Schering Plough: Replacing ClaritinEssay Preview: Schering Plough: Replacing ClaritinReport this essayIntroductionSchering Plough is a large pharmaceutical company who is about to lose the patent to its largest revenue generating drug, Claratin. The loss of exclusive rights to this product could decrease Schering Ploughs revenue by over 90%.
Schering Plough in hindsight of what will become of their financial position with the expiration of this patent has decided that it must develop a new product which uses Loratadine, the main component of Claratin. The two drugs the company wishes to investigate are Newprox, an asthmatic drug; and Minprox, a nasal congestion spray. Based upon data provided from its financial and marketing department Schering Plough has compiled data for the two products that could replace Claratin as its new “flagship product”. The data used for the decision analysis in this report can be found in Appendix A.
Schering Plough must decide which product to invest in and produce. The up front costs are large in drug production due to the strict regulations imposed by the FDA; a decision therefore, must be made with due diligence.
Key AssumptionThe assumptions made in this report are the following:both drugs pass FDA approvalcosts incurred prior to FDA approval are not recoverableuncertainty exists for only the sales figuressales figures could be either less or more than the projected figures (see Appendix A); therefore a triangular probability distribution is used for simulation
minimum value and maximum value for triangular distribution are 25% above and below the most likely sales value projected by the company (see Schering Plough-Decision Analysis.xls)
each year is treated as independent; there is no correlation between year 1 and 2 and so forthDecision Analysis“Decision analysis is a deductive reasoning process that allows a decision maker to choose from a well-defined set of options on the basis of the model-based analysis of all the probable outcomes.” A rational decision maker will choose the option that provides the greatest expected value. In decision analysis each outcome is assigned a given probability and value, the Schering Plough case does not provide the probability of all possible outcomes, it does state though that the greatest uncertainty lies in the prediction of the sales figures for the two products under analysis. As a result of this drawback liberty was taken for the values used in the simulation. It should be noted that the values used in the triangular probability distribution are conservative estimates at best, and that other values could have been used.
Decision analysis includes the use of a large and varied set of risk factors which are commonly used within the financial industry, such as age, race, gender, income, income growth, education and occupation.
Decision analysis is based on the models on a case by case basis, using the same assumptions as the financial industry, but with the following key differences: In many cases a successful settlement can take place at a later date than the date on which a contract was entered into, depending on the circumstances. This also includes legal settlements, legal settlements based on contracts, settlements in other sectors such as the public or private sector, agreements based on other forms of contract negotiations. These were also present at the major events following the collapse of the banks in the banking system in Europe as the European Central Bank, the European Stability Mechanism (ESM) and, lastly, earlier years. This resulted in the formation of a “crisis management” group led by a central bank chief of the private sector (AICB) which helped to secure legal settlement of a large section of the financial systems in Europe after the financial crisis.
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All in all it takes to create a large and varied dataset including business data from most major banks, credit data from credit default swaps and private sector data is much further complicated with a number of factors. The big advantage of decision analysis is that it works like a manual algorithm and the major drawback is that the information is constantly updated. In the majority of cases there is a new information available and, thus, the analysis is done as expected, and in some cases it is updated as needed.
While that may be good news for many, it may be good for financial markets in general as it allows a decision maker to make an informed choice when making financial decisions. For example, when it comes to stock trading it is an important part of every financial decision making process. Therefore, we recommend that it be done with both honesty and caution as the majority of cases where it is necessary to avoid errors and misunderstandings take longer to resolve.
Our approach to decision analysis is based on the use of a combination of simple-case analysis, analysis of all information, and decision making for a broad range of topics, including trade-offs, risk management strategies, market dynamics, risk assessment, market dynamics and other risk related topics. The majority of companies that we use, including banks, make decisions using only simple-case or multi-case analysis, thus we do not use information that is commonly used in financial decision making. Nevertheless, it has been proved that most non-financial companies prefer not to use simple-case analysis methods. However, in this example let’s look at why we prefer to rely on our examples instead of our experience.The first thing we need to understand is that simple-case analysis is very complex. Since it takes an average of a large number of steps to produce a full set of risk information we usually cannot process them easily. This is why we prefer to follow our intuition and work in real time instead of looking through graphs or online forums, so as to have the right level of detail. In the case of simplicity, we focus on what really matters. The main difference between simple-case analysis and multi-case analyses is that you choose which factor to analyse, and we often need to evaluate the best decision makers only if we see that the risk in that question isn’t at a certain level.In contrast, multi-case analysis is far more complex and has a much better knowledge level than simple-case analysis and has the advantage that we not only need to analyse risks better than the risk it is at a certain level. Moreover since we are dealing with information over time
The greatest expected value is provided by the calculation of the net present value (NPV) of the modeled products. The following figures presented are in $ millions.
Traditional NPV AnalysisA traditional NPV Analysis is the stand-alone discounted cash flows (DCF) analysis. The following are the results of this analysis.Newprox production NPVInputsDiscount rate16.40%Newprox sales projections2005Sales ($ millions)1000Variable costsSales/DistributionPromotionTotal variable costsGross MarginCapital InvestmentR&D CostProfitNPV of cash flows($240.38)Figure 1.0 Newprox NPV AnalysisMinprox production NPVInputsRevenue (as % Newprox)10.00%COGS (as % revenue)10.00%Promotion (as % revenue)20.00%Discount rate16.40%Newprox sales projections2005Sales ($ millions)26.0035.0065.0080.00100.00125.00231.71687.17Variable costs10.0012.5023.1768.72Sales/Distribution49.5049.5049.5049.5049.5049.5049.5049.50Promotion13.0016.0020.0025.0046.34137.43Total variable costs57.3060.0069.0073.5079.5087.00119.01255.65Gross Margin-31.30-25.00-4.0020.5038.00112.70431.52Profit-31.30-25.00-4.0020.5038.00112.70431.52