Production, Manufacturing and Logistics
Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time

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Abstract

Shorter product life cycles, more rapid product obsolescence, and the increasing intensity of global competition have driven firms to strive for a more rapid introduction of new products to market. We introduce a normative model which yields insights concerning several key new product development (NPD) decisions. First, we examine investment strategies related to the timing and duration for investments in both design and process capacity over a given planning horizon. Second, the model offers guidance regarding the optimal time-to-market and ramp-up time necessary to meet peak demand for the new product. The model thus provides both theoretical and managerial insights into the crucial linkage between time-to-market and ramp-up time decisions. Finally, the implications of several specific NPD investment mechanisms on these NPD metrics are explored.

Introduction

The rapid proliferation of new technology and the increasing intensity of global and domestic competition have led to three important trends. First, shorter product life cycles have become increasingly evident for both high technology products and products not typically regarded as high technology (Stalk and Hout, 1990, Leonard-Barton et al., 1994). For example, product life cycles as short as 1 or 2 years are more prevalent, especially in the high technology, consumer electronics, and personal computer industries (Kurawarwala and Matsuo, 1996). Second, since new technologies are proliferating at an escalating rate, product obsolescence is occurring more quickly than in the past (Leonard-Barton et al., 1994), resulting in truncated life cycles with limited maturity stages. Third, the marketplace provides numerous incentives for the more rapid introduction of new products to market. An innovating first entrant gains a monopoly that yields premium prices until a competitor’s eventual entry drives prices down (Urban et al., 1986, Stalk and Hout, 1990). Conversely, delays in bringing products to market can be devastating. Kurawarwala and Matsuo (1993) estimate a 50–75% loss of sales by a personal computer (PC) manufacturer due to a 6–8-month delay in time-to-market.

Managers have recognized the importance of enhanced production capabilities to support a fast paced entry strategy. For example, the implementation of computer-aided design/computer-aided manufacturing (CAD/CAM) software facilitates the initial design, testing, and transfer of complex designs to manufacturing, reducing time-to-market (McDermott and Marucheck, 1995). A related concept, design-for-manufacturing (DFM) advocates that considerations such as overall manufacturing costs and time to reach maximum production rate (i.e., “ramp-up time”), be explicitly considered throughout the design process (Ulrich and Eppinger, 2000). One strategy adopted by several companies to reduce time-to-market and control increased manufacturing costs is the use of aggregate project planning. In aggregate project planning, a common product platform is developed which serves as the basis for future variations or derivatives of the product, thereby facilitating faster time-to-market with increased product variety. Finally, the reliance of successful new product introduction on enhanced process capabilities is apparent in industries such as semiconductors and pharmaceuticals (Matsuo et al., 1997).

Automakers have recently recognized the importance of investments in these mechanisms to improve new product development and time-to-market. For example, Ford recently reorganized its product development processes in an attempt to reduce its product development times by about 25% (Hanford, 2003). As part of this initiative, Ford is investing close to $100 million in CAD software to lower both product development and procurement costs (Breskin, 2003). Similarly, General Motors (GM) implemented a CAD system and reduced its average engineering design cycle from 40 months to only 18 months. Both Ford and Chrysler have also adopted new flexible manufacturing systems to facilitate quicker product changeovers (World Trade, 2003, Burt and Grant, 2002). Moreover, Chrysler’s president discusses his firm’s investment in product platform initiatives (Burt and Grant, 2002): “We are making a major study on trying to develop new manufacturing processes. This would allow us to keep different platforms while maintaining the ability to produce more derivatives.”

A significant amount of research has been conducted on how a firm can bring its products to market more quickly. One stream of such research consists of analytic models to determine the trade-off between overall product quality and time-to-market. Cohen et al. (1996) and Bayus (1997) consider the trade-off for a single firm introducing a single generation of new products. Cohen et al. (2000) further these analyses by showing how a firm’s reliance on artificial cost or scheduling targets can lead to sub-optimal investments in new product development activities. Most recently, Morgan et al. (2001) extend the time-to-market and quality trade-off to address multiple generations of a product for a single firm.

A more qualitative and empirical stream of research has focused on identifying specific mechanisms whereby a firm can improve the NPD process. For example, Wheelwright and Clark (1992) show how the use of aggregate project planning increases the likelihood of successful NPD projects. Several researchers indicate that project leadership is important to NPD efforts (Gupta and Wilemon, 1990, Swink et al., 1996). Others have shown the importance of cross-functional teams in the new product development process (Iansiti, 1995, Swink et al., 1996). Gerwin and Barrowman (2002) provide an excellent overview of research discussing the importance of cross-functional teams and organizational factors for NPD success.

A significant body of literature addresses the importance of investments in enhanced process capabilities for production ramp-up. While the dynamic process change literature has traditionally focused on decreasing manufacturing costs (see Fine, 1986, Chand et al., 1996, Li and Rajagopalan, 1997), recent additions have addressed process change implemented to increase manufacturing capacity (Carrillo and Gaimon, 2000, Terwiesch and Bohn, 2001, Terwiesch and Xu, 2001). Additional research addresses the importance of process capabilities to support a firm’s efforts to decrease its time-to-market focusing mainly on investments that enhance a firm’s “changeover flexibility” (Gerwin, 1993). For example, firms may invest in process change to minimize changeover costs/time and facilitate the transition between the production of generations of new products (Franza and Gaimon, 1998, Franza and Gaimon, 2004).

While a considerable body of research has focused on the time-to-market and process improvement/development problems in isolation, consideration of both design and production decisions to support bringing new products to market has remained relatively unexplored. In their review of the product development literature, Krishnan and Ulrich (2001) comment that the literature addressing production ramp-up and product-design decision-making is relatively sparse, though notable exceptions exist. Terwiesch et al. (2001) present an excellent overview of problems inherent to the production ramp-up period in the context of the data-storage industry. They comment that the notion of production ramp-up “has been treated as the ‘smaller brother’ of time-to-market”. Terwiesch and Bohn (2001) analyze a normative model of production ramp-up and explore the interactions between experimentation, learning, and production yields. Terwiesch and Xu (2001) consider the implications of process change via recipe changes in the semiconductor industry during production ramp-up. Bayus (1995) formulates a dynamic model of innovation whereby investments in both product and process are considered. The time-to-market decision is not, however, explicitly considered in either Terwiesch and Bohn (2001) or Bayus (1995).

In this paper, we introduce a normative model to assist managers in their quest to speed new products to market. The extent and duration of investments in both design and process-related resources are analyzed over a finite planning horizon. Until management decides to introduce a new product into the marketplace, the firm reaps the benefits of its extant products. The profit to be earned from the new product is influenced by the following factors: (i) dynamic revenues; (ii) design-related knowledge; (iii) current design and production-related capacity and (iv) dynamic demand. Furthermore, the rate of production is impacted by the firm’s production and design-related capabilities. We assume that while the requisite production capabilities can be made available early in the planning horizon, production must await the official release of the new product (i.e., time-to-market). We consider the impact of future planned generations of new product introductions on investments in these capabilities during the current planning horizon. Therefore, we incorporate the strategic nature of investments in product-related knowledge and process-related capacity enabling firms to better compete in the future.

We make several important contributions to the established body of NPD literature. First, we discuss the timing of when firms should invest in design knowledge and production capacity. An appropriate investment strategy in product and process capabilities is determined to speed new products to market. Because the timing and duration for investments in both design and process are determined over a planning horizon, managers can determine whether a “concurrent” or “sequential” development approach is appropriate. Second, we derive explicit expressions for both the optimal time-to-market and ramp-up time. The analysis confirms that optimal time-to-market and ramp-up time are interlinked. Consequently, managers must consider these two important decisions simultaneously when planning for successful new product introductions. While previous authors have focused on such time-to-market measures, the explicit derivation of an expression reflecting an optimal ramp-up time has not been explored. Third, we identify and analyze a common set of factors driving both the time-to-market and ramp-up metrics, thereby characterizing the crucial linkage between these two important NPD measures. Lastly, several specific NPD investment mechanisms are analyzed to help managers better quantify the strategic benefits associated with investments in specific NPD tools such as CAD/CAM, FMS, and an aggregate product planning program.

The remainder of the paper is organized as follows. In Section 2, the profit-maximizing model is introduced. Optimal solutions are derived and discussed in Section 3. Variations to the basic model and subsequent analysis are included in Section 4. Analytic results are explored in Section 5, offering insights to improve managerial decision-making when adopting certain NPD mechanisms intended to speed new products to market. Section 6 summarizes our conclusions.

Section snippets

The model

In this section, we present a profit-maximizing model that yields insights concerning the introduction of new products to the marketplace. First, we describe the relevant decision variables and exogenous factors for the model. Second, we examine the impact of investments in design and production on both the firm’s internal knowledge-related capabilities and actual production capacity over time. Third, we explore the relationship between the magnitude of production and the following factors: (i)

Analysis of optimal solutions

We solve the non-linear dynamic model presented using optimal control methods. Chiang (1992), Seierstad and Sydsaeter (1987), and Sethi and Thompson (2000) discuss the mathematical conditions for impulsive optimal control problems with mixed constraints. The continuous Hamiltonian to be maximized for the unconstrained problem at time t appears in Eq. (11). The adjoint variable λ1(t) represents the marginal value of an additional unit of design-related knowledge (K) at time t. The adjoint

Model variations

While the model presented in Section 2 above offers a basic view of investments in design and production to speed new products to market, the empirical literature highlights many more complications that can occur. In this section, we consider two model variations that capture the impact of several important factors. First, in Section 4.1, we consider the situation where investments in process are not effective until after the firm releases the product to the market. Second, in Section 4.2, we

Managerial insights from analytic results

Analytic sensitivity results are presented providing managerial insights on a firm’s investment strategy to bring new products to market for the original model shown in Section 2. Corollary 2 lists the factors that directly affect the optimal ramp-up time and time-to-market, respectively. Corollary 3 discusses the factors that directly affect the optimal investments in design and production resources. A discussion of the managerial implications of the key results given in these corollaries

Conclusions

Shorter product life cycles and rapid product obsolescence provide increasing incentives to enter new product markets more quickly. As a consequence of these turbulent market conditions, firms are focusing on improving their new product development processes to obtain the benefits of early market entry. Academics have analyzed this issue, examining the implications of new product development metrics (e.g., time-to-market and ramp-up time) and identifying methods (e.g., aggregate project

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