Sales forecasting with SAS Advanced Analytics for the Pharmaceutical sector. A business case

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1 Sales forecasting with SAS Advanced Analytics for the Pharmaceutical sector. A business case

2 Blue BI: Company Profile Blue BI is a growing company that provides IT consulting in the Business Intelligence & Analytics environment. Our mission is turn data into actionable insights: we help organisations to take faster and smarter decisions and tackle the most complex business issues. Blue BI offers a comprehensive set of Business Intelligence end to end services, from POC s to Application Management, in the following areas: Corporate Performance Management Business Intelligence & Analytics Data Warehousing Our organisation our organization is formed by a group of professionals with more than ten years of experience, located in our offices of Milan, Turin and Rome. 2

3 Blue BI: SAS Partnership Blue BI s deep business and technology experience combined with the wide range of SAS applications, delivers to the market a competitive offer based on the following elements: Extensive expertise in some industries (i.e. Life Sciences) and delivery capabilities Functional and technological competences on SAS software solutions Flexible and scalable approach based on tangible and proven results Usage of predefined data model as project accelerator: BBI x Pharma Option for Cloud and SaaS (Software as a Service ) system deployment 3

4 Blue BI and the Life Sciences industry: BBI x Pharma Blue BI plans and builds Business Intelligence & Analytics systems in several industries, with a specific focus in Life Sciences, where we can include among our customers some of the most important domestic and foreign organisations. Our specialisation is based on the following distinguishing elements: extensive and long lasting experience on the specific business processes and analytics needs Effective implementations of reporting& analytics systems in several areas, such as: Sales Forecasting Sales Analysis (Internal/External) Geo Marketing Integration and market research Social media analysis CRM Analytics Sales Force / Field Force Effectiveness Planning & Control Logistics analytics BBI x PHARMA Mobile applications Cloud & Software as A Service deployment 4

5 What is Sales Forecast? Is a combined analysis of qualitative and quantitative components aimed to predict the sales performance, in the short, medium and long period. Quantitative components: time series that provide evidences of past performance Qualitative components: known or presumed elements which are not represented by the time series 5

6 Why is Sales Forecast becoming increasingly important? To rely on the most possible accurate and reliable Forecast Model for sales allows you: - in the short period, to organize business functions and resources - in the long period, to plan investment programs - in general, to support strategic decisions In a global and high competitive scenario, where markets are rapidly and constantly changing, forecast analysis is usefull especially in the short period. (6-12 months) 6

7 Business Case Company: One of the world s largest biotech companies, with more than 7,000 employees across six continents and a rapidly expanding product portfolio and a growing pipeline. Business Area: The company provides medical product support across the Hospital Channel Business Needs: Understanding the adoption of the existing products and predict adoption of new products allows to increase the customer service, adoption and eventually profitability 7

8 Project Phases Step 1 Collect and Analyze Historical Data Series Collected 24 months «Internal» Sales data by Product / Territory / Region Apply Regression Algorithms Loading Dataset in SAS Forecast Server Visualize and Analyze the Model Forecast Model Review 8

9 Project Phases Step 1 Collect and Analyze Historical Data Series Apply Regression Algorithms Visualize and Analyze the Model 9

10 Project Phases Step 2 Collect and Analyze Historical Data Series Stock Estimate using IMS Sales compared to «Internal» Sales Apply Regression Algorithms Visualize and Analyze the Model Daily Average Consumption evaluation Stock optimization index calculation Load the Stock optimization index in SAS Forecast Server as a regressor 10

11 Project Phases Step 2 Before regression. Collect and Analyze Historical Data Series Stock Estimate using IMS Sales compared to «Internal» Sales Apply Regression Algorithms Visualize and Analyze the Model Daily Average Consumption evaluation Stock optimization index calculation Load the Stock optimization index in SAS Forecast Server as a regressor...after regression 11

12 Project Phases Step 3 Collect and Analyze Historical Data Series Import Predictive Model in SAS Visual Analytics Visualize and Analyze the results Apply Regression Algorithms Visualize and Analyze the Model Analyze the model generates new questions Refine the Predictive Model with new regressions Validate the analysis and decision support 12

13 Project Phases Step 3 From analysis to delivery in 1 month Collect and Analyze Historical Data Series Apply Regression Algorithms Visualize and Analyze the Model 13

14 Predict Adoption for new products The issue: - Lack of an appropriate time series ( <24 months) Solution: - Time series of a product with equivalent sales characteristics - Implementation of a configurable growth curve 14

15 Technical Architecture 27/7 server availability SAS licenses deployed in SaaS mode Support services included in the periodic fees End to end Blue BI support: Project implementation, Application and System management Business Continuity: high availability, fault Tolerance, Disaster Recovery, 15

16 Benefits Strong improvement of the Sales Forecast accuracy Quick R.O.I in terms of enhanced effectiveness in the sales and distribution processes Reaction time increase: better time-to-market More compliance with corporate requirements Users empowerment, better understanding of the business processes Full automated forecasting process, previously costly and totally hand made (Excel), with: o o Automatic selection of the best statistical model Manual override opportunity to manage not predictable qualitative elements from the statistical model (i.e. new competitor product launch) Low cost and flexible technical infrastructure with no impact on the corporate governance guidelines 16

17 Q & A Please contact us at Milano Via Caldera, 21 Palazzo C Milano Tel Torino Corso Filippo Turati, Torino Tel Roma Via Giambattista Vico, Roma Tel

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