Revenue modeler

Pharmaceutical industry transition. The genetic diversity and the role and mode of practice that are currently caught in the transition between the revenue model of one blockbuster drug for everyone and the multiple personalized approaches that our differential genomics demands. 

To offer a certain combination of products and services advantageous to all partners, dynamic business models are necessary considering the special properties of IPS across the life cycle. An IPS business model covers a value proposition, the value architecture, and a revenue model. The value proposition describes the benefits and therefore the value a customer or a value partner gains from the business model. The aim of the value architecture is to create the promised customer benefit in an efficient way. The revenue model contains a description of a provider s sources and ways of revenue generation (Stabler 2002). 

The symbolic representation of this Revenue model is shown in Fig. C6.18. 

At this point, you might ask Why are we spending so much time on documenting these revenue models Do they have any special significance The answer is that their significance here is only as illustrations there would be an almost infinite number of possible, different models. These particular models were included for the sole purpose of demonstrating four important features of our methodology ... 

Keywords economic model, shareholder s profit, project cashflow, gross revenue, discounted cashflow, opex, capex, technical cost, tax, royalty, oil price, marker crude, capital allowance, discount rate, profitability indicators, net present value, rate of return, screening, ranking, expected monetary value, exploration decision making. 

A typical break-even chart is used with production models to predict optimum production levels, break-even points, and shutdown conditions under various scenarios. These models tend to involve a reasonable amount of approximation. For example, sales revenue as a function of production level involves numerous variables and relationships that are not always weU known. Such charts, however, provide useful guides for production operations. 

Textbooks on investment present a simple model where the net present value (NPV) of an investment equals annual future revenues [R) summed and discounted at the rate r, minus the initial investment cost, I. Using t as a time subscript to denote different years, the equation is... 

Drish, W. F., and Singh, S. (1992). Train Energy Model Validation Using Revenue Service Mixed Intermodal Train Data. Chicago Association of American Railroads. 

The trial was run, and FDA approval, on the basis of the results, was obtained. The drug is currently commercially successful. Were it not for the new team member who commissioned this work, this trial would have failed— at a cost of 50 million and the loss of two years of revenue. Moreover, other efficiencies (fewer patients, faster recruiting, better understanding of patient and market stratification) would not have been realized. The cost (in time and resources) for modeling projects should be balanced by the benefits of increased likelihood of success (for a drug that will be successful) and of possibly avoiding a trial for a compound that cannot succeed. 

The characteristics of the coatings Industry are such that most companies market many lines of products and within each line there are many individual products, each containing different ingredients. Usually the revenue from each product or each product line can not justify using this modeling/simulation approach in problem solving, because of the extent of technical efforts required. To be cost effective in our industry, only problems which cut across product lines justify the use of this approach. The two examples discussed here are selected to illustrate the types of problems which are amenable to this approach and the advantages and weaknesses of this approach in R. D. work. 

If a schedule is computed based on a model that does not consider uncertainties, this schedule can become suboptimal or even infeasible when the situation has changed. For example, a schedule can become suboptimal if a batch is unexpectedly of inferior quality and the revenues are a function of its quality. A schedule can become infeasible if there is an unexpected plant failure that reduces the plant capacity a batch has to be immediately transferred to another unit, but no unit is available. Then it is impossible to modify the infeasible schedule to a feasible one. 

The occurrence of the set-up procedure in period i is denoted by the binary variable Wi (0 = no, 1 = yes). The production costs per batch are denoted by p = 1.0 and the cost for a set-up is y = 3.0. Demands di that are satisfied in the same period as requested result in a regular sale Mi with a full revenue of a = 2.0 per unit of product. Demands that are satisfied with a tardiness of one period result in a late sale Mf with a reduced revenue of aL = 1.5 per unit. Demands which are not satisfied in the same or in the next period result in a deficit Bf with a penalty of a = 0.5 per unit. The surplus production of each period is stored and can be sold later. The amount of batches stored at the end of a period is denoted by Mf and the storage costs are a+ =0.1 per unit. The objective is to maximize the profit over a horizon of H periods. The cost function P contains terms for sales revenues, penalties, production costs, and storage costs. For technical reasons, the model is reformulated as a minimization problem ... 

The objective is to maximize the profit which is calculated by a cost model of sales revenues, production costs, storage costs, and penalties for lateness and for finishing line start-ups and shut-downs. The cost model adds some equality and inequality constraints with associated real valued variables for the sales, deficits, and the storage, but it does not further restrict the feasibility of the production decisions. 

Although uncertainty exists in the results of all cases of the optimization of plants because of the uncertainty in the values of the parameters in the process models themselves, in the cost and revenue values in the objective function, and in potential changes in the process inputs, we avoid such issues in this chapter and focus solely on deterministic optimization. 

Forecasting revenues fundamentally rests on models plus judgment. More formal methods project the trends of past revenues into the future adjusting for known or expected fluctuations. Typical models employed are... 

Revenue management is focused on demand forecasting - aggregated and disaggregated - demand distribution models or arrival processes to... 

A comprehensive analysis of these factors is beyond the scope of this work, but these influencers are often incorporated into the d5mamic model to more fully address the revenue implications of a move to the OTC market for a given compound. 

A common acconnting manipulation is to change the income recognition policies or bnsiness model to accelerate sales revenue. Businesses... 

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