ERP System versus Stand‐Alone Enterprise Applications in the Mitigation of Operational Glitches

Abstract

Business function‐specific stand‐alone enterprise applications (SEAs) are displacing functionally integrated enterprise resource planning (ERP) systems, despite strong empirical support for the business benefits of the latter. This study explores the conditions under which it may be more effective to use a set of SEAs instead of a single‐suite ERP system, and vice versa. Based on Organizational Information Processing Theory, we expect differences in effectiveness to grow in prominence when the uncertainty of the operating environment increases, that is, when operational glitches in production processes become more frequent. Extending the existing literature, we postulate that high functional differentiation is a precondition for SEAs to be more effective than an ERP system, hypothesizing that the level of functional interdependence ultimately determines which type of software is superior for a given production process. We test our hypotheses using data collected from 163 make‐to‐order (MTO) production processes nested within 73 manufacturing plants and seven supply chains of complex, high‐tech machinery. Results show that when functional interdependence is low, the negative effect of operational glitches on delivery performance is effectively mitigated in MTO production processes wherein process‐related information is managed predominantly using SEAs; conversely, when functional interdependence is high, using an ERP system is more effective. Our findings offer practical guidelines as to when to use SEAs versus an ERP system while also integrating and updating the findings of earlier empirical research, in which each has been analyzed separately.

Strategic Purity and Efficiency in the Motor Carrier Industry: A Multiyear Panel Investigation

“…() and Tenhiälä et al. () providing two of the more recent examples. Furthermore, our information processing explanation strictly adheres to criteria utilized to evaluate a theory's virtues (Keas ) in that it can explain a variety of findings (Laudan ), it is parsimonious (Thagard ), and aligns with accepted background knowledge (Lipton ).…”

Exploring the antecedents of organizational resilience practices – A transactive memory systems approach

“…Conformance problems only reveal themselves during final assembly. In response to the occurrence of such operational disruptions, manufacturers are forced to adjust their original order-fulfillment plans, which negatively impacts their delivery reliability (Tenhiala and Salvador, 2014; Tenhiala et al , 2018) and financial performance (Hendricks et al , 2005). In order to minimize the impact of having to adjust original order-fulfillment plans, manufacturers deploy organizational resilience practices (Craighead et al , 2007; Tenhiala and Salvador, 2014; Polyviou et al , 2019; Scholten et al , 2019).…”

“…However, collaboration and knowledge integration are often essential to the deployment of these and other relevant resources (Kogut and Zander, 1992; Grant, 1996; Brandon and Hollingshead, 2004). By accessing organizationally dispersed information and knowledge resources and integrating them towards the amendment of original plans, organizations become better able to cope with unforeseen operational disruptions (Tenhiala and Salvador, 2014; Scholten and Schilder, 2015; Ambulkar et al , 2015; Revilla and Saenz, 2017; Tenhiala et al , 2018; Polyviou et al , 2019; Scholten et al , 2019).…”

A New Perspective on Returns to Scale for Truckload Motor Carriers

“…This is why Pawson (2000, p. 296) states, “the basic structure of hypothesis‐making takes the form [of] speculating upon the generative mechanisms that give rise to observable outcome patterns” [emphasis added]. This limitation, however, is assuaged by the fact our theory is consistent with background knowledge regarding TL carrier operations (Ouellet 1994; Powell and Mayoras 1996a, 1996b; Simão et al, 2010), aligns with principles from IPT (Galbraith 1977; Tushman and Nadler 1978) that has been utilized to explain numerous supply chain management phenomena (Tenhiälä et al 2018), and can explain an array of empirical findings (Laudan 1977). Furthermore, the fact that our theory performs well across multiple theoretical virtues (Keas 2018) means that regardless of its ultimate verisimilitude, it suggests a promising direction for further inquiry (Whitt 1992; Nyrup 2015).…”