in TiS between free-

Fig. 6. SGF incoming samples: simulation input data validation

for-all and structured GMs

configurations of two model parameters: (1) governance policy (structured vs. free-for-all ) and (2) number of analysts and breakdown per work-shifts. The results here presented stem from simulation runs spanning a period of three months, modelled as a sequence of low-high-high workload levels across all sample types.

Sample Type Σ Analysts CoL

Time in System & Analyst Scheduled Utilization

3

50

HPLC KF 100

PSA

4

+3, 8% −28, 7%−20, 5%−37, 9%−27, 6%−29, 5%−34, 0%−74, 6% −9, 5% −18, 9%−12, 7%−36, 2%−36, 9%−33, 5%−28, 4%−77, 5%

60

−12, 6%−22, 9%−15, 4%−36, 3%−40, 3%−35, 5%−28, 5%−79, 8%

QC Branch G.M.

1

2

1

GM# 3 GM# 6

60

GM# 3 GM# 6

GM# 2 GM# 5

52

GM# 2 GM# 5

44

GM# 1 GM# 4

44

GM# 1 GM# 4

0.0

2.5

5.0

7.5

10.0

0.0

Number of Analysts

60

60

52

GM# 2 GM# 5

52

GM# 2 GM# 5

44

GM# 1 GM# 4

44

0

3

2.5

5.0

6

9

GM# 3 GM# 6

60

GM# 3 GM# 6

52

GM# 2 GM# 5

52

GM# 2 GM# 5

44

GM# 1 GM# 4

44

GM# 1 GM# 4

5.0

7.5

10.0

2.5

5.0

0.0

2.5

5.0

Raw Materials 60

60

GM# 3 GM# 6

52

GM# 2 GM# 5

52

GM# 2 GM# 5

44

GM# 1 GM# 4

44 2.5

5.0

10.0

6

7.5

7.5

Stability

GM# 3 GM# 6

0.0

7.5

B

C

S

#1

6 (67.4%)

1 (74.7%)

1 (65.5%)

n.a.

#2

n.a.

2 (79.7%)

2 (65.0%)

n.a.

#3

n.a.

n.a.

n.a.

4 (57.0%)

#1

6 (66.4%)

2 (58.0%)

2 (38.6%)

n.a.

#2

n.a.

2 (69.2%)

2 (44.5%)

n.a.

#3

n.a.

n.a.

n.a.

4 (57.0%)

#1

6 (66.1%)

2 (45.0%)

2 (23.3%)

n.a.

#2

n.a.

4 (57.4%)

4 (19.1%)

n.a.

#3

n.a.

n.a.

n.a.

4 (59.0%)

Σ Analysts 44

52

60

Work-Shift

Free-for-All

#1

8 (65.2%)

#2

n.a.

#3

12 (68.8%)

#1

10 (53.5%)

#2

n.a.

#3

12 (63.6%)

#1

12 (45.3%)

#2

n.a.

#3

12 (62.4%)

Σ Analysts 44

52

60

A summary list of significant remarks is presented below:

10.0

Misc.

60

2.5

4

GM# 1 GM# 4

0.0

In-process Control

0.0

G.M.

Intermediates GM# 3 GM# 6

A

branch/work-shift (scheduled utilization %)

5

Final Product GM# 3 GM# 6

WorkShift

Table 7. Free-for-All GMs - analyst breakdown per

Fast Analysis

52

RM Stability

branch/work-shift (scheduled utilization %)

The assignment of analysts to work-shifts (Table 2) took into account the arrival mode of each sample type and the types processed by each QC branch. Given that the samples allocated to branch A arrive continuously over 24 hours, analysts assigned to this branch should operate under work-shift #1. The type of work done by branches B and C does not require constant presence of analysts; 60

Misc.

52

3

Change of Line

IPC

Table 6. Structured GMs - analyst breakdown per

XRPD

12

Inter.

44

Table 4. Planned equipment breakdown GC

FP

Allocation of analysts to work-shifts under each GM, along with the average staff scheduled utilization, is detailed in Tables 6 (structured GMs) and 7 (free-for-all GMs). The field Σ Analysts results from the rotating teams.

The average TiS registered under the six GMs is presented in Figure 7. To allow for the effect of the governance policy to be assessed independently of other parameters, the equipment group scheduling rule was set as First in First Out for all six scenarios; moreover, the maximum equipment queue size was limited to two samples and number of available equipment set as the currently planned in the rolling blueprint of the lab (Table 4). The impact of scaling the number of analysts was assessed by comparing three tiers of employed staff: 44, 52 and 60.

DSC

FA

7.5

10.0

GM# 1 GM# 4

0

20

Time in System [arbitrary units]

Governance Policy

Free-for-all

Structured

Fig. 7. GM’s mean Time in System, per sample type

40

10.0

• The two-tier priority policy results in IPC samples having the shortest TiS, as requested by project stakeholders. Under free-for-all all analysts prioritize this type of work which, given the ties of IPC with manufacturing, will shorten the time it takes to complete a batch. • The biggest reduction in TiS occurs in stability samples. Given the low priority of this type of work, it does not warrant a high number of dedicated analysts when a structured policy is considered. Under free-for-all, provided that no higher priority samples are pending, analysts will leverage the opportunity to process stability 495

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samples, reducing the TiS of this sample type in around 80% and thus fulfilling another requisite expressed by project stakeholders. • With the exception of GM #1, the considered GMs registered analyst utilization rates under 70%, as required by project stakeholders. For the same volume of samples, increasing the number of analysts allows for lower TiS to be achieved, while reducing the utilization of human resources. This is understandable since only part of analysis workflow requires the presence of the analyst. • Crucially, for the same number of analysts and available equipment, nearly every sample type is processed faster under free-for-all ; The potential time-savings that can be achieved by transitioning to a free-for-all policy demonstrate that the performance of the laboratory can be improved through an organizational rearrangement, without the need to procure additional resources. Equipment Usage Rate Detailed equipment usage rate statistics for the six GMs are presented in Table 8, along with the highest number of concurrent equipment in use during one simulation run. Table 8. Equipment usage rate % (maximum number of concurrent equipment in use)

DSC

GC

HPLC

KF

PSA

XRPD

1

38.7% (3) 38.6% (34) 46.8% (57) 40.7% (4) 32.4% (12) 11.3% (1)

2

37.3% (3) 38.0% (35) 46.5% (56) 37.6% (4) 32.5% (12) 10.0% (1)

3

39.6% (3) 38.0% (34) 46.2% (57) 37.4% (4) 32.9% (12) 10.3% (1)

4

34.8% (3) 38.1% (35) 45.9% (54) 24.1% (4) 31.6% (12) 11.3% (1)

5

33.9% (3) 37.4% (35) 45.4% (53) 19.5% (4) 31.3% (12) 10.0% (1)

6

33.6% (3) 37.4% (35) 45.9% (51) 18.0% (4) 31.0% (12) 11.5% (1)

Alternative Governance Models - the guidelines according to which the laboratory operates, covering topics such as analyst staff work schedules, analytical samples’ priority levels and allocation of certain equipment to specific tasks - were benchmarked based on multi-criteria objectives, such as minimizing the sample time in system while ensuring that the analysts’ scheduled utilization level remains within specific intervals. The factor with highest impact was found to be the organizational policy; crucially, for the same allocated resources, free-for-all GMs resulted in faster sample processing times. The time-savings when compared to a structured policy are substantial, amounting to 40% in the case of IPC samples, and 80% for stability work. Concerning the planned number of equipment to be installed at the new facility, the predicted capacity of HPLCs and GCs was deemed capable of handling the predicted volume of samples while providing ample room for future demand. Increasing the size of the analyst staff also contributed to lower sample TiS, but the effect was not as pronounced as the shift resulting from changing the organizational policy. A touchstone estimate as to how the analyst staff should be allocated to cope with the demand for analytical work was provided, resulting in analyst utilization levels in the interval of 50% to 70%, as requested by project stakeholders. Having set the baseline performance metrics for the laboratory under design, the simulation model can serve as the basis to optimize the dynamic (re)scheduling of the analytical work using meta-heuristics. This optimization will consist of reallocation of both staff and equipment satisfying better high priority samples.

Equipment G.M.

489

REFERENCES

HPLCs and GCs: At most, 57 out of 100 (35 out of 50) units were used simultaneously, implying that the planned capacity is overestimated. A smaller number of units can be installed initially and eventually increased over time. DSCs, KFs and PSAs: Lower usage rates was recorded under free-for-all governance, with this effect being more pronounced for equipment with smaller planned pool size. Since analysts must interact with the equipment at some stages of the analysis, having more staff that can process a given sample reduces the time an equipment spends waiting to be tended by an analyst, thus increasing the time it is available to be used. XRPD: The single X-Ray is deemed sufficient to cope with the volume of samples requiring this type of test. Sample Throughput All six GM variants achieved a throughput rate in excess of 98%; this implies that the laboratory was able to cope with the sequence of low-high-high monthly workloads without accumulating work-in-process at the end of the simulation run. The residual corresponds to the samples that were being processed when the simulation was halted. 5. CONCLUSION The simulation study presented in this paper contributed to estimate tangible key performance metrics of the GMP QCL under design that would otherwise be unavailable. 496

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