Technological Progress and Technology Acquisition: Strategic Decision Under Uncertainty

Lévesque

2013

This special issue contains articles that exemplify the role of operations management across the entrepreneurial value chain. This value chain encompasses all stages of the entrepreneurial phenomenon, including technology commercialization, where discovery, commitment, organization, and growth must take place. We report on a literature search that identifies research questions categorized with respect to topics crucial to operations management scholars and classify these questions under each stage of this value chain. The search guides the development of an evolutionary path for the use of resources, routines, and reputation (3Rs), often lacking in this process, and enables us to propose modeling and topical gaps in the literature. We offer a framework to set up exemplars for operational tradeoffs uniquely associated with the entrepreneurial value chain. We also articulate how five contributed articles in this issue tackle some of these tradeoffs, prior to introducing four perspective pieces. We hope this discussion motivates follow‐on work and triggers a significant increase in the flow of articles that make it to both entrepreneurship and operations management top‐tier academic and practitioner publications.

Section: Operations Management Researchmentioning

confidence: 99%

The Role of Operations Management Across the Entrepreneurial Value Chain

Lévesque

2013

“…Investment in process R&D and allied cost reduction and capacity management decisions, by established firms, have long been key issues in the management of technology literature associated with operations realm (Carrillo and Gaimon 2000, Chambers and Kouvelis 2003, Gaimon 1989, see especially the review by Gaimon 2008). A relevant literature stream explores the technology adoption decisions (Erat and Kavadias 2006, Fine and Freund 1990, Gupta and Loulou 1998, Li et al 2003, and another aligned stream of literature investigates process improvement and learning (Lapre et al 2000, Li and Rajagopalan 1998, Terwiesch and Xu 2004. For instance, Li and Rajagopalan (2008) consider the value of early investment in process improvement and learning, and how it creates an option to invest in process in future periods for established firms.…”

Section: Literaturementioning

confidence: 99%

Production, Process Investment, and the Survival of Debt‐Financed Startup Firms

Tanrısever

Erzurumlu

2012

Whether to invest in process development that can reduce the unit cost and thereby raise future profits or to conserve cash and reduce the likelihood of bankruptcy is a key trade‐off faced by many startup firms that have taken on debt. We explore this trade‐off by examining the production quantity and cost reducing R&D investment decisions in a two period model wherein a startup firm must make a minimum level of profit at the end of the first period to survive and operate in the second period. We specify a probabilistic survival measure as a function of production and investment decisions to track and manage the risk exposure of the startup depending on three key market factors: technology, demand, and competitor's cost. We develop managerial insights by characterizing how to create operational hedges against the bankruptcy risk: if a startup makes a “conservative” investment decision, then it also selects an optimal quantity that is less than the monopoly level and hence sacrifices some of first period expected profits to increase its survival chances. If it decides to invest “aggressively,” then it produces more than the monopoly level to cover the higher bankruptcy risk. We also illustrate that debt constraint shrinks the decision space, wherein such process investments are viable.

“…An example specification for such a model is shown in Table 3. Huchzermeier and Loch (2001), Kavadias and Loch (2003), and Li et al (2003) have specified models that could be used as the basis for building this level of analysis. Extensions to adapt these models to the HPDP framework, such as modifying them to become a rolling horizon model and the adding in an "extraordinary budget" recourse decision variable, are straightforward.…”

Section: Strategic Planning: Portfolio Selectionmentioning

confidence: 99%

A Hierarchical Product Development Planning Framework

Anderson

2005

Uncertainty in new product development (NPD) planning embraces market, creative, technological, and process dimensions to a much greater extent than in non‐NPD project planning. Yet, NPD management is becoming increasingly decentralized, both within the firm and across the supply chain. Hence, planning for NPD uncertainty often results in path‐dependent scenarios cutting across the strategic, tactical, and operational levels of planning. To coordinate this resulting complexity, we propose a stochastic hierarchical product development planning framework with multiple recourses, i. e., corrective actions, to maximize performance across a firm's entire NPD program. We also argue the necessity for a fourth planning level, the infrastructural, that reestablishes norms for market projections, technological forecasts, scheduling, and requirements as latent uncertainty in the environment is continually revealed. An illustration from the automotive industry is presented to demonstrate a deployment of our framework. We additionally discuss the applicability of this framework for managing NPD capabilities over time.