Data Analytics in the Hardwood Industry: The Impact of Automation and Optimization on Profits, Quality, and the Environment
Libor Cech,
Joseph Cazier and
Ashley B. Roberts
Additional contact information
Libor Cech: Partner/VP, Global Process Automation, Wilmington, NC, USA
Joseph Cazier: Director of the Center for Analytics Research and Education, Appalachian State University, Boone, NC, USA
Ashley B. Roberts: Monash University, Melbourne, Australia
International Journal of Business Analytics (IJBAN), 2014, vol. 1, issue 4, 16-33
Abstract:
Numerous innovative technologies are available to assist the struggling hardwood lumber industry adapt to changing market demands and environmental concerns. However, most mill owners do not utilize automated lumber systems because they do not realize how substantial volume and value gains can be. Thus, there is a need to quantify improved efficiencies while also providing reliable information about how these measures translate into profitability for the mill. This study highlights new hardwood sawmill technologies, specifically in the areas of information systems and visualization technologies, and assesses the environmental impacts alongside the practicality of widespread application. Results from on-site testing were combined with other research in the field, concluding that properly applying visualization, optimization, and information technologies across the manufacturing process can significantly improve overall yield values. Combining engineering technologies with IS and strategic supply chain management leads to reduced waste and increased profits, benefiting local economies and forest resources across the globe.
Date: 2014
References: Add references at CitEc
Citations:
Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 018/ijban.2014100102 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:igg:jban00:v:1:y:2014:i:4:p:16-33
Access Statistics for this article
International Journal of Business Analytics (IJBAN) is currently edited by John Wang
More articles in International Journal of Business Analytics (IJBAN) from IGI Global
Bibliographic data for series maintained by Journal Editor ().