Good Forecasting: Fortune Telling or Systematic Predictions

What’s a forecast?

Forecasting in the pharmaceutical industry may be viewed as a means of predicting the future, a tool that aids in decision making, a measurement for the uncertainties that lie ahead or a help in planning the next course of action.

No matter the angle, all the perspectives intersect at one point, which is the process of painting an accurate picture of the future, and that’s where many experts may get it wrong.

Real stories discrepancies:

The discrepancies between the forecast and actual performance squashed the expectations for some drugs to become blockbusters, as in the case for Merck’s Blocarden and SmithKline’s Monocid.

While for other drugs that weren’t projected to be blockbuster drugs when first launched, the forecasting game didn’t play well either, as in the case of Searle’s Aspartame and Shering-Plough’s Garamycin.

Forecasting complexity:

Forecasting is a challenging process due to the fact that it has to cover several variants, interact with cross-functional areas and expect the progress course of numerous data-points.

Forecasting structure and approach may differ according to the function or need of the forecaster:

Forecasting structure may vary according to the dynamicity of the situation,

For example when launching a product from the R&D portfolio,  or shooting a dog on the BCG matrix, forecasting will be essential to the portfolio planning considering the risk factors for accepting the new product in the market and the which product will trade-off the shot down product.

Also, forecasting will be key determining factor when the company develops a new marketing campaign, in order to estimate the actual campaign ROI vs. the planned ROI, the sales volume in terms of units.

In a manufacturing facility, the role and form of forecasting will differ because the main objective is estimating the volume of units to be manufactured, the wasted products, consider the current stock, storage plan and expected shelf life.

With the finance department, forecasting will be crucial to plan the OPEX ratio, the allocated budgets for marketing, sales, R&D, administrative tasks.

That’s why the forecaster must understand why the forecast is needed in order to develop the best construct for generating the forecast.

Forecasting methods:

Arthur G. Cook in his book; Forecasting For Pharmaceutical Industry, listed the methods of forecasting eloquently according to two main categories, user friendliness and method complexity.

Good Forecasting: Fortune Telling or Systematic Predictions

 

Dartboard methods

  • This method depends mainly on the gut feeling, the previous experience and strong familiarity of the forecaster.
  • The disadvantages of this method maybe that the forecaster will not be able to interpret the reasons for the forecast or answer questions like how the numbers will change in case of increased marketing budgets

Workstation models

The most complex model identified by Arthur G. Cook in his book, requires several data points entries for the equations built in the software to complete the forecast.  This sophisticated workstation model may burden the user with required data.

Simple spreadsheet methods

The most commonly used till now in pharmaceutical industry forecasting, mainly because it’s simple, organized.

Analytic spreadsheet methods

An evolution to the simple spreadsheet methods that includes more advanced analytical functions like geographical areas, patient segments, plus including visual data.

Systems dynamics methods

This is a more advanced forecasting method that can include variations in the treatment behavior of the patient, such as low compliance rates, lag phases between treatment cycles through application programs such as: iThink, Vensim and Powersim.

Forecasting 3.0: what’s next?

As Ernt & Young developed their Pharma 3.0 model, which mainly revolves around “improved health outcomes”, as an evolution in Pharmaceutical industry from Pharma 2.0 and Pharma 1.0.

Pharma 1.0 model focused solely on blockbusters, while Pharma 2.0 focused on increased emerging markets penetration and branded generics development, and neither of them had considered the drivers for change to Pharma 3.0.

High patient involvement and payors interference are key driving factors in shifting the power from the prescriber to the end user.

Also the rising power and volume of OTC “Over The Counter” market, the adopted trend of devoting complete business units in pharmaceutical companies to Consumer HealthCare (CHC) plus the direct consumer communication, the real-time access to sophisticated medical information, and last but not least increased health literacy will definitely shift the decision making power from the hands of the prescriber to the hands of the patient.

Accordingly, the forecaster has to combine the scientific methodologies for future telling of the product’s performance plus considering the more dynamic patient dependent factors in the forecasting process, such as compliance rates, awareness levels, diagnosed patients, direct to consumer advertising effects in order to keep up with Pharma 3.0.

Good Forecasting: Fortune Telling or Systematic Predictionslast edit: 2017-10-26T17:18:00+00:00da Luca

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