John Chandler Johnson

At the Forefront, Looking Ahead: Research-Based Answers to Contemporary Uncertainties of Management p. 23-42 Doi: https://doi.org/10.18261/9788215031583-2018-03

Evolving at an unprecedented pace, digital technologies promise to automate not only labor-intensive and repetitive work, but also the traditional and exclusive domain of educated humans—knowledge work. This is evident in the new ways of reaching customers and coordinating activities, as well as in the fact that companies conducting a business built on the new technologies now constitute the world’s largest enterprises. The presence and evolution of these companies challenge established divisions of labor between man and machine, and almost casually redraws the boundaries between industries. Machine learning and analytics challenge the managers leading and the managerial scientists studying organizations. Everybody says they want to be data driven—but what does a company really need to do to achieve that? This article will explore the managerial, organizational, and strategic implications of allowing an ever increasing number of organizational decisions to be taken not by managers employing intuition and common sense, but by algorithms and learning systems based on massive amounts of data derived from electronically based customer interactions. We argue that these companies can be thought of as “intelligent enterprises” with enhanced abilities to sense, comprehend, act, learn and explain (SCALE) their environment and their interactions with it. To acquire these capabilities, managers need to cede authority over some decisions while acquiring new capabilities and roles for themselves.

International Journal of Physical Distribution & Logistics Management 46(1) p. 82-94 Doi: https://doi.org/10.1108/IJPDLM-10-2015-0257

Purpose: Direct Digital Manufacturing (DDM) is conceived of as either disrupting the entire manufacturing economy or merely enabling novel production. Between these extremes, we introduce an alternative where DDM coexists with and complements traditional mass production. When multiple parts run across one manufacturing line, DDM can isolate variability associated with low volume part production and may be preferred to mass production despite being expensive. If DDM complements rather than cannibalizes mass production, this alters our understanding of who adopts DDM, the products built with DDM, and DDM’s long-term supply chain implications. Design/methodology/approach: This invited article explores a DDM rollout scenario and qualitatively assesses potential supply chain reconfigurations. Findings: Our analysis recognizes that existing manufacturers with heterogeneous bills-of-material may develop DDM capabilities to isolate disruptive, low-volume production from scalable mass production. Developing DDM competence and raw material scale advantages, these manufacturers become the locus of change in a manufacturing landscape increasingly characterized by multi-product DDM supercenters. Originality/Value: Extant research largely focuses on two potential reasons for DDM adoption: cost-per-unit and time-to-delivery comparisons. We explore a third driver: DDM’s capacity to isolate manufacturing variability attributable to low volume parts. Relative to the extant literature, this suggests a different DDM rollout, different adopters, and a different supply chain configuration. We identify mass manufacturing variability reduction as the mechanism through which DDM may be adopted. This adoption trajectory would eventually enable a supply chain transition in which spare parts inventory migrates from finished goods at proprietary facilities to raw materials at generalized DDM supercenters.