Uncertainty is a persistent characteristic of the modern economy. Decision makers in business often struggle with the multi-factorial aspects of uncertainty, emanating from internal sources — such as costs of research and development (“R&D”), production and logistics, time needed to complete tasks and projects as well as technical risks such as failure of prototypes, technology, and equipment. Additionally, most decisions depend on market and external uncertainties such as price, demand, availability, economic growth, competition, and policy. Recently, shocks that are not market based but have a broad impact on the economy such as terrorism and severe weather, have also surfaced. To make matters more complicated, some of these uncertainties are time-varying such as commodity costs, product prices, economic growth, inflation and interest rates, most of which may show a correlation with the market. Uncertainties that are probabilistic in nature, such as the probability of success of an R&D program and the chance of machine failure in production, are uncorrelated with the market and need special consideration.
In this regime of hyper-uncertainty, allocation of resources and capital into projects, departments and locations, has become increasingly more difficult in almost any industry. Good decision-making, however, needs to take into account both uncertainty and decision-flexibility, the ability to initiate, stage, delay, accelerate, slow-down, abandon, switch, hibernate, redesign, change, shrink, restart or redo, a complex process that experienced managers master over time. Traditional decision aids, largely based on the discounting of cash flows based on uncertainty (risk), are not useful either due to its inability to capture the strategic aspects of the decisions or due to high complexity resulting in analysis-paralysis, confusion, and a lack of confidence. Because of this, decision-makers often rely on intuition and gut-feel to guide organizations in the right direction. As industries converge, economies integrate and product-and-technology life-cycles shorten, experience based intuition has become less effective in making decisions concerning a complex portfolio of products, technologies, and services. In this environment, decisions also have to be dynamic and companies need to be nimble, to take advantage of emerging opportunities and to optimally abandon unattractive investments and assets. Although replicating good management intuition is virtually impossible, it is increasingly important to aid such competence with an appropriate use of technology and tools.
Financial analyses conducted to support decisions often suffer from methodology confusion and lead to incorrect and prescriptive guidance. For example, the established theory, capital asset pricing model (CAPM), requires market and private risks to be treated differently and investment decisions to be separated from financing decisions. In practice, however, a discount rate, typically based on the weighted average cost of capital (WACC), is used to account for all risks. As such, the Net Present Value (NPV), derived from a financial modeling exercise, is not a metric that is consistent with the underlying theory. This also requires a precise forecasting of cash flows, something that is not possible for most projects. In some cases, companies have attempted to circumvent this issue by scenario analyses or even Monte Carlo simulation — producing more interesting outputs but they are not necessarily useful in improving decisions. It has been noted that good decision-makers make qualitative adjustments to the calculated NPV of projects to ameliorate the issues noted. The perennial question remains to be why companies expend such effort in the calculation of a metric that does not satisfy the theory or provide robust decision guidance. Without these wasted efforts, companies may be able to substantially reduce the time to decision without much difference to end outcomes. Alternately, companies may want to consider methodologies and tools that are able to accommodate different types of uncertainties in decision processes and incorporate good management intuition by considering available decision choices. Such a process also reduces complexity and provides a systematic and consistent framework, applicable across all decisions.
Another impediment to good decision-making in complex organizations is the lack of transparency in assumptions, emanating from different departments — R&D, manufacturing, marketing, and finance — each holding a different perspective but unable to communicate across the boundaries. As such, different models emerge, often in conflict with each other, making decision-making more of a negotiation process. More importantly, such a process is not typically conducive to the consideration of alternatives and selecting one that contributes maximally to shareholder value. Negotiations and subjective processes often lead to suboptimal outcomes with selected strategies dominated by power rather than anything else. What is needed here is a process that allows assumptions to flow from segmented departments, collaborative modeling and a technology that encourages the consideration of alternatives.
The assumptions used in financial models that aid decisions often reveal systematic biases that exist in the company. Because such models are often forgotten after a decision is made, organizations typically do not revisit and audit the assumptions used to understand any biases that may have existed. Doing this across all decisions will allow companies to avoid being too optimistic or pessimistic in certain parameters routinely used in models and avoid perpetuating mistakes. If all decisions made by a company goes through a singular process and technology, something that allows documentation of assumptions and decisions, it may allow ex-post evaluation and any necessary corrections to the process. It is also the case that companies do not use all existing internal data and any available external data that may be relevant to the decision, because it requires significant effort to identify, accumulate, organize and analyze. With the advent of big data technologies and cheap computing power, much of this hurdle to analysis has been lifted. However, it is important to focus on the reduction of available data into the most relevant information, rather than collecting all data in anticipation of use. The latter has led many companies down blind alleys and wasted investments, especially in big data and storage technologies, most of which remain to be irrelevant in a business context.
To improve contemporary decision-processes, what is needed is a methodology, process, and technology, that allows organizations to view all decisions — strategic, operational, and financial, in any domain — R&D, marketing, manufacturing, logistics, and finance — in a consistent fashion. Such a process will allow companies to dramatically simplify decision processes — condensing decision metrics to just three — economic value, downside risk and upside potential. The methodology should be universally applicable — in any type of decision, harboring any type of uncertainty and flexibility and even in the absence of such. The technology should allow a systematic documentation of assumptions, typically represented in uncertain terms, eliminating the need for precise estimation of cash flows (an oxymoron for strategic decisions).
One such process and technology is Decision Options®, designed and popularized over the past two decades. It incorporates the necessary conditions as mentioned above. It also provides predesigned rich constructs — such as options, swaps, and technical risks — that make modeling of any level of complexity in financial or real assets, fast and simple. It can also consume structured or unstructured data and even opinions and surveys and produce predictive and probabilistic metrics that underlie the decision sequence. The process allows experts, analysts and decision-makers to collaborate and provide relevant information to aid decisions most likely to enhance shareholder value and reduce enterprise risk.
Decision Options® technology and process have been used in many industries in the past two decades including life sciences, aerospace, energy, healthcare, financial services, and manufacturing. In life sciences, it has been used to portray an entire portfolio of drug products and protocols in a framework of value, risk and potential, allowing decision-makers to select the best portfolio to pursue. In aerospace, it has been used to design space vehicles that incorporate the best technologies from a plethora of available choices. In energy it has been used to value hybrid production plants considering all interrelationships among interacting variables such as power prices and fuel costs. In healthcare, it has been used to determine optimal patient interventions and to design operating modalities for providers. In manufacturing it has been utilized to optimize network capacity and design supply chain contracts. In financial services it has been used to value complex structured instruments that do not have price discovery in the market place. In all cases, the process starts from the decision to be made and the modeling of the decision problem, describing various uncertainties that are relevant, utilizing existing or forecasted data, and analyzing the problem using market based principles to produce value, downside risk and upside potential.
Uncertainty is often considered bad in business. However, a systematic consideration of flexibility in conjunction with unavoidable uncertainty, enhances the value of the firm. Conventional financial tools have not been useful in this regard even though they consume significant time and effort in most companies. Decision-makers have been making adjustments to counteract the deficiencies revealed in discounted cash flow analyses in an effort to make better decisions. However, as data and complexity grow in a fast changing world, it is becoming nearly impossible to consider all available information while making decisions.
These ideas are not new and leading companies in life sciences, energy, hi-technology, aerospace and financial services, already incorporate many of the concepts described here. However, those, with a good appreciation of the value of the combination of uncertainty and decision-flexibility, often struggle to implement them in practice. But it has now become a necessary condition for companies to survive and thrive. Most of the value in the modern economy emanates from companies who take advantage of uncertainty by innovation, collaboration and contracts. Although good management intuition is important for taking advantage of available opportunities, there are technologies and processes available to aid such qualitative decision-making. Companies that can institute processes that allow the flow of assumptions across departmental boundaries, methodologies that are applicable in all decisions and technologies that allow consideration of all choices, can substantially improve shareholder value.
The ability to create and evaluate alternatives is a critical need for innovative companies. Reaching fast decisions while maintaining decision quality is equally important. Industry leaders are already well on their way. It is the fiduciary responsibility of leaders of every company to give sufficient attention to how their limited resources and capital are deployed across a plethora of available opportunities.
CEO – Decision Options, LLC