Industry Examples

DPL Example Models by Industry

Top organizations spanning a wide range of industry verticals have employed DPL's robust set decision and risk analytic tools to maximize the value of their investments through smart, calculated decisions with high returns and well-understood risks.

DPL's intuitive modeling interface is paired with the most powerful analytic engine on the market making it the the obvious solution choice for your organization's next major strategic decision. Whatever your industry vertical -- DPL can help you decide!

Select an industry vertical below to view real-world DPL examples:

Life Sciences Examples

Pharmaceutical R&D Prioritization »

More than any other factor the choice of R&D projects defines the future of a life sciences company. Unfortunately all the easy blockbusters are done, and creating value with a project portfolio requires a keen understanding of clinical, market and regulatory risk. DPL helps R&D planners structure the alternatives and uncertainties around these choices to ensure that an organization’s research funds are spent most wisely.

Pharmaceutical Business Development & Licensing »

Bringing in the right product at the right price sounds simple enough, but the challenges of high uncertainty, limited information and tight timescales make it a hard problem. Getting the value wrong, or just being too slow, can mean ceding a gem to a competitor. Honed in a specialized valuation group at a leading professional services firm, DPL is designed to take on these problems and produce clear, timely results.

Medical Device Regulatory Vigilance »

Regulatory institutions within the life sciences sector are faced with the critical decision to approve, reject, or require further study for thousands of medical devices each year. Those most invasive must pass through a rigorous analysis weighing the potential health benefits against the risks -- a process that must be both consistent and transparent. DPL offers these bodies a comprehensive and consistent analytics that can generate a full suite of transparent results.

Pharmaceutical Drug Sales Forecasting »

Markets have cycles and routine volatility so a deterministic point forecast of future sales is sure to be wrong. But we you are far from helpless in the face of uncertainty. A robust forecast -- one that can only come from assessing (and not the shrugging off) key factors like dosing, competitor outcomes, and pricing -- can help organizations better manage operations and provide clear external guidance that builds investor confidence.

Oil and Gas Examples

Oil and Gas Development & Export Strategy »
Whether you're producing oil, gas or minerals, it has no value until it's brought to market. With the pursuit of deposits in increasingly inconvenient places, consideration of the costs and risks of transportation are often a major driver of the viability of a project.

Analyzing a LNG Regas Opportunity »
The final investment decision to build, convert, or increase capacity is plagued by a variety risks and uncertainties -- including regulatory hurdles, commodity prices, natural gas price volatility and market swings. DPL can provide a comprehensive, coherent framework for incorporating all these drivers of uncertainty and value, and produce a transparent, defensible recommendation.

Oil and Gas Exploration »
To drill or not to drill? Sounds simply enough - but in reality, a wildcatter has more choices than just whether to drill or not. They have the opportunity to conduct one of several tests intended to provide imperfect information on the likelihood of finding oil and how much.

Sequential Drilling Decisions »
A common and challenging decision problem in the oil & gas industry is to decide how to explore an oil field. Typically there is a cluster of prospects, giving rise to a highly dependent set of uncertainties, according to their proximity and the geology of the area. Luckily, the DPL Enterprise version includes a few clever features that makes it easier to capture the sequential learning derived from a cluster of dependent appraisal wells.

Infrastructure Examples

Valuing a Highway Concession with Real Options »

Capital may be readily available for infrastructure investments but technical hurdles, price volatility and political risks often make the final investment decision difficult. A DPL model provides a comprehensive, coherent framework for incorporating all the drivers of uncertainty and value. Further, results include an insightful dynamic roadmap so you can visualize how risk, value and options play out over time.

Manufacturing Examples

Capacity Expansion: Build vs. Expand »

When a manufacturer is unable to fulfill the needs of their customers with current facilities, they generally have two alternatives to choose from: expand the current facility or build a new one. Sounds simple enough, but there are variety of factors that should be taken into account when deciding on capacity expansion. The example model identifies the optimal decision strategy given the uncertainty surrounding product demand, capacity level, Capex, COGS, pricing, tariffs, and supply disruptions.

Mining Examples

Valuing a Mining Switch Option »

Under current depressed commodity price conditions many mining projects are financially marginal. This calls for a shift from a focus on economies of scale, which usually involves significant capital investments and inflexible modes of operation, to one that focuses on up front investments for enhancing operational flexibility -- making it easier and less costly to temporarily close and re-open marginal mines in response to price volatility. DPL can provide the real option value of this switching option so you can decide on the amount that should justifiably be invested up-front to create these vital operational flexibilities.

New Product Development Examples

Resource Allocation Strategy for Market Entry »

Key market and technical uncertainties can make or break a product entrance and must be evaluated when considering alternative development plans for bringing products to market. Do you go to market quickly with a less robust product or delay launch in favor of providing something feature-rich? DPL is used to help decide between competing development plans, different designs and to structure market research plans in order to provide companies with the best possible decision scenario for launching new products.

License vs. In-house »

This is a straightforward new product development example in which a new product is nearly ready for launch. There is a decision on whether the new product should be produced in-house and sold directly or if it should be licensed it to another company. There are several uncertain drivers that will have an impact on NPV including sales, costs, price and license fees. Furthermore, some drivers only pertain to one decision alternative. For example, we only receive license fees if we choose to license the product. The workspace includes 4 models that show the model build out in steps: from a simple, deterministic model with a single decision to a two-period probabilistic model that includes a downstream decision.

Utilities Examples

Investing in a Cogeneration Plant »

Given the increasing demand for clean and renewable sources of energy in today's world, there is significant potential for investment and further development of bioelectricity cogeneration power plants. But when is the right time to invest? DPL provides a intuitive framework for modeling the various uncertainties and embedded flexilibities inherent in these investment decisions -- so you can anticipate the added value and the optimal time to expand/build/retrofit.

Defense Examples

Adversarial Problem: Airport Defense Strategy »

Imagine you are tasked with making a decision as to which, if any, airports to defend against attack in the US. Your alternatives are to defend none, the top 10, or the top 50. Your objective for the decision is to minimize fatalities and costs. While you'd like to defend all airports and incur no fatalities, that simply isn't a feasible alternative in the real world. DPL Enterprise allows you to model the objectives of multiple non-aligned decision makers so you can gain a fuller picture of the problem.

Environmental Examples

Environmental Clean-up Problem

Some decisions problems require the careful consideration of multiple value measures. The analysis of these decision problems is called multi-attribute utility analysis (MUA). Consider a situation where you are deciding what to do about a site with environmental contamination. Solely minimizing cost will likely not result in a good decision or outcome. In this problem, three attributes are defined and tracked within the model. Furthermore, the model employs weights to combine the multiple attributes into an overall measure of value.

Classic Problems Examples

The Monty Hall Problem

For this classic problem a contestant is presented with three closed doors. Behind one of the doors is a brand new car, while behind the other two doors are goats. After the contestant picks one of the doors, the host, Monty Hall, instructs a stage hand to open one of the other two doors, and behind it lies a goat. Now there are two remaining closed doors, one with a goat and one with a car, and the host gives the contestant the option to switch. Should the contestant switch? Overall, what is the probability that the contestant will win the car?