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

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. To know more, visit: https://draup.com/talent/whitepapers/agile-ld-transformation-a-game-changer-for-pharma/

Transcript

1. Copyright @2023 Draup. All rights reserved Navigate Talent disruption in Pharma with Agile L&D strategies Conceptualized and Developed: March – 2023 This document aims to provide an overview of How Agile L&D 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
2. CONTENTS This section covers: • Technological disruption generating new skills • Impact of technological disruption on the Pharmaceutical Industry • Disrupted & Emerging Roles due to technological advancements • L&D strategy becoming critical for productivity of Workforce • Technology Disruption: Impact on the Pharmaceutical Industry & the Need for L&D Strategies 3-6 Pages • Building a Modern Workforce: Impact & Cost of L&D in Pharmaceutical Industry for sample roles 8-13
3. 3 Draup’s analysis of job roles impacted/to be impacted by New Age technologies (Non-exhaustive) Note: 1 Electronic Health record, 2 Medicine, 3 Engineering. Above analysis is based on Draup’s 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: Draup’s Proprietary Talent Database which tracks 4,500+ job roles & 4M+ career paths. In-demand 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 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 ML Engineer/ Data Scientist Process Engineers Digital Marketing Specialist Blockchain Project Managers R&D Specialist Bioinformaticians Biomedical Engineers Quantum Computing Experts Bio-fabrication Scientists Pharma job roles disrupted by Technology: Continuous technological advancement is disrupting traditional Pharma roles, while creating demand for New Age roles with emerging skills Disruption of sample job roles such as ‘Laboratory Technician’ and ‘Data Scientist’ has been analyzed further
4. 4 ~250 Million Cost saving in pharmaceutical industry by using Blockchain by 20303 ~2.4 Million New job to be created by 2025 due to new Digital Health Technologies5 Robotics & Automation Digital Health Technology Blockchain 3D Printing Artifical Intelligence ~1.2 Million Jobs to be displaced in pharma by 2025 due to AI1 2025 2030 18% of current jobs to be replaced by 2030 due to Robotics & Automation2 ~2.3 Billion Market size by 20254, suggesting high user adoption 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 Note: The research is based on Draup’s internal analysis. Draup’s 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. Disruptive innovation in Pharmaceuticals: The Pharma industry faces significant job transformation due to the disruptive influence of AI, robotics, and automation AR & VR Bioprinting Gene Editing Nano- technology Quantum Computing Bio Revolution & Confluence of advance tech in Pharma Bio revolution promises gene- therapies, hyper-personalized medicines, genetic based guidance on food, etc; these all will be assisted by advanced technology Timeline Impact of Tech Implementation of Tech in Pharma timeline 2022 Current technologies disrupting the Pharmaceutical industry Future technologies which will drive disruption Sample Case Studies Trend
5. 5 Source: Draup 5 43% 22% 22% 5% 6% Now In next 2 years In next 3-5 years In next 6-10 years None in next 10 years Rapid advances in Technology is disrupting the Talent landscape. The skills gap in existing workforce is resulting in underutilization of the workforce 2030 2023 Low High Technological Disruption Technological disruption is creating demand for New Age skills at an unprecedented rate Source: Draup analysis 1,3. McKinsey 2. World Economic Form (WEF). Note:*N= Total respondents. Draup analyses 16+ Million data attributes every day to help global HR leaders solve their challenges. Skill expertise Skill complexity Rising skill complexity leading to increase in skill gap 87% of employers3 (Total of 1,216 companies)are aware they are either already experiencing a skills shortage or will experience one soon By 2030, up to 14% of the global workforce1, will need to acquire new skills to fill the skill gap Companies are experiencing the impact of Skill gaps across Job Roles Up to 800 Million jobs2 could be lost to automation. ~1/5 of the global workforce. To overcome the impact of Tech disruption and to bridge capability gaps; L&D has become critical for organizations. L&D strategies are explored in detail in further slides Timeline Employers' response to experiencing skill shortage 2021 N*=1,216
6. 6 Economic Value of an Employee to the Organization over Time with Traditional vs. Agile L&D Agile and Proactive L&D strategies can help organizations fill skills gaps faster, saving considerable costs in the longer run. L&D leaders can play a crucial role in navigating these disruptions Note: *SMART - Specific, Measurable, Achievable, Relevant, and Time-bound. 1. Draup’s module Signals has analyzed various Industry published reports to understand the concepts of Hiring and Reskilling in an Organization Source: 1. ATD, 2. Deloitte Employee journey in the organization with Time Economic value to the organization New hire Onboarding Basic Training material for the job role Formal training post joining Intermediate generic training for new skills No positive economic value as the job role is disrupted over time and skills become obsolete Targeted L&D can result in a 53% increase in the economic value of employees to the organization1 53% Continuous upskilling: Constant New Age skills addition with every New skill emerging in the market Conscious Reskilling: Reskilling employee to a new role when the job role is not adding value even with upskilling Organizations believe that Agile L&D is critical to fill the skill gap & keep the workforce relevant2 94% L&D Leaders are critical for increasing Workforce Productivity Conscious Reskilling 1. Assess Current Situation 2. Set SMART* Objective 3. Develop a customized L&D program 4. Deliver L&D Program 5. Refine & Evaluate Continuous Upskilling 1. Identify Skill Gap 2. Define Learning Objective 3. Select Learning Methodology 4. Develop Targeted Learning Content 5. Evaluate Effectiveness Organization is investing in the employee Employee journey with traditional L&D Continuous Upskilling Journey Conscious Reskilling Journey
7. CONTENTS 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 • Technology Disruption: Impact on the Pharmaceutical Industry & the Need for L&D Strategies 3-6 Pages • Building a Modern Workforce: Impact & Cost of L&D in Pharmaceutical Industry for sample roles 8-13
8. 8 Source: Draup 8 Traditional Laboratory Technician Workflow New-Age Laboratory Technician Workflow Note: Draup’s analysis. Source: The represented data has been derived using Draup’s 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. Sample Preparation Prepare samples for analysis using microscopes, pipettes, and reagents. Instrumentation & Data Collection Conduct experiments using microscopes and chemical tests Data Analysis Manually analyze the results and create a report to communicate the findings. Reporting Communicate results with visual aids and presentations. 1 2 3 4 Sample Preparation Automated systems and microfluidics used for sample preparation. Instrumentation & Data Collection Use advanced instrumentation such as HPLC and PCR for data collection. Data Processing & Analysis Utilize LIMS, ELNs, and automated data collection, as well as AI and ML for analysis. Reporting Use tools like Tableau to create interactive data visualizations. 2 3 Data Interpretation and Visualization Use software tools like R and Tableau to visualize and interpret data. 4 5 1 Disruption in the workflow of core job roles - Laboratory Technician’s workflows will become increasingly digitalized as AI & ML algorithms drive greater efficiency & productivity Sample Reskilling case study of core job role – ‘Laboratory Technician’ (1/3) Sample Upskilling case study of a sample job role – ‘Data Scientist’
9. 9 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 Cost analysis of ‘Hiring Bioinformatician’ vs. ‘Reskilling Laboratory Technician to Bioinformatician’ Benefits of Reskilling Reskilling reduces attrition rate by providing viable career paths to disrupted job roles to in-demand ones Improves Retention 69% of talent professionals believe Reskilling can help improve diversity and inclusion (D&I)2 Improves Efficiency Cost of Reskilling an existing employee is ~1/3 the cost of hiring a new employee with the same skills1 Reduces Cost Sample Reskilling case study Laboratory Technician ❑ Data Analysis ❑ Machine Learning ❑ Statistics ❑ Bioinformatics Bioinformatician 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 Draup’s 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 (Skills Addition) (Upskilling) 0 20,000 40,000 60,000 80,000 1,00,0001,20,000 Base Pay Non-Recurring cost Salary Hike Reskilling Cost Cost of Reskilling Laboratory Technician Cost of hiring Bioinformatician ~114K ~74K Total Cost saving on every FTE USD 30K Estimated team size 40 FTEs Total savings for company ~$1.2 Million /year Talent base pay Sample Reskilling case study of core job role – ‘Laboratory Technician’ (2/3) Sample Upskilling case study of a sample job role – ‘Data Scientist’
10. 10 1. Programming, Database Skills 2. Data Analytics 3. ML/Statistical Skills 4. Laboratory Techniques 5. Applied Bioinformatics 6. Genomics & Transcriptomics 7. : NCBI & Ensembl Laboratory Technician Computational Chemist Clinical Researcher Pharmacist Skill overlap High Moderate Low Sample adjacent Job Roles to Reskill Major skill gaps to be filled to reskill adjacent roles towards Bioinformatician role with in-demand emerging skills Roles suitable for Reskilling Bridging the Skills Gap: Draup’s 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 L&D modules Laboratory Technician Bioinformatician Courses Taken / Skills Addition • Advanced Data Analysis • Machine Learning • Statistics • Bioinformatics Responsibilities- 1.Conduct experiments, maintain equipment, and record-keeping. 2.Collaborating with other laboratory staff and researchers to develop new testing methods and protocols Bana ___ Laboratory Technician, Aug 2021–Mar 2022 Biolab, Amman, Jordan Bana ____ Bioinformatician, May 2022–Present Bionl.ai, Boston, USA Responsibilities- 1.Developing and applying computational tools and techniques to analyze biological data. 2.Collaborating with researchers and other scientists to design experiments and develop data analysis strategies. Real life Sample case study 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​ Sample Reskilling case study of core job role – ‘Laboratory Technician (3/3) Sample Upskilling case study of a sample job role – ‘Data Scientist’
11. 11 Source: Draup 11 Impact on Productivity Impact on Capability 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. Impact on Efficiency Data Scientist ❑ Analyze complex data to identify patterns & trends ❑ Develop insights to support business decisions ❑ Communicate findings to stakeholders & ensure the accuracy & quality of data ❑ Programming – Python, R, SQL ❑ Statistical Analysis – SPSS, SAS; Machine Learning Explainable AI - Improved model transparency & interpretability - Better compliance - Reduced time to identify model errors and issues - Reduced time to identify model issues by 60% - Improved compliance rate by 70% AutoML - Improved accuracy of ML models - Democratized ML - More projects handled simultaneously - Increased efficiency - Reduced time for hyperparameter tuning by 80% Hugging Face - Access to pre-trained models and libraries - Improved NLP capabilities - Reduced time and effort for building NLP models - Reduced time to build NLP models by 70% - Increased number of NLP projects by 60% Apache Beam - 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% Possible impact of upskilling Data Scientist to have age skills New age skills in Data Science Current workload & skillset of Data Scientist 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 Sample Reskilling case study of core job role – ‘Laboratory Technician’ Sample Upskilling case study of a sample job role – ‘Data Scientist’ (1/3)
12. 12 4.6 K 20K 40K 60K 80K 100K 120K 140K 160K 180K Base Pay Non-Recurring cost Incentive Upskilling Cost Cost analysis of ‘Upskilling’ vs. ‘Hiring’ talent for Emerging/In-demand skillsets Skillset evolution in Data Science over time AutoML • Capability: automate repetitive tasks and accelerate the drug discovery process Hugging Face • Capability: analyze large volumes of medical literature to extract information Explainable AI (XAI) • Capability: improve the interpretability of ML models used in drug discovery Updated base pay 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 Draup’s 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 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 Cost of hiring new talent with XAI skillset Cost of upskilling Data Scientist for XAI skillset ~160K ~126K Benefits of Upskilling Firms that provided upskilling training to their employees had a 12% higher productivity.1 12% Boosts Productivity Upskilling can improve efficiency by promoting innovation and creativity. Promotes Innovation Upskilling can help employee identify & solve problems quickly & improve efficiency Improves Efficiency 141K ~121K 114K 6.8K Base pay saved on every FTE 19K 4.6K One-time cost saved on FTE ~20K ~14K Overall cost saved by company ~34K Talent base pay Sample Reskilling case study of core job role – ‘Laboratory Technician’ Sample Upskilling case study of a sample job role – ‘Data Scientist’ (2/3)
13. 13 Skill Overlap High Moderate Low Roles suitable for Upskilling Bridging the Skills Gap: Skill gap analysis using Reskilling Intelligence can help design targeted upskilling programs for Data Scientists to acquire specific skills Data Scientist ML Engineer Statistician Sample adjacent Job Roles to Upskill Major skill gaps to be filled to upskill adjacent roles with in-demand emerging skill of Explainable AI (XAI) 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 XAI Data Scientist Courses Taken / Skills Addition • Interpretable ML • LIME Tutorial • Explainable ML • Responsible AI • Model Transparency & Regulatory Compliance Sugandh ___ Data Team, Sep 2018–Present Stockholm, Sweden Responsibilities- 1.Developing and implementing data analytics strategies to support business objectives. Identifying new data sources and defining data collection strategies 2.Applying advanced statistical and machine learning techniques to extract insights and patterns from data Responsibilities- 1.Ensuring model transparency and interpretability 2.Addressing issues of bias and fairness 3.Ensuring model accuracy and reliability 4.Promoting responsible and ethical AI development Sugandh ____ XAI Working Group Lead, Sep 2022–Present Stockholm, Sweden Real life Sample case study 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​ Sample Reskilling case study of core job role – ‘Laboratory Technician’ Sample Upskilling case study of a sample job role – ‘Data Scientist’ (3/3)
14. About Draup
15. 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. Peer Intelligence Global Locations Footprint Digital Transformation Talent Acquisition Diversity & Inclusion Career Path Development Reskilling Strategic Workforce Planning Draup Capabilities & Data Assets EMPOWERS DECISION MAKING IN ROLES & SKILLS TAXONOMY DIGITAL IMPACT ON TRADITIONAL ROLES CAREER PATH PREDICTOR PEER BENCHMARKING LOCATION INTELLIGENCE Explore Diverse Job Roles, Locations and Ecosystem Insights DIVERSITY INTELLIGENCE TALENT INTELLIGENCE UNIVERSITY INTELLIGENCE COURSES/ CERTIFICATIONS and diverse other use cases…
16. 16 Draup for Talent: Draup analyses 16+ Million data attributes every day to help global HR leaders in Planning, Hiring & Reskilling their Future-Ready Workforce 700M+ 280M+ JOB DESCRIPTIONS 4M+ CAREER PATHS ANALYZED 75+ MACHINE LEARNING MODELS DEYELOPED 16M+ DAILY DATA POINTS ANALYZED 100+ LABORSTATISTIC DATABASE 1,000+ CUSTOM TALENT REPORTS 30,000 SKILLS 47,000+ DIGITAL TOOLS & PLATFORMS 300,000+ COURSES 2,500+ LOCATIONS 14,000+ UNIVERSITIES JOB ROLES 4,500+ PEERGROUP COMPANIES 500,000+ INDUSTRIES 33 175,000+ UNIVERSITY PROFESSORS PROFESSIONALS
17. 17 www.draup.com HOUSTON I BANGALORE © 2023 DRAUP. All Rights Reserved.