Juuso Liesiö

Cost-Efficiency Analysis of Weapon System Portfolios

Decisions about the acquisition and maintenance of military equipment serve to build long-term capabilities in preparation of military conflicts. Typically, these decisions involve large investments which need to be supported by adequate cost-efficiency analyses. Yet the cost-efficiency analysis of weapon systems involves several challenges: for example, it is necessary to account for the possible interactions among different weapon systems; the relevance of several impact criteria; and the variety of combat situations in which these systems may be used. In this paper, we develop a portfolio methodology where these challenges are addressed by evaluating the cost-efficiencies of entire portfolios consisting of individual weapon systems. Our methodology accounts for possible interactions among systems by synthesizing impact assessment results that are either generated by combat simulation models or elicited from experts. It also admits incomplete preference information about the relative importance of different impact criteria. This methodology guides decision making by identifying which combinations of weapon systems are efficient with respect to multiple evaluation criteria in different combat situations at different cost levels. It can also be extended to settings where multiple combat situations are addressed simultaneously. The methodology is generic and can therefore be applied also in civilian settings when portfolios of activities (such as mitigation of harmful environmental emissions) may exhibit interactions.

Selecting infrastructure maintenance projects with Robust Portfolio Modeling

Project portfolios for the annual maintenance of infrastructure assets may contain dozens of projects which are selected out of hundreds of candidate projects. In the selection of these projects, it is necessary to account for multiple evaluation criteria, project interdependencies, and uncertainties about project performance as well as financial and other relevant constraints. In this paper, we report how Robust Portfolio Modeling (RPM) has been used repeatedly at the Finnish Transport Agency (FTA) for bridge maintenance programming. At FTA, project selection decisions are guided by the RPMs Core Index values which are derived from portfolio-level computations and reflect incomplete information about the relative importance of evaluation criteria. To-date, this application has been rerun with fresh data for six consecutive years. By drawing on experiences from this application, we discuss preconditions for the successful use of RPM or other methods of Portfolio Decision Analysis in comparable settings. We also develop an approximative algorithm for computing non-dominated portfolios in large project selection problems. We report a repeated application of RPM to bridge maintenance project selection.We identify general features that contributed to the success of this application.We develop an algorithm for solving non-dominated portfolios in large RPM problems.

Baseline value specification and sensitivity analysis in multiattribute project portfolio selection

A key issue in applying multi-attribute project portfolio models is specifying the baseline value – a parameter which defines how valuable not implementing a project is relative to the range of possible project values. In this paper we present novel baseline value specification techniques which admit incomplete preference statements and, unlike existing techniques, make it possible to model problems where the decision maker would prefer to implement a project with the least preferred performance level in each attribute. Furthermore, we develop computational methods for identifying the optimal portfolios and the value-to-cost -based project rankings for all baseline values. We also show how these results can be used to (i) analyze how sensitive project and portfolio decision recommendations are to variations in the baseline value and (ii) provide project decision recommendations in a situation where only incomplete information about the baseline value is available.

Adjustable robustness for multi-attribute project portfolio selection

Robust Portfolio Modeling (RPM) supports multi-attribute project portfolio selection with uncertain project scores and decision maker preferences. By determining non-dominated portfolios for all possible realizations of uncertain parameters, decision recommendations produced by RPM may prove too conservative for real-life decision problems. We develop a methodology to reduce the set of possible realizations by limiting the number of project scores that may simultaneously deviate from their most likely value. By adjusting this limit, decision makers can choose desired levels of conservatism. Our approach also allows to capture dependencies among project scores as well as uncertainty in portfolio constraints.

Optimal Strategies for Selecting Project Portfolios Using Uncertain Value Estimates

Practically all organizations seek to create value by selecting and executing portfolios of actions that consume resources. Typically, the resulting value is uncertain, and thus organizations must take decisions based on ex ante estimates about what this future value will be. In this paper, we show that the Bayesian modeling of uncertainties in this selection problem serves to (i) increase the expected future value of the selected portfolio, (ii) raise the expected number of selected actions that belong to the optimal portfolio ex post, and (iii) eliminate the expected gap between the realized ex post portfolio value and the estimated ex ante portfolio value. We also propose a new project performance measure, defined as the probability that a given action belongs to the optimal portfolio. Finally, we provide analytic results to determine which actions should be re-evaluated to obtain more accurate value estimates before portfolio selection. In particular, we show that the optimal targeting of such re-evaluations can yield a much higher portfolio value in return for the total resources that are spent on the execution of actions and the acquisition of value estimates.

Newsvendor decisions under supply uncertainty

We analyse the impact of supply uncertainty on newsvendor decisions. First, we derive a solution for a newsvendor facing stochastic supply yield, in addition to stochastic demand. While earlier research has considered independent uncertainties, we derive the optimal order quantity for interdependent demand and supply and provide a closed-form solution for a specific copula-based dependence structure. This allows us to give insights into how dependence impacts the newsvendor’s decision, profit and risk level. In addition to the theory, we present experimental results that show how difficult newsvendor decisions under supply uncertainty are for human subjects. In our experiment, the control group replicated a well-known newsvendor experiment, whereas the test group faced additional supply yield uncertainty. Comparison of these results shows that under low-profit condition, subjects are able to incorporate supply uncertainty quite well in their decisions. Under high-profit condition, the deviation from the optimum is much more significant. We discuss this asymmetry and also propose some ways to improve newsvendor decision-making.

A Resource Allocation Model for R&D Investments: A Case Study in Telecommunication Standardization

Industrial firms need to adjust their R&D activities in response to changing perceptions about the business relevance and success probabilities of these activities. In this chapter, we present a decision model for guiding the allocation of resources to a portfolio of R&D activities. In our model, the dynamic structure of the decision problem is captured by decision trees, and interval estimates are employed to describe uncertainties about the sales parameters. Possible interactions among the activities – such as synergy and cannibalization effects – are accounted for by approximating their impact. We also describe how this model was deployed in a major telecommunication company and how the company has adopted the model into regular and extensive operational use when allocating resources to standardization activities.

A methodology for the identification of prospective collaboration networks in international R&D programmes

The planning of publicly funded research and development programmes can benefit from participatory foresight processes where research issues are evaluated with regard to multiple criteria. However, few approaches have been developed for the shaping of collaborative research networks through which the resulting priorities are implemented. We therefore develop a methodology for the joint shaping of thematic priorities and prospective collaborative networks. Our methodology helps identify networks that are aligned with the thematic priorities and consist of research groups with shared interests. The proposed PRM-Networking approach is demonstrated with a case study on the planning of a multi-national research programme.

Forecasting replenishment orders in retail: value of modelling low and intermittent consumer demand with distributions

In retail, distribution centres can forecast the stores’ future replenishment orders by computing planned orders for each stock-keeping-unit. Planned orders are obtained by simulating the future replenishment ordering of each stock-keeping-unit based on information about the delivery schedules, the inventory levels, the order policies and the point-estimate forecasts of consumer demand. Point-estimate forecasts are commonly used because automated store ordering systems do not provide information on the demand distribution. However, it is not clear how accurate the resulting planned orders are in the case of products with low and intermittent demand, which make up large parts of the assortment in retail. This paper examines the added value of modelling consumer demand with distributions, when computing the planned orders of products with low and intermittent demand. We use real sales data to estimate two versions of a planned order model: One that uses point-estimates and another that uses distributions to model the consumer demand. We compare the forecasting accuracies of the two models and apply them to two example applications. Our results show that using distributions instead of point-estimates results in a significant improvement in the accuracy of replenishment order forecasts and offers potential for substantial cost savings.

Scenario-based portfolio model for building robust and proactive strategies

In order to address major changes in the operational environment, companies can (i) define scenarios that characterize different alternatives for this environment, (ii) assign probabilities to these scenarios, (iii) evaluate the performance of strategic actions across the scenarios, and (iv) choose those actions that are expected to perform best. In this paper, we develop a portfolio model to support the selection of such strategic actions when the information about scenario probabilities is possibly incomplete and may depend on the selected actions. This model helps build a strategy that is robust in that it performs relatively well in view of all available probability information, and proactive in that it can help steer the future as reflected by the scenarios toward the desired direction. We also report a case study in which the model helped a group of Nordic, globally operating steel and engineering companies build a platform ecosystem strategy that accounts for uncertainties related to markets, politics, and technological development.