4 labauto optimize process flow (1)

8/2/2019 4 LabAuto Optimize Process Flow (1)

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Lab Automation

Impact on Workflow


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Workflow

The ultimate goal of lab automation - improvethe productivity of the laboratory and theenterprise as a whole

Focus on the parts of the enterprise where it canhave the most impact

Process flow of an organization must be studied

Points where automation have a measurable

impact must be identified Requires enterprise-level view of the organization

and the use of process evaluation techniques


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Process Optimization Approach

1) The Lean philosophy

http://www.labmanager.com/?articles.view/articleNo/3429/

Taken from Lean Manufacturing2) Six Sigma

Six Sigma combined with Lean approach to

yield Lean Six Sigmahttp://www.labautopedia.com/mw/index.php

/Lean_Sigma_in_the_lab
http://www.labmanager.com/?articles.view/articleNo/3429/http://www.labmanager.com/?articles.view/articleNo/3429/http://www.labautopedia.com/mw/index.php/Lean_Sigma_in_the_labhttp://www.labautopedia.com/mw/index.php/Lean_Sigma_in_the_labhttp://www.labautopedia.com/mw/index.php/Lean_Sigma_in_the_labhttp://www.labautopedia.com/mw/index.php/Lean_Sigma_in_the_labhttp://www.labmanager.com/?articles.view/articleNo/3429/http://www.labmanager.com/?articles.view/articleNo/3429/


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Lean Laboratory

The focus of a lean laboratory is to test samples in the most efficient way possiblein terms of cost, or speed, or both

Adoption of proper lean protocols require detailed analysis of process flow

Performance in todays laboratories tends to be negatively affected by issues

which include:

Volatile incoming workload - low productivity during troughs and/or poor lead-time performance during peaks

Too much WIP (Work in Progress) - many samples are partially tested but few are

fully completed minimize WIP

Long and variable lead times non optimum queing and scheduling

Ineffective fast-track systems too much priorities!!

Lack of cross-skilling staff too specialized

Muda, Mura, Muri: Lean laboratory pitfalls waste reduction (fluctuations in

performance increase waste. This is because equipment, workers, inventory and all

other elements required for production must always be prepared for peak

production)


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Lean Laboratory

The most successful lean laboratory projects tackle theleveling and standard work aspects first

Best foundation for a lean laboratory is analysis ofhistorical data and forecasts in order to adequatelymeet the desired target.

Lean in the laboratory is about more than a one-offproject.

Continually improve how the new process works mustbe maintained and performance must be managed andmonitored using appropriate KPIs (key performanceindicators).


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Leaning the Lab

a) Level the load and mix into the Lab using aleveling Q (queue)

b) Apply Pacemaker scheduling to the 1st processstep downstream from the Heijunka.

c) Create Standard Work and analysts roles thatensures smooth work flow after the Pacemaker(i.e. once a sample is launched into the Lab donot let it stop or Q again).

d) Short interval performance management (i.e.review performance daily and correct ifnecessary).


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https://www.beckmancoulter.com/wsrportal/

wsr/research-and-discovery/products-and-

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scheduling-software/index.htm
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Six Sigma

Six Sigma is a business management strategy, originallydeveloped by Motorola, USA in 1986 that is widely used inmany sectors of industry.

Six Sigma seeks to improve the quality of process outputs

by identifying and removing the causes of defects (errors)and minimizing variability in manufacturing and businessprocesses.

It uses a set of quality management methods, includingstatistical methods, and creates a special infrastructure ofpeople within the organization

A six sigma process is one in which 99.99966% of theproducts manufactured are statistically expected to be freeof defects (3.4 defects per million).


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Lean Sigma

Lean and Six Sigma are

distinct methods, each comes

with a different methodology.

The practitioner of Lean

Sigma must first decide which

is appropriate in any

circumstance.

As a general guide quality

improvements are tackled

using the Six Sigma

methodology while efforts toenhance speed or reduce

waste are tackled using the

Lean methodology.
http://www.labautopedia.com/mw/index.php/Image:LeanSigmaLabFig2.jpg


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Process Optimization

Regardless of the approach used, the key points to understand with regardto process flow and laboratory automation are:

The goal is enterprise optimization. Lab automation should beimplemented where it can have the best impact on theenterprise. Creating a highly automated laboratory that does not in someway impact the enterprise critical path is of questionable value. Thus

automation opportunities should be evaluated from both an enterpriseview and an individual laboratory view. The impact of technology on thebalance and flow of the entire enterprise process must be understood,planned and managed.

Automation can enable good science, not create it! Simply applying moretechnology horsepower will not create good science. If the process in

question is not scientifically sound or well understood, it should not beautomated. The ability to generate more data, faster cannot replace andmust integrate with the process of asking and answering the properscientific questions. If a process is scientifically sound, but sensitive tohuman errors, then automation may provide a solution.


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All about understanding process

flow!!!


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Tools To Help Process Flow Analysis

Capacity analysisIdentifies workflow bottlenecks and capacity-constrained resources

Static (time independent) or dynamic modeling (time dependent)

Gap analysis

Compares current output with a desired output and identify the means to bridge

the discrepancy or "gap". Takt time (from the German word taktzeit - clock cycle)

The maximum time allowed to produce a product in order to meet demand

Takt Time is defined as: T = Ta / Td

Where:

Ta = Time Available Time to Work (e.g.minutes of work / day) Td= Total demand (eg. product units produced / day)

T = TAKT Time (e.g. minutes of work / unit produced)

http://www.labautopedia.com/mw/index.php/Evaluation_of_bottlenecks_and_pr

ocess_flow
http://www.labautopedia.com/mw/index.php/Evaluation_of_bottlenecks_and_process_flowhttp://www.labautopedia.com/mw/index.php/Evaluation_of_bottlenecks_and_process_flowhttp://www.labautopedia.com/mw/index.php/Evaluation_of_bottlenecks_and_process_flowhttp://www.labautopedia.com/mw/index.php/Evaluation_of_bottlenecks_and_process_flow


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Lets look at one way to help

analyze process flow in the lab

Simulating your lab


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Dynamic Modeling

Allows experimentation in a virtual environment.

A simulation model may be investigated by overloading it with excess work to expose

bottlenecks, or running it on reduced resources to better understand impact of these

conditions on output and cycle time.

Careful simulation model analysis may suggest process improvements and the modification

of work plans, without the need to interrupt the current facility. When changes are

implemented they are done so with confidence and a minimum amount of risk.

Simulation models will help to engage stakeholders. Visualizing a model of a new facility or

proposed changes to an existing process will generate confidence that proposed

modifications will result in a successful outcome. Stakeholder support will be more easily

earned and maintained throughout the improvement project.

Simulation can be a key step in resource planning. With a simulation model it may be possible

to accurately gauge capacity, throughput and cycle times for complex systems, leading to thedeployment of a measured FTE/equipment resources that match desired outcomes.

Simulation models can be used to demonstrate how a system will cope with unexpected

demand. For example, models can be used to identify ways in which redundancy in other

systems are released when the process is stressed beyond typical operating capacity


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http://www.labautopedia.com/mw/images/LSLSimulationFig2.gifhttp://www.labautopedia.com/mw/index.php/Image:LSLSimulationFig1.gif


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We can use EXCEL to simulate

the queuing part.

EXCEL examples of simulating queues


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Simulation Software (Queue)

http://web2.uwindsor.ca/math/hlynka/q

soft.html

http://www.me.utexas.edu/~jensen/ORMM/computation/unit/que_add/index.h

tml

http://apps.business.ualberta.ca/aingolfsson/qtp/
http://web2.uwindsor.ca/math/hlynka/qsoft.htmlhttp://web2.uwindsor.ca/math/hlynka/qsoft.htmlhttp://www.me.utexas.edu/~jensen/ORMM/computation/unit/que_add/index.htmlhttp://www.me.utexas.edu/~jensen/ORMM/computation/unit/que_add/index.htmlhttp://www.me.utexas.edu/~jensen/ORMM/computation/unit/que_add/index.htmlhttp://apps.business.ualberta.ca/aingolfsson/qtp/http://apps.business.ualberta.ca/aingolfsson/qtp/http://apps.business.ualberta.ca/aingolfsson/qtp/http://apps.business.ualberta.ca/aingolfsson/qtp/http://www.me.utexas.edu/~jensen/ORMM/computation/unit/que_add/index.htmlhttp://www.me.utexas.edu/~jensen/ORMM/computation/unit/que_add/index.htmlhttp://www.me.utexas.edu/~jensen/ORMM/computation/unit/que_add/index.htmlhttp://web2.uwindsor.ca/math/hlynka/qsoft.htmlhttp://web2.uwindsor.ca/math/hlynka/qsoft.html


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Laboratories need scheduling. Samples with

analytical procedures are processed on

workbenches in parallel or sequential order. Here,

multiple samples and procedures compete for the

same devices. Through the use of specific conflictresolution strategies, resources can be shared, and

throughput can be optimized. Labs can be made

leaner.

http://www.labautopedia.com/mw/index.php/Concepts_for_Dynamic_Scheduling_in_the_Laboratory

SCHEDULING
http://www.labautopedia.com/mw/index.php/Concepts_for_Dynamic_Scheduling_in_the_Laboratoryhttp://www.labautopedia.com/mw/index.php/Concepts_for_Dynamic_Scheduling_in_the_Laboratoryhttp://www.labautopedia.com/mw/index.php/Concepts_for_Dynamic_Scheduling_in_the_Laboratoryhttp://www.labautopedia.com/mw/index.php/Concepts_for_Dynamic_Scheduling_in_the_Laboratory


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Scheduling

Simulation of lab would bring about a goodunderstanding of the process flow on theworkbench Lab Unit Operation (LUO)

Need to build a consistent framework of process Imagine a lab with limited resources multiple

samples with different procedures are processedin parallel, competing for the devices.

Need to optimize resources, need to strategize inorder to achieve targeted productivity -scheduling


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Why schedule?

Workflows in the laboratory range from high throughputscreening tests to quality control (QC) environments.

Scheduling algorithms required to determine the executionsequence for the various activities and use of resources(instruments etc).

Have to understand instruments, composition of activities,procedural elements of workflows, data recording andretrieval etc.

Scheduling algorithm. A scheduling algorithm is a

sequence of mathematical operations that assembles andoptimizes a working plan from the given activities andworkflows. It allocates processing time on devices. Inaddition, it considers any sample- or workflow-specificconstraints as well as global optimization criteria whenmaking placement decisions


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Activity

An element of a workflow defined by an end user. It is definedby its duration and constraints to other activities. It mayconsist of several working-plan activities.

An activity has predecessors triggered via time andconditional constraints.

An activity may also generate triggers for subsequentactivities

An activity can use a device, containers, consumables, anddurables

Manual activities may also be included in a workflow.

It is essential to allocate enough time execution of activities.

An activity-finished event generator (e.g., a pushbutton) canbe added to allow an operator to indicate the conclusion ofthe activity.


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Breakdown of activity duration and

relationship to other related

activities


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http://www.labautopedia.com/mw/index.php/Image:DynamicSchedulingFig2.png


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Activity

Activities need to be defined carefully. At a minimum,the following two alternatives may occur:

I. Activities are defined in the finest granularity. In thiscase, an activity always represents one command

sent to one device. In this case, an activity is identicalto a working-plan activity. This first case is calledworking-plan activities only.

II. Activity definition is in a higher granularity. Here, anactivity consists of several working-plan activities.

However, the semantics of these working-planactivities remains the same. This second case is calledworking-plan activities with fine structure


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The Gantt chart showing a sample

weighing step and a subsequent GC

content-of-active-ingredient


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Scheduling Scheduling algorithm calculates the timing in the working plan

Analyzing the timing of activities will allow scheduling which will optimizeresource usage, minimize idle time and optimize overall process.

Simulation helps!

Detailed description of work flow/working plan must be obtained beforeattempting scheduling

Need to describe the workflow - Workflow description language take

into consideration control flow, data flow, material flow, workflowelements, maintenace, calibration, workflow scenario, exceptionhandling..

A software system, programmed by taking into consideration the elementsdiscussed above controls the scheduling.

The end user (e.g., chemist) does not need to know how to implement

and code them. She or he only needs to know their functionality andsemantics.

The actual implementation may be left to a system administrator or anautomation specialist, who usually is most familiar with the intrinsicfunctionality of each workbench.


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Scheduling

These concepts are usually bundled to a softwaresystem that accepts different samples withdifferent workflows.

To optimize the schedule, different scheduling

runs must be generated and compared. The result's quality correlates with the calculation

time. Longer computation implies higher quality,but scheduling runs that are too long is

counterproductive. They worsen optimization results and squander

available resource time.


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