Technology Specifications and Production Timing in a Co‐Opetitive Supply Chain

Abstract

Motivated by Google’s technology specifications on Android devices, we consider firms’ decisions on production timing in a co‐opetitive supply chain comprising a manufacturer and an original equipment manufacturer (OEM), where the manufacturer acts as the OEM’s upstream contract manufacturer and downstream competitor. We consider the market acceptance uncertainty of key product designs. If a firm decides to implement ex post production strategy (PS), it can delay the production until the market acceptance uncertainty of its product is resolved. Otherwise, ex‐ante production strategy (AS) is implemented. We find that, due to the co‐opetition, PS does not always benefit either the manufacturer or the OEM, because the value of delayed production is diminished as the competitor may commit a production quantity earlier under AS. Further, firms’ decisions on production timing are dependent on the degree of market acceptance uncertainty of their products and competition intensity. We find that both firms choose PS when uncertainty is high, while only one of them chooses PS when uncertainty is moderate or low. Interestingly, when the competition is intense, the manufacturer tends to choose PS, which can benefit from both the resolved market acceptance uncertainty and OEM’s early commitment of production quantity.

Sharing Knowledge to an Entrant for Production Investment Confronting COVID‐19: Incentive Alignment and Lose–Lose Dilemma

“…Findlater, Satterfiel, and Kandlikar (2019) study farmers' capacity decisions considering limited information related to weather and climate risk. Niu et al (2019a) study the production timing decisions of two product providers-a contract manufacturer (CM) and an OEM-in a cooperative supply chain when they faced market acceptance uncertainty because of product design innovation. Hu et al (2020) classify innovation into technical innovation and nontechnical innovation, and they study an innovator's outsourcing decision when its innovative design can be learnt by a CM with a self-branded business.…”

“…Stanko and Calantone (2011) point out that outsourcing represents the most common way of sharing knowledge in a supply chain. Therefore, literature on knowledge sharing is usually based on strategic outsourcing decisions (e.g., Chen et al, 2016;Gupta, 2008;Hu et al, 2020;Niu et al, 2019a: Zhu et al, 2007, and can be classified, according to the information flow direction, as horizontal information sharing or vertical information sharing. Prior literature (e.g., Gal-Or, 1985, 1986He, Devine, & Zhuang, 2018;Li, 1985;Vives, 1984) has focused on horizontal information sharing, which investigates whether each duopolist or oligopolist will make its demand information known to the rival.…”

“…(1) Effect of supply expansion: We use the following inverse demand functions, which are widely used in economics and operations management (Christen, Boulding, & Staelin, 2009;Hu et al, 2020;Niu et al, 2019a;Singh & Vives, 1984;Swinney, Cachon, & Netessine, 2011;Vives, 2000):…”

“…Knowledge and information sharing is believed to be an important and interesting research topic (Choi, Govindan, Li, & Li, 2017;Shen, Choi, & Minner, 2018). Previous studies have extensively examined the interactions between knowledge and information sharing and other elements such as qualityimprovement efforts (Chen, Zhao, Lewis, & Squire, 2016;Zhu, Zhang, & Tsung, 2007), risk management (Niu & Zou, 2017), production timing decisions (Niu, Chen, Fang, Yue, & Wang, 2019a), and product innovation (Hu, Mai, & Pekeč, 2020;Shen, Li, Dong, & Quan, 2016). Zhu et al (2007) and Chen et al (2016) are the most related publications with a common interest in improving production efficiency; however, they omit the roles of horizontal competition and cooperation.…”

Strategic Waiting for Disruption Forecasts in Cross‐Border E‐Commerce Operations

“…We follow the previous literature, such as Niu et al. (2019), by assuming that the supplier announces the wholesale price earlier than the disruption forecast, 3 after which the e‐tailer decides the order quantity for the bonded‐warehouse channel. If the e‐tailer has decided not to wait in stage 1, then the e‐tailer determines before the release of the disruption forecast and based on the prior belief about the future disruption (i.e., Pr( f = 1) = ψ ).…”