Describes in detail the problems which the pharmaceutical industry faces ... because of the technical, regulatory and structural problems, collaboration among pharmaceutical supply chain partners is lacking relative to other industries ... risk associated with forecast error that cannot be analyzed with deterministic point forecasting techniques can be analyzed with stochastic techniques, such as Monte Carlo Analysis, that account for uncertainty through probability and ranges.
Nowadays, pharmaceutical company executives face a dizzying array of complicated industry conditions when analyzing the supply of and demand for prescription drugs. Rising technical barriers posed by biotechnology; tougher regulatory policies set by government agencies; more stringent cost containment measures imposed by health care providers; growing product litigation from patient advocates; mounting competition from generic drugs; increasing prevalence of counterfeit branded drugs; and declining return on investment from new products are just a few examples of adverse market conditions that are making it more and more difficult to forecast in the pharmaceutical industry.
With all these changes, the intrinsic structure of the pharmaceutical industry is becoming increasingly inflexible. Subsequently, pharmaceutical industry structure is preventing broad-based adoption of supply chain forecasting solutions that other industries have employed for years. In an effort to maintain shareholder value while combating vagaries of the marketplace, some pharmaceutical companies' executives have been distracted. Pharmaceutical industry conditions are diverting senior executives' attention and corporate resources away from business fundamentals, such as improving demand planning. This article argues that technical, regulatory and structural factors that are unique to the pharmaceutical industry exacerbate supply and demand uncertainty, increase dysfunctional behavior among pharmaceutical supply chain partners, and produce economic inefficiencies.
The technical issues in pharmaceutical manufacturing can cause sudden interruptions in product supply. Processes used in pharmaceutical manufacturing can be highly volatile, and, therefore, are more difficult to control than those used for other types of manufacturing. This is particularly true for complicated, biotechnology processes. In spite of statistical process controls and six sigma initiatives, biotech products can experience wide fluctuations in the chemical yield of production batches.
Long-range, strategic forecasting, highly variable production processes, combined with highly regulated product development processes and long lead times for building new plants, require strategic forecasts that span entire life cycle. Therefore, strategic forecasts for pharmaceutical products require horizons of ten years or more. Pharmaceutical forecasters are well aware that the ramifications of forecast error may not become evident until years after resources are committed and plants are constructed.
Consequently, forecasters in the pharmaceutical industry attempt to mitigate risk of forecast error in their long-range, capacity planning decisions. Risk associated with forecast error that cannot be analyzed using deterministic point forecasts are evaluated using stochastic techniques, such as Monte Carlo Analysis, which incorporate uncertainty through probability and ranges. While these methods provide the ability to quantify levels of uncertainty, decision-makers are still faced with difficult choices.
On the one hand, capacity decisions based upon "upside forecast" probabilities, whose outcomes do not materialize, can result in excess capacity and over-supply. On the other hand, capacity decisions based upon conservative "downside forecast" probabilities may underestimate market demand and lead to long-term product shortages. Therefore, depending upon the amount of investment, risk-averse supply planners tend to err on the side of caution and may size plant capacity below "upside" forecasts to hedge against over-investment. …