The cumulative capability ‘sand cone’ model revisited: a new perspective for manufacturing strategy

Abstract

The cumulative capability or the 'sand cone' model (Ferdows and De Meyer 1990, Lasting improvements in manufacturing performance: in search of a new theory. Journal of Operations Management, 9(2), 168-184) has been central in the debate on relations among dimensions of manufacturing performance. The central thesis of this model is that manufacturing performance is cumulative and sequential, with quality performance forming the foundation. An implicit assumption underlying the model is that the indirect effects of quality leading to delivery, then to flexibility and finally cost are stronger than direct effects among these same performance dimensions. Despite its frequent use, the sand cone model has not been empirically tested for direct and indirect effects or for the sequence of effects. Most tests have used correlations or regression relationships that only establish positive relationships among dimensions, not the specific sand cone sequence. We conduct two new tests of the actual sequence of the sand cone theory using data from 189 manufacturing plants. We do not find universal support for the sand cone theory, since some plants in our data appear to be following the sand cone sequence, while others are not. Our empirical tests support other limited evidence in the literature that a contingency theory is needed rather than an outright rejection or acceptance of the sand cone model.

Empirical investigation of alternate cumulative capability models: a multi-method approach

“…The datasets of manufacturers from Thailand represent more recent evidence with larger sample sizes compared to most previous studies. Using different methods to compare different trade-off, cumulative and simultaneous capabilities this study extends the work of Schroeder et al (2011) by verifying the best-fit cumulative and simultaneous capabilities models for another developing country. The study complements the work …”

“…Quality and product introduction speed may form the basis of cumulative capabilities for manufacturers competing in more competitive and rapidly changing industries, where product life cycles are shorter (Flynn and Flynn, 2004). As stated by Schroeder et al (2011), national contingency factors have not been taken into account by most existing studies because they examine their datasets based on a single cumulative capability model.…”

“…Based on a multi-country (e.g., Finland, Germany, Japan, Korea, Sweden, and the United States) cross-sectional dataset, not every manufacturer was found to apply the Q-D-F-C model (Schroeder et al, 2011). Another repeated multi-country (i.e., Hungary, Italy, and Taiwan) cross-sectional study by Narasimhan and Schoenherr (2013) also found that manufactures did not follow the Q-D-F-C model as they progressed.…”

“…Specifically, this study empirically tests and extends the applicability of two popular models (Q-D-F-C and Q-D-C-F) that are potentially predominant among Thai manufacturing industries. To achieve the utmost rigour, the study applies multiple methods, including correlations and sequential tests (see Schroeder et al, 2011), followed by structural equation modelling (SEM). SEM is the most appropriate method for replication studies (Rosenzweig and Easton 2010;Narasimhan and Schoenherr, 2013).…”

“…Using a model fit index, SEM helps to compare the fit between each model with the same dataset. Furthermore, SEM allows us to systematically examine the direct and indirect effects of various capabilities (Schroeder et al, 2011), leading to a more comprehensive understanding of cumulative capabilities in terms of both sequential and simultaneous relationships amongst different capabilities.…”