SAP® Innovation Awards 2020 Entry Pitch Deck

SAP® Innovation Awards 2020 Entry Pitch Deck IDEA: DHL’s Integrated Tasking System using Advanced Analytics to Optimize Fulfilment with SAP DHL Supply Chain Company Information

Headquarters US: Columbus, Ohio. Global Bonn, Germany Industry DHL - 3PL Supply Chain; Customer Industry – Life Science Web site https://www.logistics.dhl/global-en/home.html

Please describe your company and its business. How is your company attempting to run as an intelligent enterprise? ▪ DHL Supply Chain is the global leader in supply chain management & third-party logistics, implementing innovative logistics solutions across a wide range of industries. ▪ Our operations range across all market verticals including eCommerce, Retail Consumer, Automotive, Industrial and Life Sciences. ▪ This range of operations results in DHL operating on a diverse range of warehouse management systems. ▪ Many of DHL customers operate on SAP ERP using either core SAP Warehouse Management or SAP Extended Warehouse Management module. ▪ We work with our customers and SAP to optimize labor intensive tasks such as picking, by applying advanced optimization techniques.

Challenge Thank you for installing Current picking operation is inefficient. Orders are small and are grouped together with ~12 orders per cart. This this beautiful waving is done manually by grouping order pick lists from SAP together. Typical assignment will span entire warehouse system that makes our life operation. at work easier and drastically improves Solution productivity Create a waving/tasking optimization tool that stands takes open order pool, optimizes work and sends back to Site Waving Manager SAP as the system of record.

Outcome Drastic increase in productivity and order turn time

Reduction in Savings in wave travel time planning time Increase in travel 50% 30% time 60%

Fortune 500 - Life Science Company DHL customer provide access to open order database allowing direct feed of orders

DHL has installed IDEA application in 4 operations. Case study describes install in DHL operation for large multinational conglomerate with over $20B in revenue focused in the area of healthcare

▪ Picking assignments travel entire warehouse operation ▪ Pick lists must be manually sorted by operator ▪ Quality issues as operator has to reference too many individual pick sheets ▪ Labor shortages

Current State ▪ Orders are printed out and manually clustered together onto pick carts/order pickers ▪ Pick assignments usually travel entire warehouse, can require equipment changes and travel to remote zones ▪ Operator travels entire warehouse without benefit of a single pick list

Solution ▪ Waving & Tasking Solution called IDEA built in Python, allows wave planner to take control and optimize work flow

Open orders are listed by priority. Note, this is Planner selects priority of orders to wave custom build based on the needs of the operation

Solution ▪ Orders are classified by type, could include single vs multi item; equipment requirements and warehouse zones. In this example Mezzanine orders are identified as this is a distinct area of the warehouse that requires extra travel.

Solution ▪ The planner can adjust the assignment settings, to set the max number of orders, picks and volume an assignment can have.

Solution ▪ In this example, orders which require a pick in the mezzanine are split out separately. The mezzanine is located on the 2 nd floor of the warehouse and requires operators to take a freight evaluator to/from. Combining picking in this area helps drive greater productivity. Main Pick Zone

Mezzanine Pick Zone (2nd floor)

Solution ▪ For planning purposes, orders are further classified by type. Mezzanine orders are classified as single vs multi and orders which require a pick in both the main pick area and mezzanine are identified. These could be waved onto separate assignments

Solution ▪ Selected orders are printed and now must be scanned into the SAP Warehouse Management System to update inventory as the system of record

Solution ▪ Task Sheet generated

In this example a task sheet is generated IDEA has optimized combining 5 multi-item this assignment, by orders with 10 pics in main combining orders that pick area have picks in just the BE & BF aisles

Each order on task must be scanned into SAP Warehouse Management System (prior to pick) as system of record of inventory by location. SAP Warehouse Management can then generate any down stream tasks (replenishment, loading) as before

Solution ▪ Task Sheet generated Order # Item # Location Qty UOM

IDEA also produces a pick sheet in location sequence, that the operator can use when picking.

Previously the operator would have a separate pick list for each order and need to manually combine them when picking

Task Sheet is generated, Sequential Pick List indicating orders SAP Pick for 20 orders / 47 lines Generated (previously picked Warehouse Management should are concentrated in two off of individual order collate) print for a pick assignment aisles Multi-item order assignment. Orders with the same item have been grouped together, Operators will now assign drastically increasing more orders on an pick density assignment with aid of pick list

Note, operation chose not to add more attributes to pick list (qty, order number) to force operator to reference individual order sheet

IDEA is an loosely integrated SAP Warehouse Management System auxiliary application focused on the intelligent optimization of picking process (generation of pick tasks, optimization of picking sequence). Integration: SAP Warehouse Management System => IDEA (Orders, Item Master, mixed): IDEA => SAP Warehouse Management System (pick acks) – using task sheet, barcode scanner and WMS GUI

Deployment status LIVE

Date Various throughout 2019 Number of users 100+ (across 4 installs)

SAP® technologies used:

Deployment status SAP product (live or proof of concept [POC]) Contribution to project

1 SAP Warehouse Management Live System of record for inventory, communication, etc

SAP Extended Warehouse 2 Live System of record for inventory, communication, etc Management

Communication of open orders to IDEA, via email SAP ERP Live 3 refresh every 15 min

The following advanced technologies were part of the project.

Technology or use case Yes or No Contribution to project

1 3D printing

2 Blockchain

3 Internet of Things (IoT)

4 Machine learning or AI

5 Conversational AI

Automated process to transfer open orders into IDEA program, 6 Robotic process automation YES create task sheet and update SAP as system of record

7 Data anonymization

Optimization of tasks using advance heuristics to group orders 8 Augmented analytics YES onto assignments to minimize travel