Inventory Tracking for Unstructured Environments via Probabilistic Reasoning

Mabaran Rajaraman, Kyle Bannerman and Kenji Shimada
Additional contact information
Mabaran Rajaraman: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Kyle Bannerman: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Kenji Shimada: Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Logistics, 2020, vol. 4, issue 3, 1-29

Abstract: Workpiece location is critical to efficiently plan actions downstream in manufacturing processes. In labor-intensive heavy industries, like construction and shipbuilding, multiple stakeholders interact, stack and move workpieces in the absence of any system to log such actions. While track-by-detection approaches rely on sensing technologies such as Radio Frequency Identification (RFID) and Global Positioning System (GPS), cluttered environments and stacks of workpieces pose several limitations to their adaptation. These challenges limit the usage of such technology to presenting the last known position of a workpiece with no further guidance on a search strategy. In this work we show that a multi-hypothesis tracking approach that models human reasoning can provide a search strategy based on available observations of a workpiece. We show that inventory tracking problems under uncertainty can be approached like probabilistic inference approaches in localization to detect, estimate and update the belief of the workpiece locations. We present a practical Internet-of-Things (IoT) framework for information collection over which we build our reasoning. We also present the ability of our system to accommodate additional constraints to prune search locations. Finally, in our experiments we show that our approach can provide a significant reduction against the conventional search for missing workpieces, of up to 80% in workpieces to visit and 60% in distance traveled. In our experiments we highlight the critical nature of identifying stacking events and inferring locations using reasoning to aid searches even when direct observation of a workpiece is not available.

Keywords: inventory management; industry 4.0; construction 4.0; IoT; smart manufacturing; construction technology; probability; graphs (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2305-6290/4/3/16/pdf (application/pdf)
https://www.mdpi.com/2305-6290/4/3/16/ (text/html)

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:gam:jlogis:v:4:y:2020:i:3:p:16-:d:384339

Access Statistics for this article

Logistics is currently edited by Ms. Mavis Li

More articles in Logistics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().