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FORECASTING IN REGULATED MARKETS(US & UK)

Kaustubh V. Kokane

Summer Trainee- Supply Chain

Mentor- Mr. Umang Gandhi (GM- Supply Chain)

27th June, 2013Thursday

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Flow of Presentation

Forecasting Industry best practices US forecasting

Forecasting process Pros & cons Challenges Suggestions for improvement

UK Forecasting Forecasting process Pros & cons Challenges Suggestions for improvement

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Forecasting

Predicting future demand Short and mid-term forecast tactical / operational

planning (1 month to 1 year) Long-term forecast Capacity planning (1 year +)

Critical point of co-operative work between supply chain and marketing teams

Forecast models Time horizon Time-series analysis

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Maturity-wise Forecasting

Criteria Matured Drugs Growth Stage Drugs New Drugs

Availability of historic sales data

Ample Short-span sales history

No sales history

Forecasting Extensive quantitative forecasting possible

Short-term forecasting possible

Focus on qualitative forecasting with inputs regarding launch scenario, promotions, etc.

Forecast Accuracy Expected to be more accurate

Accuracy will improve over time

Difficult to forecast with great accuracy

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Branded v/s Generics

Branded Drugs Generic Drugs

Indian market dominated by branded drugs

US & EU markets dominated by generic drugs

Drugs prescribed & sold by their trademarks

Drugs prescribed & sold by their generic name (molecular name)

Usually costlier than generics Usually cheaper than branded drugs

R&D expenses involved for manufacturer Drug-discovery R&D expenses not involved

Patent protection for the manufacturer Generic players come into picture only after a molecular patent expires

Major companies- Pfizer, GSK, Novartis, J&J, Roche, Sanofi

Major companies- Teva, Mylan, Sandoz, Greenstone, Hospira, Dr. Reddy’s

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Replenishment v/s Forecast Models

Criteria Replenishment Model Forecast Model

Production Demand-driven Forecast-driven

Inventory Decreased retailers’ & manufacturers’ inventory

Inability to meet changing demand patterns & risk of obsolation

Flexibility More responsive Not flexible enough to handle frequent changes

Information Automated information processing through VMI and collaborative planning

Risk of inaccurate information

Lead time Decreased because of availability of inventory at each level

Predictable lead time

Industry example

GSK, Teva Pharmaceuticals, Dr. Reddy’s (domestic)

All generic pharma exporters based in India, Ranbaxy (domestic)

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Effects of Ineffective Demand Planning

Higher production cost Overstocking or stock-outs Inefficient logistics Dissatisfied customers- losing to competition FDA cracking up on expired drugs

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INDUSTRY BEST PRACTICES

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Data used for Forecasting

Primary Data

Point-of-sale data not readily available

Proprietary patient data (through third-party services such as e-talk’)

Secondary Data

Purchase data from distributors (fee-for-service)

Vendor-managed inventory examples:

(US)

(Denmark) Channel data (ex. Retailers’ IMS

access to pharma co’s- - partnership) Utilize sales force (feedback from

retailers)

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Pfizer (Australia)

Integrated demand forecast review process in business planning

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Teva Pharmaceuticals, UK

Was relying on stand-alone Excel sheets for forecasting large no. of SKU’s (till 2009)

Laborious and time-consuming process Implemented customized demand management

tool- RefleX to forecast & plan demand Forecast accuracy improved from 65% to 80% Seamless business integration thereafter

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FORECASTING @ WOCKHARDT

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Forecasting Process- US

Manual Adjustments(Supply Chain team)

Customer Forecasts & Inventory Data

(EDI)

John Galt SolutionsForecasting Software

Sales & Marketing Inputs

Demand Forecast

Sales History & Trends

Measuring Forecast Accuracy

Highlights: 18-months rolling forecast Forecast accuracy (MAPE method): 75% (A-class), 66% (overall) Forecast accuracy measured by MAPE, quantitative bias

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Product Group-wise Forecast Error (US)

Average Forecast Error:

For matured products: 34%

For non-matured products: 70%

Note: Forecast data from January-13 to April-13 was analyzed for the above results

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Strong quantitative fundamentals & future plans

Long horizon for forecasting- capacity planning

Industry-standard MAPE method of measuring forecast accuracy, can be benchmarked

Working capital management

Poor visibility from customer-end (no customer collaboration)

All marketing inputs may not reflect in final forecast (as supply chain team takes the final call)

Pros Cons

Forecasting @ Wockhardt US

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Challenges- US

Controllable: Lack of visibility (customer side) Penalties related to tendering

Non-controllable: Demand volatility Sales concentration- top 3 wholesalers Market is very sensitive to short-supplies

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Suggestions

‘Bias’-based forecast accuracy monitoring Consistently lower or higher forecast than actual sales In April 2013 (forecasted over 4 months):

30% SKU’s – Under-forecasted 18% SKU’s – Over-forecasted

Causes: Undetected patterns “Beat the numbers” approach

Weighted bias / size-based bias

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Suggestions

Shift from MAPE to WMAPE (Weighed Mean Absolute Percentage Error) Yields more meaningful analysis of forecast accuracy

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Suggestions

Consensus forecasting: Particularly for non-matured drugs (high uncertainty) Concurrent forecasting team with SC and marketing

executives (S&OP) Review supply and demand requirements frequently

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Forecasting Process- UK

Sales Forecast (Marketing team)

Supply Chain team(Past trends, stock

levels & other inputs)

Manual Adjustments(Supply Chain team)

Demand ForecastMeasuring Forecast Accuracy

Highlights: 12-months rolling forecast SKU category-wise forecast accuracy analysis

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Strong marketing team inputs

Long horizon for forecasting- capacity planning

Poor visibility from customer-end (no customer collaboration or VMI)

No dedicated forecasting software solution in place

Forecast accuracy cannot be benchmarked with current metric

Forecasting @ Wockhardt UK

Pros Cons

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Challenges- UK

Controllable: Customer collaboration Service level management (90-95%)

Non-controllable: Demand volatility Market is very sensitive to short-supplies

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Suggestions

EDI of inventory and sales data

Established standard in US & EU Modeling & analyzing the inventory data Suitable alternative to VMI for mid-level producers Better visibility of customer demand trends Better tackling of bullwhip/whiplash effect

Top customers Wockhardt

Forecast & inventory data

Better serviceability

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Suggestions

Quarterly benchmarking against competitors for forecast accuracy and market share Shifting to industry-standard metric of measuring

forecast accuracy (MAPE / WMAPE)

Particular Forecast Accuracy

US Generic Marketers ~70%

Wockhardt US 60-70%

Teva Pharmaceuticals UK 85%

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Suggestions

Validate forecasts for established drugs: Scientific validation in statgraphics Remove bias and better selection of forecasting model

Quantitative forecasting software: Optimum use of available data; data mining Useful for established products

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Kaustubh V. KokaneSummer Trainee- Supply ChainMentor- Mr. Umang Gandhi (GM- Supply Chain)

Institute- Prin. L. N. Welingkar Institute of Management, Mumbai

Thank You !