Coming SAP HANA for Retail Solution Septembar 23, 2015 Miroslav Kržić

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1 Coming SAP HANA for Retail Solution Septembar 23, 2015 Miroslav Kržić Coming, Belgrade New Idea & Modis, Moscow

2 MODIS Leading Russian Fashion Brand

3 География присутствия и план развития Общее население в городах, где представлен МОДИС более 48 млн. человек, в том числе: 15 городов 1 млн.+ 15 городов 500 тыс городов 200 тыс.+ 9 городов 100 тыс.+ Федеральный охват свыше 125 магазинов в 65 городах. В 2014 году количество магазинов превысит 140 Общая площадь магазинов в 2014 году достигнет м2. Средняя площадь магазина составляет м2

4 Modis: Aplikaciona/Virtuelna Infrastruktura Integracija

5 Modis applications and virtual infrastructure landscape Applications landscape DEV SAP Retail ERP Landscape TEST PROD DB+CI HANA Ph2 APP1 APP2 Datawarehouse Landscape (SAP BW) POSDM DEV TEST HANA Phase 1 CRM PROD MAP POS Landscape POS Srv New POS BI (BO) Server PI Server 1C Other Virtual Servers Exchange, AD, File/Print, SharePoint...) Core IT Infrastructure in M1 datacenter VMware vsphere Virtual infrastructure with centralized management and monitoring VMware SRM (DR extension) Core networking (SAN/ LAN Switches) Compute resources (5 hosts/servers) HANA Appliances Disk storage resources H1 H2 H3 H4 H5 D2D HP 3PAR HP EVA Backup Disaster Recovery Datacenter for Core Business Services (applications) Existing Project in progress

6 Modis Application Integration and BI New design - based on SAP HANA TeamIdea,

7 SAP HANA New Modis Analitical Plaftorm SAP ERP, POS, CRM Data Sources Real Time Data Replication SAP HANA Analytics, Forecasting, MAP, Replenishment, Markdown New TeamIdea,

8 Modis Sales Transactions volume RETAIL co. - POS - volume of transactions Volume of sales $ Avg sale amt 25 $ Avg no of items 3 8$ per item No of receipts/invoices per year No of recpt lines per year data warehouse 5 years recpt history recpt lines 5 year No of SKU % of new SKU per year 40% no of new SKU SKU total no of recpts/day (300 working days) no of SKU/day no of trans/sec (12 hours working day) 8 no of stores 400 no of billed customers per store per day 833?! data volume (dw no compression) dimension keys: store 4 datetime 8 sku 18 tt 2 payment type 2 consumer type 4 repeating cust. 4 sku group 2 sku type 2 sku number 2 sku design/color 2 store region 2 uom 2 discount type 2 measures: qty 8 price 8 cogs 8 discount 8 hard currency val 8 total bytes 96 ~100 Expected value (bytes) 100 fact table size (bytes) B 00 fact table size (GB) 150 GB

9 Merchandise and Assortment Planning

10 MAP Project Goals and Objectives PROJECT GOALS PROJECT GOALS 1 Create flexible and simple to use planning methodology; Bring best practice planning process for Fashion Retail; Improve the manageability of the planning process; Improve transparency of the planning process in company Improve planning result accuracy and reduce planning error; Involve business users into the planning process with strictly defined roles; Reduce user mistake; Analyze current process gaps and requirements received from the business team responsible for financial, retail, assortment and purchasing plan; Develop one centralized, easy to use and flexible planning platform for all MAP planning activities using SAP technology; Involve business user in planning process with planning SAP tool with strictly defined roles and responsibilities; Improve master data quality used for planning process; TeamIdea,

11 Demand planning structure and requirements Board of directors TOP-DOWN PLANNING STRATEGY Country Manager Sales Budget at Sales org., distribution channel, brand and country Strategic Planning Wholesale Manager Breakdown at division, agent, customer and product class Sales Budget Breakdown at store, division and product class Store Planning Merchandise Planning Breakdown at division, product class and prod. group Retail Manager Sales Agent Calculate forecast from actual sales Forecast Plan quantities at SKU level per store cluster Assortment Planning Options Planning Plan number of options per store cluster Retail Product Manager Purchase Manager Evaluate global demand (wholesale and retail) and release quantities to create purchase orders Purchase Planning Capsule Definition Decide capsule configuration and deliv. dates Design, sales and purch. Fashionworks has a Merchandise and Assortment Plan solution for the Retail Fashion business that has evolved to a more flexible environment, SAP IP, conserving its integration quality with the transactional system.

12 Planning for retail overview real time requirements Store Planning Assortment Planning Merchandise Planning Pre Season Capacity Planning Strategic Planning PLANNING OTB End of season Purchasing In Season Promotions Update OTB Markdown planning. Replenishment

13 Modis Forecast Replenishment and Markdown Modules

14 Key Modules for Effective Execution New modules (blue) and integration with datasources (green) POS (dataset from point of sales system) Forecast module Stock ad DC/Store (dataset from ERP) Markdown/ Clearance module Allocation/ Replenishment module Transactions flow (goods movement and status change trans.) Control flow/data input TeamIdea,

15 TeamIdea,

16 Store replenishment real time and reliable forecast Forecast flow Manual selection Automatic system selection Model Initialization Specify model parameters Stock withdrawn from the warehouse Enter manually or interface Reference material consumption Alternative historical data (AHD) Create forecast values Calculated by the system Enter manually Define Forecast model Creating historical data

17 TeamIdea,

18 TeamIdea,

19 Modis Transformation of Data Processing

20 SAP NetWeaver BW - New Architectural Paradigm SAP NetWeaver BW Data Modeling DBMS Analytical / Planning Engine Data Management Relational Database Data Storage SAP NetWeaver BW Data Modeling HANA Analytical / Planning Engine Data Management Data Storage

21 SAP HANA Platform for Real Time Business

22 Common Design & Modelling Environment Big Data Bundles for Retail from SAP 3 rd party, open source analytic tools BusinessObjects BI Suite SAP Predictive Analysis SAP Visual Intelligence R, SAS, SPSS, etc. SAP real time data platform Open APIs and Protocols Transactional Data Management Federated Access In-Memory Data Management Analytics EDW Data Management Information Management & Real-Time Data Movement Mobile Data Management Common Landscape Environment

23 The Future of Modis: HANA + Hadoop Enriching Retail POS with Customer Product Interest

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