Redistributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective

Abstract

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing.     

The Adoption and Use of the Hirschman–Herfindahl Index in Nonprofit Research: Does Revenue Diversification Measurement Matter?

Since its introduction by Tuckman and Chang (Nonprofit Volunt Sector Q 20(4):445–460, 1991), the Hirschman–Herfindahl Index (HHI) has been widely adopted into the nonprofit literature as a precise measure of revenue concentration. This widespread adoption has been characterized by diverse composition, with the HHI’s calculation being largely determined by the nature of the available data and the degree to which it contained disaggregated measures of revenue. Using the NCCS 990 Digitized Data, we perform an acid test on whether different HHI measures yield significantly different results. Four measures of revenue concentration—an aggregated measure based on three revenue streams, an aggregated measure separating government grants from other contributions, a more nuanced measure based on seven revenue streams, and a fully disaggregated measure based on thirteen revenue streams—are used to predict two dominant nonprofit financial health dimensions: financial volatility and financial capacity. Overall, our results show that aggregation in HHI measurement matters; aggregation often downplays relationships by influencing the significance levels and magnitudes of estimates in a non-trivial way.