InstructorRoyed Training
TypeOnline Course
Student Enrolled11
Price$490 / 34300 INR.
Buy NowBook Now
Loading
Loading

Introduction

Who should attend

Course Coverage

Features

Related Courses

About Pharma Forecasting

pharmaceutical forecasting models

This Pharma forecasting training will provide practical understanding about the pharmaceutical forecasting models, tools and techniques. Pharmaceutical forecasting training covers pharmaceutical and biopharmaceutical market sizing and forecasting models. Hence, this course will demonstrate sales forecasting skills for pharmaceutical and biopharmaceutical managers to be successful. In fact, this pharmaceutical forecasting course provides real life case based simulations for better understanding the complex issues of market and new product forecasting. As a result, it is easy for participants to understand the concepts. Moreover, cutting edge simulations help participants apply the knowledge in interactive real life simulation.

Above all, this pharma forecasting training course focuses on different forecasting techniques which are commonly used in industry. Also at the same time, the course provides in-depth understanding on evidence-based forecasting for innovators or similar biopharmaceuticals.

The course can be completed at any place. So, it allows the user to stop and start at their leisure. 

Moreover, user can access the course at own pace. It allows the user to stop and start at their leisure. 

  • Course Code: RYD-090
  • Title of the course: Advanced Certificate course on Pharma Forecasting (ACPF)
  • ️Nature of the course: Online distance learning course. Course can be accessed online across anywhere 24×7.
  • Duration: 1 Month
  • Certification: Course certificate will be provided at the end of the successful completion of the course.

Who should attend this pharma forecasting training?

  • Pricing, Market Access Professionals who want to learn about regional markets.
  • Pharma BD professionals who are working on market size, market expansion and new product launch etc.
  • Strategic planners who want to know where they might go next
  • Those who want insight into the way that markets influence each other
  • Anyone who wants to understand pharma brand forecasting methods and tools.

Course Coverage

The pharma forecasting course provided comprehensive advanced understanding on forecasting models which are used in pharmaceutical industry.

  • Fundamental of Pharma Forecasting: Forecasting fundamentals, role of forecasting in pharma strategic decisions, key forecasting terminologies, overview of forecasting tools and models Sources of market data, data validation and cleaning, assessing data reliability, integrating primary and secondary market research data
  • Forecasting Methods: Market research-based forecasting, expert panel consensus, Delphi method, scenario analysis, Time series analysis, regression analysis, moving averages and exponential smoothing, use of statistical software in forecasting, Patient-Based Forecasting – Patient flow models, incidence and prevalence-based forecasting, line of therapy forecasts, Therapeutic area–specific case analyses, product life cycle forecasting examples, market event simulation, interactive workshops and hands-on exercises, Pipeline product forecasting, peak sales estimation, scenario planning, competition and market adoption rates
  • Forecasting for Generic and Biosimilar Products – Lifecycle management considerations, patent expiry impact, competitor pipeline analysis, pricing and market share estimations
  • Financial and Revenue Forecasting – Revenue modeling, budgeting and resource allocation, pricing strategies and impact on forecasts, profit and loss projection
  • Forecast Accuracy and Validation
  • Practical Applications and Case Studies
  • Real World Evidence and Real World Data
  • Simulations – Data visualizations
  • Advance practical training in market sizing and forecasted sales.
  • Detailed training on forecasting models and techniques with practical case studies.
  • Training on excel based pharmaceutical and biopharmaceutical forecasting.
  • Forecasting model development

Pharma Forecasting Course Features

This pharma forecasting training utilizes 24×7 interactive learning tools to guide each participant through the various forecasting models. The course challenges students to apply what they have learned through the use of interactive exercises, reflection questions, expert live chat and a final assessment at the end of the course.

Important Learning Features: 

  • Online 24×7 access from anywhere. Hence, one can learn at your convenience.
  • The course provides timing flexibility. In other words, one can attend the lecture sessions at your own convenient time.
  • The course is build on interactive e-learning. Hence, it helps user to understand of the concepts effectively.
  • Above all, course provides simulation for real life working. Hence, it helps user to apply the decision making skill in real life scenarios.

Course Coverage Area

  • The pharma forecasting course provided comprehensive advanced understanding on forecasting models which are used in pharmaceutical industry.
  • Advance practical training in market sizing and forecasted sales.
  • Detailed training on forecasting models and techniques with practical case studies.
  • Training on excel based pharmaceutical and biopharmaceutical forecasting

Related Courses

Followings are the few of the similar courses, you may be interested in

Role and Responsibility of Pharma Forecasting Professionals

The role of a pharma forecaster is to analyze and predict trends, opportunities, and challenges in the pharmaceutical industry. Hence, they use various data sources, statistical models, and market research techniques to forecast future demand for pharmaceutical products and services. The insights provided by pharma forecasters help pharmaceutical companies, healthcare providers, and policymakers make informed decisions regarding drug development, production, distribution, and pricing.

Here are some specific responsibilities and tasks of a pharma forecaster:

Data Analysis

Pharma forecasters collect and analyze data related to market trends, patient demographics, disease prevalence, healthcare policies, and competitor activities. They use statistical techniques and software to identify patterns, correlations, and emerging trends.

Market Research

They conduct market research to gather information on new drugs, treatment protocols, regulatory changes, and patient preferences. This research helps in understanding market dynamics, competitive landscape, and potential barriers to entry.

Demand Forecasting

Using historical data, market research findings, and statistical models, pharma forecasters predict the future demand for pharmaceutical products. Therefore, this involves estimating the sales volume, market share, and revenue potential for specific drugs or therapeutic areas.

Scenario Planning

Above all, pharma forecasters develop scenarios and conduct sensitivity analysis to assess the impact of different factors on the pharmaceutical market. Hence, this helps companies prepare for various scenarios, such as changes in reimbursement policies, patent expirations, or entry of generic competitors.

Pricing and Reimbursement Analysis

Most importantly, pharma forecasting professionals analyze pricing strategies, reimbursement policies, and healthcare payer trends to understand the financial implications for pharmaceutical products. Hence, forecasting analysis helps in optimizing pricing and market access strategies.

Communication and Reporting

Pharma forecasters communicate their findings and recommendations to key stakeholders, such as executives, product managers, marketing teams, and regulatory bodies. So, they prepare reports, presentations, and visualizations to effectively communicate complex information and present to respective department stakeholders.

Overall, the role of a pharma forecaster is crucial in guiding strategic decision-making within the pharmaceutical industry. Hence, their insights and forecasts help pharmaceutical companies stay competitive, make informed investments, and adapt to the evolving market dynamics.

Section 1Forecasting - Models, Techniques (Core Sessions)
Lecture 1Sales Forecasting in Lifesceience industry
Lecture 2New Product Forecast algorithms
Lecture 3Patient Based Forecasting Model | Applying more filters and variables
Lecture 4Prescription Based Forecasting Model | Differences between Patient Based and Prescription Based Forecasting Model | Which model to use and when?
Lecture 5Prevalence Vs. Incidence Model
Lecture 6EPI Based Forecasting | Sales Based Forecasting | When and where to apply which forecasting model
Lecture 7Sales Forecasting Tools | New Product Forecasting | In Market Forecasting
Lecture 8Market Sizing Assignment 1 - Hands on Training
Lecture 9Market Size Assignment 2 [Asthma Brand] - Applying sales forecasting tools to carry out multiple years sales forecast | Excel Based Model
Lecture 10Patient Based Model Vs. Patient Flow Model | Critical Differences in Model | Concept of Black Box in Patient Flow Model | Application of both model
Lecture 11Concomitancy and polypharmacy | How it alter the basic forecasting algorithm | Practical Working | Comorbidity
Lecture 12Forecasting Techniques | Simple Conjoint-type Models | Zipf's Law | Simple Elasticity Model | The Bass Model |Simple Extrapolation
Lecture 13Bottom-up or Top-down Forecasting | LRx | Significance of LRx data analysis
Lecture 14Simulation on Bottom-up forecasting
Lecture 15Assessment on Bottom-up forecasting
Lecture 16Oncology Brand Forecasting
Lecture 17Revenue Forecasting Case Study - Novel Antihypertensive 
Lecture 18First-in-class | Best-in-class | Market Access Strategic Decision Making 
Lecture 19Basics of Pharma Forecasting  | Qualitative | Quantitative | Time Series Forecasting | Case Based Example
Lecture 20Quantitative Vs. Qualitative Forecasting | Application in different scenarios | Practical Case Example | Scenarios  
Lecture 21Commonly used Pharma Forecasting Tools | Understanding each types of tools | Time Series Analysis [ARIMA, Exponential Smoothing] | Regression Analysis | Machine Learning Models | Simulation Models | Delphi Method | Scenario Planning
Lecture 22Time Series Forecasting | Step by step time series forecasting | Characteristics of Time Series forecasting
Lecture 23ARIMA Model 
Lecture 24ARIMA Model application on pharmaceutical industry | Application in different domains of pharma | Logical step by step Arima Model Development 
Lecture 25Exponential Smoothing : in pharma forecasting | Smoothing Factor | Step by step working on Time Series Data by applying Exponential Smoothing
Lecture 26Causal Model of Forecasting | Steps & Key components | Case Based Application 
Lecture 27 Sample Time Series Analysis Data for a Pharma Brand
Lecture 2810 Time Series Curve with explanation 
Lecture 29Assignment : Developing Causal Model of Forecasting | Asthma Drug | Antihypertensives
Lecture 30Judgmental Forecasting | Understanding | Forecasting Technique | Delphi Method | Case Study - Judgmental Forecasting of Alzheimer Drug 
Lecture 31Important Pharma Data Sources
Lecture 32Flu Vaccine Forecasting
Lecture 33Forecasting Methods for different classes of pharmaceuticals
Lecture 34Meaning Significance Example of Different Forecasting Strategies
Lecture 35Explanation of Different Models in the Context of Pharma Forecasting | Simple Conjoint-type Models | Zipf’s Law | Simple Elasticity Model | The Bass Model | Simple Extrapolation
Lecture 36Conjoint-Based Demand Estimation in Pharma Forecasting | Conceptual Foundation | Attributes Decomposition | Step By Step Calculation 
Lecture 37Patient vs Physician Preference Split in Pharma Forecasting (Dual-Decision Modeling in Demand Estimation) | Calculations| Case Examples
Lecture 38Zipf’s Law in Pharma Forecasting (Power-Law Distribution of Demand) | Calculation | Application | Case Study
Lecture 39Simple Elasticity Model in Pharma Forecasting (Price → Volume → Revenue Decision Engine) | Calculation step by step | Case Based Analysis
Lecture 40The Bass Model in Pharma Forecasting - New Product Adoption & Diffusion Modeling | Calculation | Case Scenario
Lecture 41Simple Extrapolation in Pharma Forecasting | Trend-Based Demand Projection | 3 Different Methods | Calculation | Sample Exercise
Section 2Pharmacoeconomic | Setting up Value of the asset
Lecture 42Introduction to Pharmacoeconomics | Different Types of Economic Evaluation | Cost-effectiveness plane | Economic modeling |Sensitivity Analysis | One way sensitivity analysis | Probabilistic sensitivity analysis 
Lecture 43Economic Evaluation Methods | Various methods | Importance of perspective in health economics | Discounting | Uncertainty 
Lecture 44Core fundamentals of Health Economics | Clinical and economic burden | Public Health Care Payer Vs. Patient and Societal | Positioning a new treatment with the current treatment | QALY | ICER | Threshold value | Clinical trials vs health economics assessment
Lecture 45QALY | How to calculate QALY | Importance and Significance 
Lecture 46ICER | How to calculate ICER | Importance and Significance | Incremental Effectiveness determination 
Lecture 47Pharmacoeconomic Evaluation Checklist | Health Economics Map | Economic evaluation cycle | Checklist of economic evaluation | Costs and Outcome relevant to different groups | Comparators | Problem of choosing the comparators
Lecture 48Pharmacoeconomic Evaluation - Resource and Cost | Fixed, Variable and Total Cost | Calculation | Average vs. marginal cost | Importance of marginal cost | Discounting | Discounting calculation | Preferable discounting rate | Sources of unit cost data | Reference costs
Lecture 49Pharmacoeconomic Evaluation - Benefits and Outcome | C/E Ratio | Intermediate Vs. Final Outcome | Sources of Effectiveness Data | QALY | Utility Weight | WTP | VPF | ATP | Difference between WTP & ATP 
Lecture 50Pharmaco-economic evaluation – analysis and results | Different Types of Models | Understanding Decision Tree - Components with practical Example | Markov model 
Lecture 51CER and PCOR
Lecture 52Value Dossier | Sample Generic Dossier | Sample Innovator Biologic Dossier | Strategic Differentiations
Lecture 53Advanced Price Justification Writing | Real Life Cases Pricing Justification Writing
Lecture 54ICER Within Accepted Thresholds
Lecture 55ICER Accepted Thresholds Global Benchmark Data
Lecture 56ICER Threshold Types — Meaning & Practical Use
Lecture 57Case Simulation: GDP-Based ICER Calculation (3 Scenarios)
Section 3Patent and Exclusivity Based Forecasting | Revenue Forecasting
Lecture 58R&D Process & Introduction to drug discovery | Understanding of IND, NDA and ANDA
Lecture 59Patent & Exclusivity - Strategic Understanding | Patent Restoration| Orphan Drug Exclusivity (ODE) | NCE & NCI Exclusivity | GAIN Exclusivity | Pediatric Exclusivity | 180 days exclusivity
Lecture 60Patent Cliff | How to calculate the patent cliff
Lecture 61Portfolio Based Value Forecasting | Developing Excel Based Working Model
Section 4Forecasting and Valuation
Lecture 62Training on Basic Finance  |  Understanding Financial Statment | Income Statement | Balance Sheet | Cash Flow Statement
Lecture 63Valuation Methods: DCF | rNPV | Sunk Cost Method | Comparables | Sum of Parts | Deterministic Vs. Probabilistic rNPV |  Peak Sales - Max-Min Approach
Lecture 64Valuation Methods: EBITDA method of valuation | Enterprise Value and Enterprise Multiple Calculation
Lecture 65Business Development Simulation and Decision Making | Early Stage VS. Late Stage Valuation Methodology | Stair Step Model
Lecture 66Modelling and Deal Valuation - Top Down Model | Epidemiology data method | Complex Model | Forecasting | Heuristic or ‘prophesy’ method | Different value perspectives | Deal Structuring Structuring the deal
Lecture 67BD&L Ecosystem in Pharma: How Licensing Actually Happens
Lecture 68Why do most high-value deals happen before Phase III?
Lecture 69What are the risks of ignoring early-stage assets?
Lecture 70In-Licensing vs Out-Licensing vs Co- Development: Strategic Decision-Making in BD&L
Lecture 71How would you identify a high-potential licensing opportunity?
Lecture 72Understanding Asset Classes in Licensing: Strategic Selection & Deal Implications
Lecture 73Asset Scouting Framework: Identifying High-Value Licensing Opportunities
Lecture 74Case Study – Deal Structuring - 3 Different Assets 
Lecture 75Selecting the Right Therapy Area | Market Intelligence for High-Impact Licensing Decisions - Case Based Analysis
Section 5Forecasting related discussions
Lecture 76Forecasting Biosimilar | Key Factors to consider
Lecture 77Consensus Meeting 
Lecture 78One Number Vs. Multi Number Forecasting
Lecture 79Active Vs. Passive Cannibalization | Impact on forecasting | Case Study
Lecture 80Treatment Algorithm and Forecasting : Case Study
Lecture 81Chronic and Acute Segment | Forecasting on Chronic and Acute Segment Drugs
Lecture 82Prescription Value Calculation 
Lecture 83Commonly used Pharma Forecasting Tools | Understanding each types of tools | Time Series Analysis [ARIMA, Exponential Smoothing] | Regression Analysis | Machine Learning Models | Simulation Models | Delphi Method | Scenario Planning
Section 6Forecasting Case Studies | Used Cases
Lecture 84Demand Forecasting for a Drug Targeting Duchenne Muscular Dystrophy (DMD) | Rare Disease Forecasting | Model Development | Assignment | Download Excel Work File
Lecture 85Forecasting the Uptake of a New Cancer Drug After Launch in a Specific Region
Lecture 86Forecasting Demand for a New Cancer Treatment Expected to Generate Billions in Sales Globally
Lecture 87Generic Drug Forecasting - Post Patent Expiry 
Lecture 88Estimating the Market Uptake of a Generic Version of a Popular Cholesterol-Lowering Drug
Lecture 89Market Share Forecast for a New Hypertension Drug
Lecture 90Forecasting During Early Stage of Development 
Lecture 91Case Simulation : Demographic-Based Forecasting
Lecture 92Forecasting and Valuation for Weight Loss Drugs | Forecasting Model | Model Assumptions | Data Inputs | Projected Revenue Calculations 
Lecture 93Forecasting Assignment
Section 7Working on Datasets
Lecture 94Introduction to Healthcare Datasets
Lecture 95Dataset 1: Claims Data (Insurance)
Lecture 96Dataset 2: Retail Pharmacy Prescription Data
Lecture 97Dataset 3: Longitudinal Patient Data
Lecture 98Characteristics of Different Set of Healthcare Data
Lecture 99Reimbursement and Pricing Datasets
Section 8Course Certificate
, , ,