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Agile L&D Transformation: A Game-Changer for Pharma

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In this insightful whitepaper, we will explore how technological disruption impacts the Pharmaceutical Industry, the challenges and trends it faces, and why embracing agility and adaptability in learning strategies has become paramount. – PowerPoint PPT presentation

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Title: Agile L&D Transformation: A Game-Changer for Pharma


1
Navigate Talent disruption in Pharma with Agile
LD strategies
Conceptualized and Developed March 2023 This
document aims to provide an overview of How Agile
LD strategies can help companies navigate
technological disruption and design targeted and
cost-effective Reskilling and Upskilling
programs. Case studies of Pharma specific job
roles have been analyzed Copyright _at_2023 Draup.
All rights reserved
2
CONTENTS
Pages 3-6
  • This section covers
  • Technological disruption generating new skills
  • Impact of technological disruption on
  • the Pharmaceutical Industry
  • Disrupted Emerging Roles due to technological
    advancements
  • LD strategy becoming critical for productivity
    of Workforce
  • Technology Disruption Impact on the
    Pharmaceutical Industry the Need for LD
    Strategies

8-13
  • Building a Modern Workforce Impact Cost of LD
    in Pharmaceutical Industry for sample roles

3
Pharma job roles disrupted by Technology
Continuous technological advancement is
disrupting traditional Pharma roles, while
creating demand for New Age roles with emerging
skills
Draups analysis of job roles impacted/to be
impacted by New Age technologies (Non-exhaustive)
In-demand Technology In-demand Technology In-demand Technology In-demand Technology In-demand Technology Emerging Technology Emerging Technology Emerging Technology Emerging Technology
Technology Artificial Intelligence Robotics Automation Digital Health Technology Blockchain 3D Printing Gene Editing Nano- technology Quantum Computing Bioprinting
Application Drug Discovery Disease Diagnosis Clinical Trial Manufacturing Logistics Laboratory Telemedicine Mobile Health EHR1 Supply Chain Compliance Authentication Drug Delivery Tissue Eng.3 Drug Developing Precision Md.2 Gene Therapy Drug Discovery Drug Delivery Diagnostics Imaging Simulation Drug Discovery Supply Chain Organ Transplant Drug Testing2 Personalized Md
Disrupted Job Roles Data Entry Clerks Laboratory Technicians Traditional Sales Representatives Supply Chain Manger Quality Control Inspectors Genetic Counselors Biopsy Specialists Computational Chemist Manufacturing Technicians
Disrupted Job Roles Clinical Researchers Sterile Compounding Technicians Patient Monitoring Workers Regulators, Pharmacists Tool Mold Maker Drug Development Workers Production Workers Biostatistician Animal Researchers
Job roles Created Clinical Trial Designer Automation Specialist Digital Health Product Manager Blockchain Compliance Managers 3D Printing Engineers Gene Therapy Manufacturing Specialists Nanoparticle Engineer Quantum Algorithm Developer Tissue Engineer
Job roles Created ML Engineer/ Data Scientist Process Engineers Digital Marketing Specialist Blockchain Project Managers RD Specialist Bioinformaticians Biomedical Engineers Quantum Computing Experts Bio-fabrication Scientists
Disruption of sample job roles such as
Laboratory Technician and Data Scientist has
been analyzed further
Note 1 Electronic Health record, 2 Medicine, 3
Engineering. Above analysis is based on Draups
internal research, press releases, and DBS
publicly available data. The roles and
technologies will change with different
industries, In demand and Emerging job role are
not exhaustive. Source Draups Proprietary
Talent Database which tracks 4,500 job roles
4M career paths.
3
4
Disruptive innovation in Pharmaceuticals The
Pharma industry faces significant job
transformation due to the disruptive influence of
AI, robotics, and automation
Future technologies which will drive disruption
Current technologies disrupting the
Pharmaceutical industry
18 of current jobs to be replaced by 2030 due to
Robotics Automation2
250 Million Cost saving in pharmaceutical
industry by using Blockchain by 20303
Impact of Tech
Implementation of Tech in Pharma timeline
2030
Quantum Computing
2.4 Million New job to be created by 2025 due to
new Digital Health Technologies5
1.2 Million Jobs to be displaced in pharma by
2025 due to AI1
Gene Editing
Nano- technology
2.3 Billion Market size by 20254, suggesting
high user adoption
Timeline
2025
Bioprinting
AR VR
Digital Health Technology
Robotics Automation
3D Printing
Artifical Intelligence
2022
Blockchain
Trend
Bio Revolution Confluence of advance tech in
Pharma
Used AI to identify a new drug candidate for
idiopathic pulmonary fibrosis in just 46 days
Used robotics in drug development, reduced
screening time by 40 cost by 25.
Used blockchain- based platform, reduced time for
drug supply chain traceability by 90
Used 3D printing to reduce the prototyping time
from months to weeks reduced cost by 90.
Used remote monitoring, improved patient outcomes
in 80 of cases
Sample Case Studies
Bio revolution promises gene- therapies,
hyper-personalized medicines, genetic based
guidance on food, etc these all will be assisted
by advanced technology
Note The research is based on Draups internal
analysis. Draups ML model tracks 2M industry
reports, news articles, publications, and digital
intentions. Source 1. World Economic Form (WEF),
2. McKinsey, 3. MarketsandMarkets, 4.
ResearchAndMarkets, 5. Deloitte.
4
5
Rapid advances in Technology is disrupting the
Talent landscape. The skills gap in existing
workforce is resulting in
underutilization of the workforce
Technological disruption is creating demand for
New Age skills at an unprecedented rate
Companies are experiencing the impact of Skill
gaps across Job Roles
Employers' response to experiencing skill shortage
Skill complexity
N1,216
2030
43
87 of employers3 (Total of 1,216 companies)are
aware they are either already experiencing a
skills shortage or will experience one soon
Rising skill complexity leading to increase in
skill gap
By 2030, up to 14 of the global workforce1, will
need to acquire new skills to fill the skill gap
Skill expertise
2023
22
22
Timeline
Up to 800 Million jobs2 could be lost to
automation. 1/5 of the global workforce.
6
5
In next 2 years
In next 3-5 In next 6-10 None in next
years years 10 years
Now 2021
To overcome the impact of Tech disruption and to
bridge capability gaps LD has become critical
for organizations. LD strategies are explored in
detail in further slides
Technological Disruption
Low
High
5
Source Draup analysis 1,3. McKinsey 2. World
Economic Form (WEF). NoteN Total respondents.
Draup analyses 16 Million data attributes every
day to help global HR leaders solve their
challenges.
6
Agile and Proactive LD strategies can help
organizations fill skills gaps faster, saving
considerable costs in the longer run. LD leaders
can play a crucial role in navigating these
disruptions
  • LD Leaders are critical for increasing Workforce
    Productivity
  • Organizations believe that Agile
  • 94 LD is critical to fill the skill gap
  • keep the workforce relevant2
  • Conscious Reskilling
  • Assess Current Situation
  • Set SMART Objective
  • Develop a customized LD program
  • Deliver LD Program
  • Refine Evaluate
  • Continuous Upskilling
  • Identify Skill Gap
  • Define Learning Objective
  • Select Learning Methodology
  • Develop Targeted Learning

Economic Value of an Employee to the Organization
over Time with Traditional vs. Agile LD
Conscious Reskilling Reskilling employee to a
new role when the job role is not adding value
even with upskilling
53 Targeted LD can result in a 53 increase in
the economic value of employees to the
organization1
Economic value to the organization
Continuous upskilling Constant New Age skills
addition with every New skill emerging in the
market
Intermediate generic training for new skills
No positive economic value as the job role is
disrupted over time and skills become obsolete
Formal training post joining
Employee journey in the organization with Time
Organization is investing in the employee
Basic Training material for the job role
Onboarding New hire
Employee journey with traditional LD
Continuous Upskilling Journey
Conscious Reskilling Journey
6
Note SMART - Specific, Measurable, Achievable,
Relevant, and Time-bound. 1. Draups module
Signals has analyzed various Industry published
reports to understand the concepts of Hiring and
Reskilling in an Organization Source 1. ATD, 2.
Deloitte
7
CONTENTS
Pages 3-6
  • Technology Disruption Impact on the
    Pharmaceutical Industry the Need for LD
    Strategies
  • This section covers
  • Evolution of workflow of Laboratory
  • Technician
  • Cost analysis of Reskilling vs Hiring
  • Skill gap analysis for Reskilling to
    Bioinformatician role
  • Impact of new-age skills on workflow of Data
    Scientist
  • Cost analysis of Upskilling vs Hiring
  • Skill gap analysis for Upskilling to XAI
  • skillset

8-13
  • Building a Modern Workforce Impact Cost of LD
    in Pharmaceutical Industry for sample roles

8
Sample Upskilling case study of a sample job role
Data Scientist
Sample Reskilling case study of core job role
Laboratory Technician (1/3)
Disruption in the workflow of core job roles -
Laboratory Technicians workflows will become
increasingly digitalized as AI ML algorithms
drive greater efficiency productivity
Traditional Laboratory Technician Workflow
New-Age Laboratory Technician Workflow
Sample Preparation
Sample Preparation
1
1
Prepare samples for analysis using microscopes,
pipettes, and reagents.
Automated systems and microfluidics used for
sample preparation.
Instrumentation Data Collection
Instrumentation Data Collection
Reporting
Reporting
2
2 5
4
Conduct experiments using microscopes and
chemical tests
Communicate results with visual aids and
presentations.
Use advanced instrumentation such as HPLC and PCR
for data collection.
Use tools like Tableau to create interactive data
visualizations.
Data Processing Analysis
Data Interpretation and Visualization
Data Analysis
3
4
3
Manually analyze the results and create a report
to communicate the findings.
Utilize LIMS, ELNs, and automated data
collection, as well as AI and ML for analysis.
Use software tools like R and Tableau to
visualize and interpret data.
Note Draups analysis. Source The represented
data has been derived using Draups Proprietary
Talent Database, Draup tracks and analyse 4,500
job roles and 280M Job descriptions across
functions to understand the disruption in
workflow due to tech. advancements. Similar
analysis can be performed for any job role.
8
9
Sample Upskilling case study of a sample job role
Data Scientist
Sample Reskilling case study of core job role
Laboratory Technician (2/3)
Cost Analysis of Reskilling vs. Hiring -
Companies can save 2/3rd the cost of hiring a
new employee by Reskilling an existing
employee, which can also significantly improve
retention rate and efficiency Benefits of
Reskilling
Improves Retention
Improves Efficiency
Reduces Cost
69 of talent professionals believe Reskilling
can help improve diversity and inclusion (DI)2
Reskilling reduces attrition rate by providing
viable career paths to disrupted job roles to
in-demand ones
Cost of Reskilling an existing employee is 1/3
the cost of hiring a new employee with the same
skills1
Sample Reskilling case study
Cost analysis of Hiring Bioinformatician vs.
Reskilling Laboratory Technician to
Bioinformatician 0 20,000 40,000 60,000 80,000
1,00,0001,20,000
Laboratory Technician
Talent base pay
(Skills Addition)
114K
Total Cost saving on every FTE
Cost of hiring Bioinformatician
USD 30K
  • Data Analysis
  • Machine Learning
  • Statistics
  • Bioinformatics

Estimated team size
40 FTEs
74K
Cost of Reskilling Laboratory Technician
(Upskilling)
Total savings 1.2 Million
Bioinformatician
for company
/year
Base Pay
Non-Recurring cost
Salary Hike
Reskilling Cost
Note Non-Recurring Cost one-time expense
during the hiring process, includes advertising
costs, background check fees, travel expenses for
interviews, sign-on bonuses, relocation expenses,
etc. Analysis based on Draups insights from
customer engagement, industry blogs,
whitepapers. Draup analyses 16 Million data
attributes every day to help global HR leaders in
Planning, Hiring Upskilling their Future-Ready
Workforce. 1. WEF, 2. LinkedIn Survey
9
10
Sample Upskilling case study of a sample job role
Data Scientist
Sample Reskilling case study of core job role
Laboratory Technician (3/3)
Bridging the Skills Gap Draups Reskilling
Intelligence platform identifies the skills gap
by analzying 700 Million profiles and 280 Million
JDs. 4 Million career paths are analyzed to
suggest targeted LD modules Major skill gaps to
be filled to reskill adjacent roles towards
Bioinformatician role with in-demand emerging
skills
Sample adjacent Job Roles to Reskill 1. Programming, Database Skills 2. Data Analytics 3. ML/Statistical Skills 4. Laboratory Techniques 4. Laboratory Techniques 5. Applied Bioinformatics 5. Applied Bioinformatics 6. Genomics Transcriptomics 6. Genomics Transcriptomics 7. NCBI Ensembl 7. NCBI Ensembl
Laboratory Technician
Computational Chemist
Clinical Researcher
Pharmacist
Roles suitable for Reskilling Roles suitable for Reskilling Roles suitable for Reskilling Roles suitable for Reskilling Roles suitable for Reskilling Skill overlap Skill overlap High High Moderate Moderate Low
Real life Sample case study
Laboratory Technician
Bioinformatician
Courses Taken / Skills Addition
Bana Laboratory Technician, Aug 2021Mar 2022
Biolab, Amman, Jordan
Bana Bioinformatician, May 2022Present
Bionl.ai, Boston, USA
  • Advanced Data Analysis
  • Machine Learning
  • Statistics
  • Bioinformatics
  • Responsibilities-
  • Conduct experiments, maintain equipment, and
    record-keeping.
  • Collaborating with other laboratory staff and
    researchers to develop new testing methods and
    protocols
  • Responsibilities-
  • Developing and applying computational tools and
    techniques to analyze biological data.
  • Collaborating with researchers and other
    scientists to design experiments and develop data
    analysis strategies.

Note Draup performs complex assessments around
various other critical Reskilling parameters
between existing and desired roles to understand
the skill gap and match it with relevant learning
modules. Source Draup Reskilling/Upskilling
module which tracks 4M career paths, 300K
courses, and 30K skills is used to identify the
relevant job roles transitions. Draup talent
module which tracks 700M professionals is used
to identify relevant profile
10
11
Sample Upskilling case study of a sample job role
Data Scientist (1/3)
Sample Reskilling case study of core job role
Laboratory Technician
Impact of New-age skills Upskilling Data
Scientist with new skills allows organizations to
stay ahead of the curve, driving innovation and
efficiency (up to 80) as the field evolves
Current workload skillset of Data Scientist
Possible impact of upskilling Data Scientist to
have age skills
New age skills in Data Science
Impact on Productivity
Impact on Capability
Impact on Efficiency
  • Improved accuracy of ML
  • models
  • Democratized ML
  • More projects handled
  • simultaneously
  • Increased efficiency

- Reduced time for hyperparameter tuning by 80
AutoML
Data Scientist
  • Improved model transparency interpretability
  • Better compliance
  • Reduced time to identify
  • model issues by 60
  • Improved compliance rate by 70

Explainable AI
- Reduced time to identify model errors and
issues
  • Analyze complex data to
  • identify patterns trends
  • Develop insights to support business decisions
  • Communicate findings to stakeholders ensure
    the accuracy quality of data
  • Reduced time to build NLP models by 70
  • Increased number of NLP projects by 60
  • Access to pre-trained models and libraries
  • Improved NLP capabilities

Hugging Face
- Reduced time and effort for building NLP models
  • Programming Python, R, SQL
  • Statistical Analysis SPSS, SAS Machine Learning
  • Scalable and distributed data processing
  • Easy integration with other big data tools
  • Reduced effort for data preprocessing
  • Improved efficiency for big data processing
  • Reduced processing time for big data by 50
  • Reduced data preprocessing time by 40

Apache Beam
Note Skillset and Draup leveraged its database
of 1M digital intentions for employers across
multiple industries, extracted from sources such
as news articles, job descriptions, video
interviews, journals to analyse the digital
strategies and use cases of peer companies.
11
12
Sample Upskilling case study of a sample job role
Data Scientist (2/3)
Sample Reskilling case study of core job role
Laboratory Technician
Cost Analysis - Upskilling vs. Hiring While
investing in upskilling programs may seem costly,
the benefits outweigh the expenses i.e., up to
15 annual cost savings Skillset evolution in
Data Science Cost analysis of Upskilling vs.
Hiring talent for Emerging/In-demand
skillsets Benefits of Upskilling over time
Overall cost saved by company
34K
Boosts Productivity Firms that provided 12 upskil
ling training to their employees had a 12
higher productivity.1
  • AutoML
  • Capability automate repetitive tasks and
    accelerate the drug discovery process

180K 160K 140K 120K 100K 80K 60K 40K 20K K
160K
One-time cost saved on FTE
19K
126K
14K
44..66K 6.8K
Talent base pay
Improves Efficiency Upskilling can help employee
identify solve problems quickly improve
efficiency
Updated base pay
  • Explainable AI (XAI)
  • Capability improve the interpretability of ML
    models used in drug discovery

141K
Base pay saved on every FTE
114K
121K
20K
Promotes Innovation Upskilling can improve
efficiency by promoting innovation and creativity.
  • Hugging Face
  • Capability analyze large volumes of medical
    literature to extract information

Cost of hiring new talent with XAI skillset
Cost of upskilling Data Scientist for XAI skillset
Base Pay
Non-Recurring cost
Incentive
Upskilling Cost
Note Non-Recurring Cost one-time expense
during the hiring process, includes advertising
costs, background check fees, travel expenses for
interviews, sign-on bonuses, relocation expenses,
etc. Analysis based on Draups insights from
customer engagement, industry blogs,
whitepapers. Draup analyses 16 Million data
attributes every day to help global HR leaders in
Planning, Hiring Reskilling their Future-Ready
Workforce. Source 1. PwC Survey
12
13
Sample Upskilling case study of a sample job role
Data Scientist (3/3)
Sample Reskilling case study of core job role
Laboratory Technician
Bridging the Skills Gap Skill gap analysis using
Reskilling Intelligence can help design targeted
upskilling programs for Data Scientists to
acquire specific skills Major skill gaps to be
filled to upskill adjacent roles with in-demand
emerging skill of Explainable AI (XAI)
Sample adjacent Job Roles to Upskill 1. Programming 2. ML Architecture, ML Algorithms 3. Statistical Modeling 4. Data Visualization 5. Libraries Frameworks 6. LIME Shapley Values 7. Regulatory Consideration
Data Scientist
ML Engineer
Statistician
Skill Overlap High Moderate Low
Roles suitable for Upskilling
Real life Sample case study
Data Scientist
XAI Data Scientist
Courses Taken / Skills Addition
Sugandh XAI Working Group Lead, Sep
2022Present Stockholm, Sweden
Sugandh Data Team, Sep 2018Present Stockholm,
Sweden
  • Interpretable ML
  • LIME Tutorial
  • Explainable ML
  • Responsible AI
  • Model Transparency Regulatory Compliance
  • Responsibilities-
  • Developing and implementing data analytics
    strategies to support business objectives.
    Identifying new data sources and defining data
    collection strategies
  • Applying advanced statistical and machine
    learning techniques to
  • extract insights and patterns from data
  • Responsibilities-
  • Ensuring model transparency and interpretability
  • Addressing issues of bias and fairness
  • Ensuring model accuracy and reliability
  • Promoting responsible and ethical AI development

Note Draup performs complex assessments around
various other critical Upskilling parameters
between existing and desired roles to understand
the skill gap and match it with relevant learning
modules. Source Draup Reskilling/Upskilling
module which tracks 4M career paths, 300K
courses, and 30K skills is used to identify the
relevant job roles transitions. Draup talent
module which tracks 700M professionals is used
to identify relevant profile
13
14
About Draup
15
Draup leverages Machine learning models to curate
Reskilling insights provided in the report.
Similar analysis can be performed for 4,500 job
roles and any Business function.
Draup Capabilities Data Assets
EMPOWERS DECISION MAKING IN
Reskilling
CAREER PATH PREDICTOR
Career Path Development
ROLES SKILLS TAXONOMY
DIVERSITY INTELLIGENCE
Strategic Workforce Planning
UNIVERSITY INTELLIGENCE
Talent Acquisition
Explore Diverse Job Roles, Locations and
Ecosystem Insights
DIGITAL IMPACT ON TRADITIONAL ROLES
Peer Intelligence
TALENT INTELLIGENCE
PEER BENCHMARKING
Diversity Inclusion
Global Locations Footprint
LOCATION INTELLIGENCE
COURSES/ CERTIFICATIONS
Digital Transformation
and diverse other use cases
15
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700M
PROFESSIONALS
Draup for Talent Draup analyses 16 Million data
attributes every day to help global HR leaders in
Planning, Hiring Reskilling their Future-Ready
Workforce
4,500 JOB ROLES
33 INDUSTRIES
500,000 PEER GROUP COMPANIES
280M JOB DESCRIPTIONS
300,000 COURSES
2,500 LOCATIONS
14,000 UNIVERSITIES
30,000 SKILLS
4M CAREER PATHS ANALYZED
47,000 DIGITAL TOOLS PLATFORMS
175,000 UNIVERSITY PROFESSORS
75 MACHINE LEARNING MODELS DEYELOPED
16M DAILY DATA POINTS ANALYZED
100 LABOR STATISTIC DATABASE
1,000 CUSTOM TALENT REPORTS
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www.draup.com HOUSTON I BANGALORE 2023 DRAUP.
All Rights Reserved.
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