1. Work Pattern Analysis
w/ & w/o Site-specific Information
in a Manufacturing Line
Takeshi Kurata1, Rei Watanabe2
Satoki Ogiso1, Ikue Mori1, Takahiro Miura1, Karimu Kato1
Yasunori Haga3, Shintaro Hatakeyama3, Atsushi Kimura3 and Katsuko Nakahira2
1 Human Augmentation Research Center, AIST, Japan
2 Faculty of Engineering, Nagaoka University of Technology, Japan
3 DENSO CORPORATION, Japan
APMS 2023

2. HPM and Geospatial Intelligence (GSI)
2
Kurata, T., Geospatial Intelligence for Health and Productivity Management in Japanese Restaurants and
Other Industries, APMS, pp. 206–214 (2021) doi: 10.1007/978-3-030-85906-0_23

3. Geospatial Intelligence (GSI) with 6M data
3
IE: Industrial Engineering, OR: Operations Research,
IoT: Internet of Things, IoH: Internet of Humans, UI: User Interface
XR: VR, AR, MR, etc. (AR: Augmented Reality, VR: Virtual Reality, MR: Mixed Reality)
RM: Raw Material, WIP: Work In Progress, SFG: Semi-Finished Goods, FG: Finished Goods
Conceptual diagram of GSI 6M information in Service and
Manufacturing sites

4. Study focus and research question
5
DENSO CORPORATION: Promoting activities aimed at improving both
productivity and QoW, especially the working environment, such as the thermal
environment.
DENSO & AIST: Conducting multifaceted analysis of productivity and QoW using
various acquired data while utilizing indoor GSI technology.
Focus of this study (today’s presentation): Work analysis using flow line data for
capturing a comprehensive view of the actual work behavior of each worker on the
manufacturing line.
Research question: Examine whether it is possible to start the analysis even before
site-specific information is available.
Room temperature heat map and flow line (exposed temperature and activity of each worker)

5. Work pattern analysis w/ GSI:
Start with or without site-specific information?
4

6. Work site in this study
6
Manufacturing line in a DENSO factory
• Manufacturing process of automotive work-in-progress
• Main area for analysis: manufacturing line (1,400 m2)
Measurement period: 5 days
Number of workers: 10
• Day shift: 5 (Leader, Deputy leader, Receiving, Visual inspection, Internal inspection)
• Night shift: 5 (Leader, Deputy leader, Receiving, Visual inspection, Assembly)
Manufacturing line
Break
room
Air shower
Staying plots

7. Manufacturing line
Air shower Break room
Indoor Positioning
7
Indoor positioning system in this study
(PDR & BLE & Map)
CE50: about 3m
Indoor positioning tech map
[16] S.Ogiso, et al., “Integration of BLE-based proximity detection with particle filter for day-long stability
in workplaces,” IEEE/ION PLANS 2023
BLE beacons
• 48 locations in the manufacturing line
• 8 locations in the break room and others

8. Overview of our work analysis method
8
Staying
plots
Extract “staying plots” from
flow lines
Generate the “work area transition model” by
clustering of staying plots of all shifts & workers
Obtain “work area transition instances” by
registering features of each shift & worker in
the work area transition model
Find “work patterns” by
clustering of work area transition instances
Extract exceptions from work area transition
instances by comparing to work patterns
Work area
transition model
Clustering
Work pattern A
(Cluster A) Work pattern B
(Cluster B)
Work pattern C
(Cluster C)
Clustering
Indoor
positioning
Work area transition
instances
Exception 1
(non-routine work 1)
Exception 2
(non-routine work 2)

9. How to generate
the work area transition model
9
K-means++

10. Work area transition model generated in this study
10
525 dimensions in total
• 84-dimensional staying rate features
• 441-dimensional moving rate features

11. How to obtain a work area transition instance w/
the work area transition model
11
Registering staying rate features and moving rate features of each shift & worker
in the work area transition model
Staying rate
features
Moving rate
features

12. Extract “staying plots” from
flow lines
Generate the “work area transition model” by
clustering of staying plots of all shifts & workers
Obtain “work area transition instances” by
registering features of each shift & worker in
the work area transition model
Find “work patterns” by
clustering of work area transition instances
Extract exceptions from work area transition
instances by comparing to work patterns
Find work patterns
12
Staying
plots
Exception 1
(non-routine work 1)
Work area
transition model
Clustering
Work pattern A
(Cluster A) Work pattern B
(Cluster B)
Work pattern C
(Cluster C)
Clustering
Indoor
positioning
Work area transition
instances
Exception 2
(non-routine work 2)
K-means++

13. Examples of work area transition instances and clustering results
13
Example of work area transition instances in the typical work patterns for each role
Assumed deployment information for each role
provided by the site manager after clustering
Clustering results of work area transition instances
and their relationship to roles
• ID 1-8: Work pattern IDs (cluster IDs)
• ID 0: Four work patterns with one instance
• 46 work area transition instances into 12 clusters
Note: Role and shift info are used only as ground
truth (not for clustering).
1 2 3
5 7 8

14. 14
How to extract exceptions (non-routine works)
from work area transition instances
RSS: Residual Sum of Squares

15. Extracted exceptions of work area transition instances
15
Seven extracted exceptions of work area
transition instances along with their
exception indicators and the threshold.
Exceptions A and B for visual inspection.
5

16. Conclusions
16
12 work patterns and 7 non-routine works found w/o
site-specific information containing each worker's
role, shift and typical work areas of each worker
More findings by interviewing on-site
managers w/ the analysis results
(Not discussed today...)
Future works
Verification of
the applicability
in other sites
Pre-evaluation of site
improvement/design ideas by
simulation based on the work
area transition model and
work patterns
Multifaceted analysis of productivity
and QoW w/ physical-work data,
operational data, environmental
exposure data, vital sign data, and
subjective data
Work pattern analysis only w/ flow lines
based on indoor GSI (Geospatial Intelligence)

17. On-going other case: Express-way service area with a two-
story building
17
26 clusters of staying plots (48 workers, 20 days) Work area transition model
Examples of work area transition instances
1F
2F
1F
2F

18. Evaluating the work environment and physical load of factory workers
18
[15] Nakae, S., el al., Geospatial intelligence system for
evaluating the work environment and physical load of
factory workers, 45th Annual International Conference
of the IEEE Engineering in Medicine & Biology Society
(EMBC), 5 pages, 2023
Multifaceted analysis of productivity and
QoW w/ physical-work data, operational
data, environmental exposure data, vital
sign data, and subjective data

19. Previous related work: Simulation with a work process model
generated from flow lines and picking data in a warehouse
19
Single picking Zone picking
Myokan, T., et al., Pre-evaluation of Kaizen plan
considering efficiency and employee satisfaction by
simulation using data assimilation -Toward
constructing Kaizen support framework-, Proc.
International Conference of Serviceology (ICServ2016),
7 pages (2016)

20. Conclusions
20
12 work patterns and 7 non-routine works found w/o
site-specific information containing each worker's
role, shift and typical work areas of each worker
More findings by interviewing on-site
managers w/ the analysis results
(Not discussed today...)
Future works
Verification of
the applicability
in other sites
Pre-evaluation of site
improvement/design ideas by
simulation based on the work
area transition model and
work patterns
Multifaceted analysis of productivity
and QoW w/ physical-work data,
operational data, environmental
exposure data, vital sign data, and
subjective data
Work analysis only w/ flow lines
based on indoor GSI (Geospatial Intelligence)