extending forecasting and replenishment wtih hana and mobility
DESCRIPTION
SAP HANATRANSCRIPT
© 2012 SAP AG. All rights reserved. 2
Agenda
Overview of an extended system
Mobile In-Store Management on Mobile Devices
Out of shelf detection Concepts
Out of Shelf Detection on mobile devices
© 2012 SAP AG. All rights reserved. 3
Agenda
Extension Architecture
SAP On-Shelf-Availability Overview
Mobile In-Store Inventory Management Overview
Ipad/Iphone Demo
© 2012 SAP AG. All rights reserved. 4
Customer Activity RepositoryFoundation for predictive retail applications
Web Channel
On Shelf Availability
Assortment Planning
Promotion Planning (PMR)
Merchandise Planning
Price Optimization
Markdown Optimization
Loss Prevention Analytics
Partner Solutions
Custom Solutions …
…
Dashboards
Mobile
POS
Financial Planning
Demand Planning
Customer Segmentation
POS Sales Audit
Replenishment& Allocation
CustomerActivity
Repository
Market Data
Demographics
Social Media
Customer
Promotions
Multi-Channel Sales
Merchandise
Pred
ictiv
e A
naly
tics
Retail Analytics Model & KPIs
Assortment
Locations
Click Stream
Mul
tiple
Cha
nnel
s
Inventory
Goods Movements
BW / HANA
…
© 2012 SAP AG. All rights reserved. 5
Inventory Related Applications for Store Users
MasterData
Inventory
Orders
BW on HANA
ERP
POS DM On Shelf Availability
Retail Price
Promotions
In-StoreMIM
SupplyNetwork
SCM
Sales History
…
…
…Loss
Prevention
BOBJ Dashboards
MasterData
SupplyNetwork
Inventory
Orders
F&R
POS
F&R
Open Integration
Non-SAP
Applications for Store Users
On shelf availability analysis warns store personnel about potential out of self situations (mobile app)
In-Store MIM enables the store user to control merchandise and inventory in the stores
F&R store UI provides store users access to centrally created order proposals
© 2012 SAP AG. All rights reserved. 6
Some Statistics
Impact : Inventory & Sales 8% - 15% out of shelf inventory* $69 billion estimated lost sales
Causes: 75% in-store Poor store processes Late and insufficient ordering Incorrect store forecasts
*) Source: D. Corsten/T. Gruen: „On Shelf Availability: An Examination“
**) Source: EPCglobal
© 2012 SAP AG. All rights reserved. 8
Monitoring the System Inventory is Not Enough
No separate inventories for back room and shelf
2
System Inventory is wrong in most cases:– 65% of all inventory
figures are wrong.2
– Due to
– Theft
– Damage
– Spoilage
– Scanning errors
– Weighting errors
2 A. Raman, N. DeHoratius & Z. Ton, “Execution: The Missing Link in Retail Operations”, California Management Review 43, 136–52 (2004)
System Inventory Cannot be Used to Monitor Shelf Availability
© 2012 SAP AG. All rights reserved. 9
Possible Integration into Business Processes
Analytical
Detection of Out-of-Shelf
Process Optimization
Determination of KPIs
Identification of Process
weaknesses
– Optimization of delivery cycle, pack size, shelf capacity, shelf replenishment …
– Organizational Measures– …
© 2012 SAP AG. All rights reserved. 10
Possible Integration into Business Processes
Out-of-Shelf Detection
Direct Support of Store Processes
Analytical
Operational
– Backroom replenishment– Correction of inventory figures– Ordering– Shelf tidying
– Optimization of delivery cycle, pack size, shelf capacity, shelf replenishment …
– Organizational Measures– …
© 2012 SAP AG. All rights reserved. 11
Measurements and Challenges
Automated Measurements Direct Measurements with technical support
Indirect Measurements by the acts of purchasing
Challenges Application on different selling classes
– Fast Seller– Slow Seller
Recurring variability of the sales level
Consideration of promotions Trend Outlier …
© 2012 SAP AG. All rights reserved. 12
Methods for statistical measurments
Depending on the provided data there are two approaches possible:
1. Analysis on sales- timeseries
a) Sales minimum- limit
b) Maximum period of sequenced zero sales days
t
ymin
t
Nmax
© 2012 SAP AG. All rights reserved. 13
Monitoring the maximum waiting time I
Basis: Series of sales transactions per product, no time aggregation Idea: Frequency analysis of waiting times
determination of a maximum tolerable waiting time tL,max
tL,max can be determined from the probability of exponential distributed waiting timesas a function of average sales per period given a tolerable false alert rate.
t
tL,max
tL
2. Analysis of transactional data- Exceeding of a maximum tolerable waiting time
tL,max
tL,max
© 2012 SAP AG. All rights reserved. 14
Monitoring the maximum waiting time II
Independent of the sales behavior
t
tL tL,max
Ultra Slow Seller
t
tL tL,maxFast Seller
t
tL tL,max
Slow- Seller
Promo
© 2012 SAP AG. All rights reserved. 15
The Algorithm
The Algorithm is the science behind the on shelf availability app. Running on SAP HANA, it analyzes several months of granular T-Log sales data for each product / location
The algorithm sits on Hana, while it accessing the new POS DM on HANA, leveraging in-memory computing power to execute millions of queries constantly
Without NewDB and POSDM on HANA, this kind of number crunching is not possible in a business-useful way
© 2012 SAP AG. All rights reserved. 16
In-Store Applications Overview
Store Associate Application Displays out-of-shelf alerts and a work-list Allows corrections to be done right on the sales floor and in the
back room Updates the rest of the system appropriately
Store Manager Application Displays analytics on out-of-shelf alerts Allows Analysis of alert resolutions on different levels
© 2012 SAP AG. All rights reserved. 17
Store Associate ApplicationOverview
Goal: Displays alerts for products that may have on-shelf availability issues
Store associates in different departments can respond to alerts quickly, and can avoid having to walk throughout the entire store on a daily basis
Store associate can refill his shelf and trigger follow-on activities to prevent potential lost sales
Provides transparency of on-shelf availability situations for regular and promotional products
© 2012 SAP AG. All rights reserved. 18
Manager DashboardOverview
Goal
Provides historical insight into how many products have on shelf availability issues on a weekly, daily and hourly basis
Managers can view details on how store associates resolved each of the on shelf availability issues, and in what time frame
Managers can use this information to optimize resource scheduling, analyze why items are out of shelf, and optimize inventory levels
© 2012 SAP AG. All rights reserved. 19
Mobile In-Store Merchandise and Inventory Management
Mobile In-Store MIM enables store associates to execute core merchandising, inventory and customer service functionality on a mobile device in the aisle
Functionality Inventory/Price/Product Lookup Customer Order Management (COM)/ Order Status Store Ordering Receiving Goods Movement Cycle Counting / Physical Inventory Purchase Order
© 2012 SAP AG. All rights reserved. 20
Mobile In-Store Merchandise and Inventory Management
Mobile In-Store MIM enables store associates to execute core merchandising, inventory and customer service functionality on a mobile device in the aisle
Benefits answer nearly any question a customer might have about inventory, price, and product
details. create an order for home deliver or pickup and includes features such as create customer,
sales order, and order status Receiving functionality provides ability to post receiving and update the inventory instantly. Goods movement can be used by store associates to correct the inventory on the shelf or
transfer products. With Cycle Counting/Physical Inventory store associates can perform counting and ensure
an accurate inventory. Purchase Order allows ordering products from an external or internal vendor, move products
from one store to other, and handle returns. .