Dual-level Recognition for Environment Aware Mobile Robot

1. Final presentation: Best Route
Enrico Abate-Daga

2. Content of the presentation
• Company Presentation
• Memory Refresher: The Best Route Experiment at Deutz-Fahr
• The Project: Application, Architecture
• The Results: Business, Technical
• Lessons Learned
• Next Steps

3. Company Presentation

4. 4
Facts &
Figures
Standorte München(DE), Cluj (RO), Florianópolis (BR)
Mitarbeitende Gesamt 97; DE 49 – RO 32 – BR 16 (Headcounts)
Zertifizierte
Techniker
75% aller Mitarbeitenden
Gesellschafter Seit Mai 2020 Teil eines neu formierten Digital Service Champions
gemeinsam mit der binary und der direktgruppe
Umsatz in DE 2018: € 9 Mio 2019: € 11 Mio
Umsatzverteilung 45% recurring revenue, 45% projects, 10% licenses/ provisions
Eigenkapital > € 1.8 Mio
Relevante
Kunden
Segmente
Automotive (Dräxlmaier, TRATON, Continental, Magna, Infineon)
Travel & Transport (Lufthansa Group, Amadeus)
„Hidden champions“ (KWS SAAT SE, pfm medical, Viridium, …)
Öffentliche Auftraggeber (BMVG, Bundeswehr, EU Agenturen)
Technologie
Partnerschaften
Microsoft, AWS, HCL, Nexthink, Splunk, Beezy, MangoApps, Coyo, …

5. © Beck et al. 2020 | CONFIDENTIAL
5
Beck et al.
Mission statement
Work.Together.
We help our customers to quickly and non-
disruptively renew, refresh and keep running
their critical business systems, using new AI,
ML, Automation and other cloud native tools,
systems and approaches.
We enable and empower our customers’
employees to work more productively in the
cloud and on the edge.
Enable Digital Work
Human-centric transformation
Mitarbeitende
arbeiten digitaler
und produktiver
Form Data to Results
Data-driven
transformation
Aus Daten werde
nutzbare Informationen.
Shape a flexible IT
Flexible & agile infrastructure & operations
Wir helfen IT Abteilungen auch in Zukunft wertvolle Partner
des Business zu bleiben
work.
together.
Unsere Business Practices
Mehr unter www.becketal.com

6. The Best Route
Experiment @Deutz-Fahr

8. Current Situation
Most of the parts needed in production are already picked by robots
Some parts are still picked manually
• Powertrain: about 120-150 parts in „Supermarket“ with about 1200
parts
• Electronics: about 80-100 parts in “Supermarket” with about 900
parts
Automated picking is technically not (yet) economically feasible
Manual picking is slow and (relatively) error prone:
• 40 minutes per tractor
• 2-4 errors per day

9. Expected Results
Reduce picking time to about 20 minutes per
tractor
Reduce errors to 1 per month
Reduce stress of employees and increase
workplace ergonomics
Have data to work with…

10. The Experiment

11. Starting Situation
Paper list was not sorted in a logical way
Contains irrelevant information
Relevant information is difficult to read

12. App for Picker
Picklist as app on Smartphone or tablet
Sorting changes dynamically
• TSP: Ant colony optimization algorithm
GUI was optimized to reduce errors
• Number of parts to be picked is huge, because it is
a frequent error
• Color highlighting for most important information
(KB1 R 10 18)

13. Overview of Tractors
When a part is missing, logistics is informed in real
time
Supervisor has overview of tractors with missing
parts and their location in the plant

14. MIDIH Architecture vs. App Architecture
BUSINESS APPLICATIONS AND SERVICES
DATA ACCESS AND INFORMATION MANAGEMENT SECURITY
DATA MANAGEMENT AND ANALYTICS
PublicCloudOnPremise
DATA SOURCES
ERP
Enterprise Service Bus
Data Analytics
Dashboard
Transformation
Queue

15. The Results

16. Picking time reduced
Picking time decreased dramatically, especially for
unexperienced pickers
For the experiment we „trained“ the system with
an experienced worker and then tested the system
with (a different) experienced worker and an
unexperienced one
Goal was to reduce time to below picking time,
achieved in (almost) all cases.
28:19
23:19
20:02
38:03
28:12
23:57
CURRENT METHOD SMARTPHONE ON WRISTLET SMARTPHONE ON TROLLEY
Experience Unexperienced

17. Errors reduced
Error rate will (very likely) decrease
Can be measured reliably after a few months of
productive usage
App addresses most common errors
• Pick one instead of two-three parts
• Pick part in the „right“ position of the „wrong“
section
34
12
1 3
Type of error
Not enough parts Wrong section Wrong part Forgotten part

18. Information flow optimized
Information flows in real time
Logistics can contact suppliers more quickly
Supervisor has an overview about which parts is
missing on which tractor in which part of
production

19. Lessons Learned

20. MIDIH saved us time and gave us flexibility
Project could have been done without MIDIH, but
with MIDIH was faster
Developed in the cloud, implemented on
premise/hybrid

21. Some lessons were re-learned
Observe users and let them test early prototypes
The best solution for IT is not always the best one for
users
Many business trips can be avoided
• Original plan: the team would spend two-three
weeks on site
• Plan due to Covid-19: two one-day trips

22. Next Steps

23. MIDIH Exploitation for Beck et al and SDF
Set the application productive at Lauingen
Powertrain: being implemented
Other picking areas: Q4-2020/Q1-2021
Possibly other factories
Further usage of MIDIH within plant (e.g. supporting a
Data Lake)
Use project as reference for other potential customers

24. THANK
YOU!