FY1819 Services Supply Chain VP Award - Awesome Team

LUCI project team LUCI project team delivered a excellent result during the past several months. 1) Rule and process alligned on reverse data 2) 52M of data clean up by achieving >99.9% data accuracy 3) ξ4.4M cost avoidance by avoid 37,762pcs part scrap 4) New system enablement by achieving: 4.1 Automation Enablement 4.2 Granularity Elaboration 4.3 Customer Centric 4.4. Knowledge Base Build Up

Planning Data Team Service parts supply chain E2E info collection: 1. WW Hub outbound service order GR in Service ECC for inhouse orders - combined into Service BI - PCG Document flow This info is lost since SECC created due to design reason, even now GR can't be mapped well with GI. We accomplished combining SO, DN, GI with GR and got the complete document flow for WW Hub outbound data. 2. Collect total historical consumption detail data for daily PAL/Backlog/Miss reason analysis/reverse data monitor We need historical consumption detail data to do further detail analysis such as daily PAL, daily backlog, miss reason analysis and reverse data monitor, etc. The data is very huge(avg 7K+ lines/CD), we are taking huge workload to download from Service ECC and import into local SQL Server Database for whole team multiple usage.

SSC - New Tech TeamAs you may know, we are a brand new team - New Tech Team, we only have three people (currently grow up to four). However, we are handling several evolutionary tasks, which aims to support, optimize and improve our business. Some tasks you may already heard and applied in our daily operation process, such as demand forecast for LTBD, RA output, and designed front-end visualization. For this and next quarter, we will deliver our results based on Artificial Intelligence (AI) and Machine Learning (ML) for whole picture demand forecast, minimum stock, allocation and etc.. All delivery results we provide with friendly visualization tools and historical validation result to achieve users' satisfaction.

EC E2E management Engineering Change management is to deal with the potential defective parts. It is very important to avoid customer complaint on service parts quality. To handle WW inventory is a complicated hard work with high communication cost. We built 9 processes in 2016 and about 20 teams involved in. We requested to build a system to make the process automation and manageable. But the system quotation is ξ350k and it is not affordable. For the past 2 and half years, EC E2E management and all related teams had to handling all EC cases manually. And there were some PAL miss cases related on communication gap. Elina Zhang, as EC team lead, take initiatives to build EC Management system by hiring interns from Oct 2017. After 9 months hard work, the system launched in Jul 2018. All 9 processes and 112 steps are managed by the system and running smoothly in the past month. The system is very powerful on process control. It provides visibility on process management, reminds operation owner automatically and show the performance. Everybody could check EC cases details, stock disposition and new parts supply status on-line, and all info updated weekly.In future, the system will be developed to manage more complicated process such as product quality case handling, parts DOA management, and parts engineering inquiries.

No Source Project Team No Source Project Team worked together and overcome difficulties, reduce high-risk parts from 24Kpcs to 13.7Kpcs in Q1, all of which have solutions. For LCM 01AW977&01AX899, team push forward Samsung rebuild solution and reduces MOQ from 28K to 8K which we need, design the downgrade solution, greatly reduces the purchase cost and provides a choose for customer. For LCD 01AY700, team find 3.5k OMB resource and PCR solution is in process. For MB 00UR162, team avoid 1.3Kpcs new buy by reverse YR% improvement. For camera 04X1392, team find external rebuild suppliers and materials to cover 500pcs demand.

MBG Machine Learning Project Team ​​​​​​​In FY18/19 Q1, MBG Planning and Data Team work closely with BTIT, apply the machine learning models that have been previously validated on historical data to current phone return forecast of LTB. At present, the model has been applied to more than 10 products in NA and LA, and the phone return forecast deviation is controlled within +-10% for these product with more than 9 months actual return. The machine learning result has become very important reference for planner to make our LTB and E&O/swap risk tracking. Now, they are exploring analysis models to find more commonalities in the same phone series.

EMEA logistics Team Achieved stable delivery network across 25 countries of EMEA region which are mix of onshore/offshore, EU/non EU. Very high complexity and low amount of resources. The current status of EMEA logistics is stable operational performance and give possibility to focus on costs saving activities and remodeling for the future.

Open Market Parts Management TeamOpen Market Parts Management is an excellent team, which achieves exceptional performance through teamwork. With their professional procurement knowledge and experience, they set up properly OMB process and make overall recovery rate increase from 40% to 85%, with ξ1.8M Cost avoidance in the past 2 years

O2D Automation & Pipeline inv. AutomationThe team has worked over the last several months to gather SLA and inventory data from many different sources and create a unifying format with which to apply the logic. In O2D automation project, team overcomed difficulties on LAS DHL data issue to ensure accuracy, implemented automated root causing that helps narrow the misses into actionable categories. In Inventory project, team worked day and night to design system logic and framework, and successfully deployed an auto reporting system, easy segmentation of the metric is now available.