Project CAPS: Structured analysis of field quality data with integration of enhanced machine data at CLAAS

Transcription

1 Project CAPS: Structured analysis of field quality data with integration of enhanced machine data at CLAAS Rome, November 2016 Axel Holtkotte, CLAAS Service & Parts GmbH

2 Agenda CLAAS CLAAS Service and Parts GmbH Project CAPS Usage of SAS Warranty Analysis Usage of SAS Visual Analytics Integration of enhanced data sources 2

3 The CLAAS Company Company Type CEO Turn-Over Worldwide Export Rate Employees worldwide KGaA mbh (private-owned) Cathrina Claas-Mühlhäuser 3,8 Mrd. 77,2 % Excerpt of CLAAS manufactured products: Machines with 50 to 900 HP between 3 and 30 tons Availlable working width between 2 and 14 meters Machines with GPS-controlled accuracy of 2-3 cm during operation Machines with sensoric-controlled and self-learning working capabilities 3

4 CLAAS Service & Parts GmbH Overview CLAAS Service & Parts is an independent company of the CLAAS Group Service Product Segment Management Technical Documentation Market & Customer Relations Warranty and Reporting Parts Sales Logistics & SCM Pricing Purchasing Material Planning Cross-Over Functions Marketing & Product Management Projects & Applications Controlling Human Ressources Quality Management 4

5 Agenda CLAAS CLAAS Service and Parts GmbH Project CAPS Usage of SAS Warranty Analysis Usage of SAS Visual Analytics Integration of enhanced data sources 5

6 CLAAS Service & Parts GmbH Main Tasks of Warranty & Reporting department Distibution of classic KPI`s for steering and controlling of the After-Sales-Network Identification, Description, Priorization and Distribution of the most important technical problems from all markets regarding the following aspects: Customer Satisfaction Safety Costs Frequency 6

7 Project CAPS The target process Data Entry Main focus identification Detail Analysis Statistics Each failure is uniquely listed in the system The system identifies key aspects without manual work Problem analysis based on assembly groups, parts or symptoms on all effected machines incl. manual work Automatic transfer and provision of data and common usage of the tools worldwide Solved by other CLAAS Systems SAS Warranty Analysis (SWA) SAS Visual Analytics (VA) 7

8 Agenda CLAAS CLAAS Service and Parts GmbH Project CAPS Usage of SAS Warranty Analysis Usage of SAS Visual Analytics Integration of enhanced data sources 8

9 SAS Warranty Analysis Semi-automatic topic identification and detailed analysis Usage of Emerging Issues for main focus identification and review by the analysts The system identifies claim peaks or rising topics on its own and the analyst can use this as work base. Usage of precise machine and claim selection rules in the detailed analysis, that are auto-updated every weekend. Definition of precise Market Failure Clusters (MFC), that need to be solved by further instances of the Product Improvement Process. Reduction of manual review work by the update processes. Time can be spent on other topics or even more detailed analysis (Weibull, Trend by Exposure, Failure Relations etc.) Screenshots of SWA 9

10 From SAS Warranty Analysis to SAS Visual Analytics Priorization and Hand-Over to attached systems A SAS programmed attachment and the CLAAS priorization tool Issue Priority Ranking (IPR) are used to: Define the most critical topics, that need to be solved asap. Enable an automatic transfer from SAS Warranty Analysis to other systems like Visual Analytics or the SAP-based CLAAS Issue Tracking System (3I 8D). SAS Visual Analytics SAS developed attachment to use CLAAS IPR and extract data from SWA CLAAS Issue Tracking System 10

11 Agenda CLAAS CLAAS Service and Parts GmbH Project CAPS Usage of SAS Warranty Analysis Usage of SAS Visual Analytics Integration of enhanced data sources 11

12 SAS Visual Analytics Distribution of SAS Warranty Analysis Infos to the Users Standardized Reports with all SAS Warranty Analysis Topics are available in Visual Analytics Users can reach such a list with restricted access to the data, depending on their role. Access to these reports is also possible from CLAAS Issue Tracking System by Hyperlinks. Interactive analysis of a cluster is transferred to the user, instead of sending attachments. 12

13 SAS Visual Analytics KPI-Reporting Usage of VA for Reporting of machine performance in After-Sales (Costs and Failures per unit) The interactive usage of data from different sources in VA allows it, to describe precisely trends and their relation to Market Failure Clusters found with Warranty Analysis. Usage of Visual Analytics for calculation and distribution of KPI s for the After-Sales-Network Drill-Down through defined hierarchies. 13

14 Agenda CLAAS CLAAS Service and Parts GmbH Project CAPS Usage of SAS Warranty Analysis Usage of SAS Visual Analytics Integration of enhanced data sources 14

15 Enhanced data integration The integration of enhanced machine data The next step after the finish of the CAPS project is the integration of telemetric data, that the machines produce during work The first step is to gather, comprimize and pre-calculate relevant telemetric data for a better understanding of the environment, in which a failure occured. What informations can be given in addition?! Examples: Engine load Harvested crop type Error messages Location Failure Clusters (detected with SWA) But Predictive Maintenance can only be done in a second step, because Telecommunication infrastructure in very rural areas for Telemetric systems and high Telematics activation rates on the machines are necessary You need 100% reliable sensor technology and a look-up database built up by experience and high knowledge to interpretate data correctly The infrastructure and IT landscape for vast data calculation, storage or streaming must be set up 15

16 Enhanced data integration Integration of enhanced machine and environmental data The current situation of the experimental CLAAS Big Data landscape (not only for Predictive Maintenance!) Testsoftware Terrain Data The current data storage Weather Data Project CAPS TELEMATICS Data from CLAAS machines Productive Software - Usage of claim and machine data in CAPS Project - But what with - CRM - Shop Floor Management - Spare Parts Orders - For us, question after question appears. Questions like system interactions, use- and senseful data copying and storaging, how to precalculate or stream data, how to handle the security, how to avoid misuse (even internally), has other software impacts and so on. 16

17 Lessons learnt What did we learn about and what is facing us?! Professional tools for detection and analysis of market failures allow deeper insights and faster reaction on growing problems Leaving the way of ACCESS, Excel and others and the shift towards high-performance analytics software saves a lot of work time. This is so far the biggest benefit of our project. Telemetric data has a vast field of possibilities, but the way towards Predictive Maintenance is a long one. - You need a working database for failure modes and related telemetric events, that needs to be filled over years - You need reliable sensor technologies and high performance systems for the event streaming, because in our business real-time is necessary due to seasonality - We focus on quick wins from the data in the first step. The data landscape for IT-infrastructure grows somehow together, but there are risks and benefits like data accessability for others, calculation methods of KPI or the cross-over work with other departments Specialists are necessary for this job! A data scientist only would never work, because he will never have the knowledge like the department experts! 17

18 Questions left?! 18