Markku Heikkilä

A fuzzy approach to R&D project portfolio selection

A major advance in the development of project selection tools came with the application of options reasoning in the field of Research and Development (R&D). The options approach to project evaluation seeks to correct the deficiencies of traditional methods of valuation through the recognition that managerial flexibility can bring significant value to projects. Our main concern is how to deal with non-statistical imprecision we encounter when judging or estimating future cash flows. In this paper, we develop a methodology for valuing options on R&D projects, when future cash flows are estimated by trapezoidal fuzzy numbers. In particular, we present a fuzzy mixed integer programming model for the R&D optimal portfolio selection problem, and discuss how our methodology can be used to build decision support tools for optimal R&D project selection in a corporate environment.

Fuzzy entropy used for predictive analytics

Process interruptions in (very) large production systems are difficult to deal with. Modern processes are highly automated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from the very large sets of data. Feature selection is intended to reduce the complexity of identifying cases with high possibility of failure by excluding numerous factors in the process systems. We use fuzzy entropy as the basis of a feature selection method and we show how the outcome of feature selection can be utilized to further failure prediction steps.

Improving Investment Decision Making by Expanding Key Knowledge with Real Option Tools

Assessment of investment profitability with discounted cash flows has proven insufficient when industry structures change. Managers need an extended flexibility in decision–making that DCF analyzes cannot offer. Real Option valuation as a method for improving flexibility in investment decisions has been widely researched but less used in business. With good support tools it is possible for managers to cross the threshold to Real Option methods and thinking. Real Option spreadsheet tools support both decision–making processes and business learning. They utilize the flexibility that new key knowledge brings to volatile industry situations and lead to better investment decisions.

Possibilistic Bayes modelling for predictive analytics

Studies in the process industry (and also common sense) show that the most cost effective way to keep production processes running is through predictive maintenance, i.e. to carry out optimal maintenance actions just in time before a process fails. Modern processes are highly auto-mated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from very large sets of data. Modern analytics develops algorithms that are fast and effective enough to create possibilities for optimal JIT (Just-in Time) maintenance decisions.

Fuzzy Real Options Models for Closing/Not Closing a Production Plant

In traditional investment planning investment decisions are usually taken to be now-or-never, which the firm can either enter into right now or abandon forever. The decision on to close/not close a production plant has been understood to be a similar now-or-never decision for two reasons: (i) to close a plant is a hard decision and senior management can make it only when the facts are irrefutable; (ii) there is no future evaluation of what-if scenarios after the plant is closed. However, it is often possible to postpone,modify or split up a complex decision in strategic components, which can generate important learning effects and therefore essentially reduce uncertainty. If we close a plant we lose all alternative development paths which could be possible under changing conditions; on the other hand, senior management may have a difficult time with shareholders if they continue operating a production plant in conditions which cut into its profitability as their actions are evaluated and judged every quarter. In these cases we can utilize the idea of real options. The new rule, derived from option pricing theory, is that we should only close the plant now if the net present value of this action is high enough to compensate for giving up the value of the option to wait. Because the value of the option to wait vanishes right after we irreversibly decide to close the plant, this loss in value is actually the opportunity cost of our decision. In this work we will use fuzzy real option models for the problem of closing/not closing a production plant in the forest products industry sector.