Real Options Logic Revisited: The Performance Effects of Alternative Resource Allocation Regimes

The tumultuous inception of an epidemic is usually accompanied by difficulty in determining how to respond best. In developing nations, this can be compounded by logistical challenges, such as vaccine shortages and poor road infrastructure. To provide guidance towards improved epidemic response, various resource allocation models, in conjunction with a network-based SEIRVD epidemic model, are proposed in this article. Further, the feasibility of using drones for vaccine delivery is evaluated, and assorted relevant parameters are discussed. For the sake of generality, these results are presented for multiple network structures, representing interconnected populations—upon which repeated epidemic simulations are performed. The resource allocation models formulated maximise expected prevented exposures on each day of a simulated epidemic, by allocating response teams and vaccine deliveries according to the solutions of two respective integer programming problems—thereby influencing the simulated epidemic through the SEIRVD model. These models, when compared with a range of alternative resource allocation strategies, were found to reduce both the number of cases per epidemic, and the number of vaccines required. Consequently, the recommendation is made that such models be used as decision support tools in epidemic response. In the absence thereof, prioritizing locations for vaccinations according to susceptible population, rather than total population or number of infections, is most effective for the majority of network types. In other results, fixed-wing drones are demonstrated to be a viable delivery method for vaccines in the context of an epidemic, if sufficient drones can be promptly procured; the detrimental effect of intervention delay was discovered to be significant. In addition, the importance of well-documented routine vaccination activities is highlighted, due to the benefits of increased pre-epidemic immunity rates, and targeted vaccination.

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Just-in-Time system in terms of real options

Competitio ◽

10.21845/comp/2004/1/10 ◽

2020 ◽

Vol 3 (1) ◽

Author(s):

Andrea Rozsa

Keyword(s):

Resource Allocation ◽

Real Options ◽

Best Practice ◽

Real Option ◽

Corporate Strategy ◽

Capital Budgeting ◽

Just In Time ◽

The Real ◽

Practice Approach ◽

Physical Assets

The value creation process in a company and the competitive position are critically influenced by corporate resource allocation and proper valuation of investment alternatives. After the Second World War, capital budgeting and strategic planning emerged as two complementary but different systems for resource allocation. The real options approach developed in the ’80s may provide a useful tool for making a connection between capital budgeting and strategic management. Real options are implicit managerial and operating flexibilities embedded in many non-financial assets and liabilities. In a wider sense: “A real option is the investment in physical assets, human competence, and organisational capabilities that provide the opportunity to respond to future contingent events” (Kogut-Kulatilaka, 2001). This paper shows that Just-in-Time (JIT) system as management philosophy can be regarded as a knowledge-based or capability-based implicit strategy rather than a simple, easy-toimitate best practice approach. Moreover, implementation of JIT can be considered as a strategic investment. The presentation focuses on how the relation among strategic investments, developed technological systems and corporate strategy can be expressed through the real options view.

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Resource Allocation in Strategic Factor Markets: A Realistic Real Options Approach to Generating Competitive Advantage

Journal of Management ◽

10.1177/0149206316683778 ◽

2017 ◽

Vol 43 (8) ◽

pp. 2588-2608 ◽

Cited By ~ 18

Author(s):

Michael J. Leiblein ◽

John S. Chen ◽

Hart E. Posen

Keyword(s):

Resource Allocation ◽

Competitive Advantage ◽

Real Options ◽

Real Option ◽

Learning Ability ◽

Strategic Factor ◽

Feedback Learning ◽

Factor Markets ◽

Allocation Decisions ◽

Resource Allocation Decisions

This paper develops a realistic real option theory of resource allocation decisions in strategic factor markets. Competitive advantage in factor markets is underpinned by market failures that allow firms to acquire assets at less than their value in use. We recognize that market failure may result from uncertainty regarding the current and/or future value of an asset, which map, respectively, to uncertainty as modeled in the feedback learning and real options literatures. The realistic real option framework we develop grafts insights from the strategic factor market, feedback learning, and real option valuation literatures. We argue that competitive advantage may emerge not only from luck, or ex ante differences in information or complementary assets, but also because firms differ in a specific type of learning ability—the ability to integrate new information to exercise a contingent claim on an asset in a factor market. We dimensionalize these differences in terms of information processing and belief updating, argue that these differences lead to different resource allocation decisions, and suggest how these decisions may generate competitive advantage.

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