Public Supply Plan Optimization with the SAP IBP S&OP ...into the SAP Integrated Business Planning...

Christoph Eckert, SAP SCM Optimizer Development

Gurobi User Conference, Feb 06, 2017

Supply Plan Optimization with the

SAP IBP S&OP Optimizer

Public

© 2017 SAP SE or an SAP affiliate company. All rights reserved. 2Public

Agenda

History of Optimization at SAP

SAP Integrated Business Planning for Sales and Operations Planning (SAP IBP S&OP)

S&OP Model and Features

Some Figures

Summary

History of Optimization at SAP


Optimization at SAPThe 90’s

First optimization engines built end of the 90’s

Optimization for SCM in SAP Advanced Planning and Optimization (SAP APO)

Production Planning & Detailed Scheduling (PP/DS)

Supply Network Planning (SNP)

– MILP approach using commercial solver

Deployment


Transportation Planning / Detailed Scheduling (TP/VS)

Model Mix Planning (MMP)


Sequencing (SEQ)



Added further applications in the 2000’s

Further components of SAP APO

Capable to Match (CTM)

SAP Transportation Management (SAP TM)

Vehicle Scheduling and Routing (VSR)

Carrier Selection (CS)

– MILP approach using open source solver with commercial license

SAP Multiresource Scheduling (SAP MRS) – Planning of Technicians



More engines using (MI)LP since the 2010’s

All using open source solver with commercial license

Genios – Optimization on SAP NetWeaver

– Customer projects, e.g. “Trim Optimization”

SAP Integrated Business Planning for Sales and Operations Planning (SAP IBP S&OP)

Optimization Function Library (OFL) – Optimization on SAP HANA

– e.g. “Retail-Assortment-Planning”

And more other engines

SAP Transportation Management - Load Planning (SAP TM LP)

SAP Integrated Business Planning for Response (SAP IBP Response)

SAP Transportation Management – Load Consolidation (SAP TM LC)


Optimization at SAPSAP chooses Gurobi

SAP chooses Gurobi as new OEM Partner

June, 2016 – SAP, the leading enterprise software provider in supply chain management, has chosen

Gurobi as an OEM partner. As a result of this agreement, the Gurobi Optimizer has been integrated

into the SAP Integrated Business Planning (SAP IBP) and Advanced Planning and Optimization (SAP

APO) solutions to solve complex optimization problems. In addition, the Gurobi Optimizer enabled a

seamless integration into the SAP HANA in-memory-platform.

http://www.gurobi.com/company/news/sap-chooses-gurobi

Applications using Gurobi as solver

SAP APO – SNP and Deployment (replacing other commercial solver)

SAP IBP S&OP (replacing open source solver)

Enhancements for SAP HANA platform

Memory handling

Thread handling

http://www.gurobi.com/company/news/sap-chooses-gurobi


Optimization at SAPSAP chooses Gurobi

Effort for switching solver to Gurobi

• Implementation of API-mapping internal (MI)LP representation – GUROBI model and solver (effort

already done some time ago, only technical mapping on API basis)

• Intensive tests on a set of approx. 1000 scenarios – Compare time to optimum or solution quality

after given runtime, depending on customer setting

• 86% w/o problems: 52% comparable, 34% better results

• 9% solved using Gurobi tuning tool (most times default setting was sufficient) or mapping of previous solver

specific parameters to similar Gurobi solver parameters (e.g. MIPFocus, ScaleFlag, NumericFocus,…)

• Only few scenarios not ‘solved’ by intensive tuning, some large scenarios not tried to tuned at all

• → Very few problems at customers, merely in cases using special solver parameters with the old solver, but

such scenarios had intense parameter tuning for the old solver which we didn’t now

• After productive switch only few problems with regards to the change, most could be solved on our

own using experience from tests, only very few needed help from Gurobi support, which reacted

very quickly and proposed a solution

SAP Integrated Business

Planning for Sales &

Operations Planning


SAP Integrated Business PlanningWhat is IBP?

Tom Wallace Definition

Integrated Business Planning is a set of decision-making processes to:

Balance demand and supply

Integrate financial planning and operational planning

Link high-level strategic plans with day-to-day operations

“Integrated Business Planning (IBP) is the business planning process

for the post-recession era, extending the principles of S&OP

throughout the supply chain, product and customer portfolios,

customer demand and strategic planning, to deliver one seamless

management process.”


SAP Integrated Business PlanningHigh Level Solution Architecture

SAP Integrated Business Planning

Excel: Plan

Sales/

Marketing

Demand

Planning

Finance

Supply

Chain

Executives

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SAP HANA™

SAP Jam™

IBP Model & EngineAttributes

Key Figures

Scenarios & Simulation

Predictive Analytics

Heuristics & Optimization

Process Workflow

Alerts

Social Collaboration

Web: Analyze

Mobile: Analyze

ERP

CRM

BPC

APO

BW

Any other

system

Files


SAP Integrated Business PlanningSolution Components

Sales & Operations Planning

Supply Chain Control Tower

DemandResponse &

SupplyInventory

SAP HANA Platform

Challenge Possible Deployment Priority

Struggling to synchronize supply and

demand?


for Sales and Operations

Difficult to determine optimal inventory

levels that support your high customer

service level - across the network?


for Inventory

Unable to manage and plan across the

complex supply network?


for Supply

Inability to sense actual demand for a

short-term horizon?


for Demand

Need to respond to short term changes in

supply and demand?


for Response

Lack of visibility and information to analyze

and manage the end-to-end supply chain?

SAP Supply Chain Control Tower


SAP IBP for Sales & Operations PlanningSales and Operations Planning Process

• Consensus Demand Planning and New Products

• Input from Sales, Marketing, Finance, Operations including quantity, price, revenue and margin

1. Demand

• Based on updated demand plan, understand capacity and material impacts including contract manufacturing

• Understand long term gaps in supply and demand2. Supply

• Review and resolve outstanding supply issues

• Compare constrained plan with finance plan

• Define and compare scenarios 3. Balance

• Escalate resolution plans with scenarios if applicable

• Review key changes since last meeting4. Executive Review

Repeat

Monthly

Typical Process


SAP IBP for Sales & Operations PlanningWeb UI used for Analytics


SAP IBP for Sales & Operations PlanningExcel UI used for Planning

S&OP Model and Features


SAP IBP Sales & Operations PlanningThe Model – Master and Operational Data

DCPlantSupplier CustomerProductResource

Master Data Model

Location (Plant, DC, Supplier, ...) Lane Product Production Process Model (PPM) Resource

Demands Orders Capacity Profiles

Operational Data (Time Series)


SAP IBP Sales & Operations PlanningThe Model – Features

TransportDiscrete Lots

Minimal Lots

Fix Costs and Cost Rate

Satisfy DemandDelay Costs

Non-Delivery CostsProcure


PPM

Products

ProduceDiscrete Lots

Minimal Lots


Store

Storage

Capacity

Safety Stock

Transport-

Capacity

Production

Capacity


SAP IBP Sales & Operations PlanningThe Model – Typical Supply Chain

SUP 302

SUP 301 Customer 1

1 A

A Product Capacity

Resource 1 Material

Customer 2

A

Customer 3

A

DC 101

A

DC 102

A

Location

4

4

5

MFG 202

A

1

6

35

4

Multi-level

BOM

MFG 201

A

1

6

35

4

Multi-level

BOM

Production

Source


SAP IBP Sales & Operations PlanningConstraints - Examples

Stock balance equation:

𝑺𝒕𝒐𝒄𝒌 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕

= 𝑺𝒕𝒐𝒄𝒌 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 − 𝟏 +

𝑷𝒑𝒎

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒑𝒑𝒎, 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕

−

𝑷𝒑𝒎_𝒏𝒆𝒆𝒅𝒔𝒊𝒏𝒑𝒖𝒕

𝑷𝒓𝒐𝒅_𝒙

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒑𝒑𝒎_𝒏𝒆𝒆𝒅𝒔𝒊𝒏𝒑𝒖𝒕, 𝒑𝒓𝒐𝒅_𝒙, 𝒍𝒐𝒄, 𝒕 + 𝒍𝒆𝒂𝒅𝒕𝒊𝒎𝒆

+

𝑭𝒓𝒐𝒎𝒍𝒐𝒄

𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕(𝒑𝒓𝒐𝒅, 𝒇𝒓𝒐𝒎𝒍𝒐𝒄, 𝒍𝒐𝒄, 𝒕) −

𝑻𝒐𝒍𝒐𝒄

𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕𝒐𝒍𝒐𝒄, 𝒕 + 𝒍𝒆𝒂𝒅𝒕𝒊𝒎𝒆

Maximum inventory:

𝑺𝒕𝒐𝒄𝒌 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 − 𝑺𝒍𝒂𝒄𝒌𝑴𝒂𝒙𝑺𝒕𝒐𝒄𝒌(𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕) ≤ 𝑴𝒂𝒙𝑰𝒏𝒗𝒆𝒏𝒕𝒐𝒓𝒚𝑳𝒆𝒗𝒆𝒍(𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)

Inventory target:

𝑺𝒕𝒐𝒄𝒌 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 + 𝑺𝒍𝒂𝒄𝒌𝑻𝒂𝒓𝒈𝒆𝒕𝑺𝒕𝒐𝒄𝒌(𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕) ≥ 𝑰𝒏𝒗𝒆𝒏𝒕𝒐𝒓𝒚𝑻𝒂𝒓𝒈𝒆𝒕𝑳𝒆𝒗𝒆𝒍(𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)



Minimum and maximum lot-size:

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏(𝒑𝒑𝒎, 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕) ≤ 𝑴𝒂𝒙𝑳𝒐𝒕𝑺𝒊𝒛𝒆(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)

resp.

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 ≥ 𝑴𝒊𝒏𝑳𝒐𝒕 𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 ∗ 𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝑴𝒊𝒏𝑷(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 ≤ 𝑴𝒂𝒙𝑳𝒐𝒕 𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 ∗ 𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝑴𝒊𝒏𝑷(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)

Maximum:

𝟎 ≤ 𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕) ≤ 𝑴𝒂𝒙𝒊𝒎𝒖𝒎(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)

Minimum:

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒑𝒑𝒎, 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 + 𝑺𝒍𝒂𝒄𝒌𝑳𝑩𝑷(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕) ≥ 𝑴𝒊𝒏𝒊𝒎𝒖𝒎(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)



Resource capacity:

𝑹𝒆𝒔_𝒑𝒑𝒎

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏(𝒓𝒆𝒔_𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕) ≤ 𝑹𝒆𝒔𝑪𝒂𝒑𝒂𝒄𝒊𝒕𝒚(𝒓𝒆𝒔, 𝒕)

Minimum resource consumption:

𝑹𝒆𝒔_𝒑𝒑𝒎

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏(𝒓𝒆𝒔_𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕) + 𝑺𝒍𝒂𝒄𝒌𝑴𝒊𝒏𝑹𝒆𝒔(𝒓𝒆𝒔, 𝒕) ≥ 𝑴𝒊𝒏𝑪𝒐𝒏𝒔𝒖𝒎𝒑𝒕𝒊𝒐𝒏(𝒓𝒆𝒔, 𝒕)

Fix costs:

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒑𝒑𝒎, 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 ≤ 𝑩𝒊𝒈𝑴 ∗ 𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝑭𝒊𝒙𝑷(𝒑𝒑𝒎,𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕)

Demands:

𝑫𝒆𝒎𝒂𝒏𝒅 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕

= 𝑫𝒆𝒍𝒊𝒗𝒆𝒓𝒆𝒅 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕 +

𝑳𝒂𝒕𝒆_𝒕

𝑳𝒂𝒕𝒆𝑫𝒆𝒍𝒊𝒗𝒆𝒓𝒚 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕, 𝒍𝒂𝒕𝒆_𝒕 + 𝑵𝒐𝒏𝑫𝒆𝒍𝒊𝒗𝒆𝒓𝒚 𝒑𝒓𝒐𝒅, 𝒍𝒐𝒄, 𝒕


SAP IBP Sales & Operations PlanningCost

Production, Transport and Procurement

Fix cost

Cost rate

Violation cost rate for minimum and adjusted values (pseudo hard)

Resource

Minimum resource capacity utilization violation cost rate

Inventory/Stock Holding

Inventory holding cost rate

Maximum inventory violation cost rate

Safety stock violation cost rate

Minimum and maximum coverage violation cost rate

Demand

Non delivery cost rate

Late delivery cost rate


SAP IBP Sales & Operations PlanningObjective

Objective function:

𝑴𝑰𝑵(

𝑫𝒆𝒎𝒂𝒏𝒅

𝑵𝒐𝒏_𝑳𝒂𝒕𝒆_𝑫𝒆𝒍𝒊𝒗𝒆𝒓𝒚 𝒅𝒆𝒎𝒂𝒏𝒅 ∗ 𝑵𝒐𝒏_𝑳𝒂𝒕𝒆_𝑫𝒆𝒍𝒊𝒗𝒆𝒓𝒚𝑪𝒐𝒔𝒕𝒓𝒂𝒕𝒆 𝒅𝒆𝒎𝒂𝒏𝒅

+

𝑷𝒑𝒎

𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏 𝒑𝒑𝒎 ∗ 𝑷𝒓𝒐𝒅𝑪𝒐𝒔𝒕𝒓𝒂𝒕𝒆 𝒑𝒑𝒎 +

𝑷𝒑𝒎

𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓𝑭𝒊𝒙𝑷 𝒑𝒑𝒎 ∗ 𝑷𝒓𝒐𝒅𝑭𝒊𝒙𝑪𝒐𝒔𝒕 𝒑𝒑𝒎

+

𝑷𝒑𝒎

𝑩𝒊𝒈𝑴 ∗𝑺𝒍𝒂𝒄𝒌𝑳𝑩𝑷 𝒑𝒑𝒎 + 𝑻𝒓𝒂𝒏𝒔𝒑𝒐𝒓𝒕, 𝑷𝒓𝒐𝒄𝒖𝒓𝒆𝒎𝒆𝒏𝒕

+

𝑳𝒐𝒄𝑴𝒂𝒕

𝑺𝒕𝒐𝒄𝒌 𝒍𝒐𝒄𝒎𝒂𝒕 ∗ 𝑰𝒏𝒗𝒆𝒏𝒕𝒐𝒓𝒚𝑪𝒐𝒔𝒕𝒓𝒂𝒕𝒆 𝒍𝒐𝒄𝒎𝒂𝒕

+

𝑳𝒐𝒄𝑴𝒂𝒕

𝑺𝒍𝒂𝒄𝒌_𝑻𝒂𝒓𝒈𝒆𝒕_𝑴𝒂𝒙_𝑺𝒕𝒐𝒄𝒌 𝒍𝒐𝒄𝒎𝒂𝒕 ∗ 𝑻𝒂𝒓𝒈𝒆𝒕_𝑴𝒂𝒙_𝑺𝒕𝒐𝒄𝒌𝑽𝒊𝒐𝒍𝒂𝒕𝒊𝒐𝒏𝑪𝒐𝒔𝒕𝒓𝒂𝒕𝒆 𝒍𝒐𝒄𝒎𝒂𝒕

+

𝑹𝒆𝒔

𝑺𝒍𝒂𝒄𝒌𝑴𝒊𝒏𝑹𝒆𝒔 𝒓𝒆𝒔 ∗ 𝑴𝒊𝒏𝑹𝒆𝒔𝑪𝒐𝒏𝒔𝑽𝒊𝒐𝒍𝒂𝒕𝒊𝒐𝒏𝑪𝒐𝒔𝒕𝒓𝒂𝒕𝒆)

Some Figures


SAP IBP Sales & Operations PlanningFigures and Numbers

IBP S&OP customers by industries

Oil and Gas/Chemical

Consumer Products/CPG

Beverage

High-Tech

Consumer Durables



Scenario 1 2 3 4 5 6 7 8

Locations 10128 153 886 1149 196 747 85 121

Products 1380 22640 14356 22854 37 312 6970 327

Location Products 41131 119886 56789 404347 1877 9483 20763 48768

Resources 394 5 30 200 12 13 147 5

Transportation Rules 40885 99651 42274 378436 3706 9700 13089 24092

Production Rules 2604 18905 828 7607 89 586 10593 324

Procurement Rules 47 10489 14282 18486 6 17 2271 3

Buckets 25 25 88 121 28 37 27 186

IBP S&OP scenarios by master data numbers


IBP S&OP scenarios by mathematical model

* Virtualized 20 x Intel Xeon [email protected], 256GB main memory, Windows Server 2012 R2

** Time limit used by customer, but by far not the time to the optimum


Scenario 1 2 3 4 5 6 7 8

Continuous Variables 2.3M 4.7M 16.9M 99.8M 170439 501592 798708 10.3M

Binary Variables - - - - 2450 11229 28347 8340

Integer Variables - - - - 6350 - 54963 -

Linear Constraints 1.6M 1.9M 7.8M 50.5M 91573 417918 658655 5.3M

Runtime* 21s 165s 18min 7:15h 2:00h** 1:24h 1:00** 2:00**

Memory* 2GB 3.7GB 13.4GB 89.6GB 2.4GB 3.4GB 2.2GB 11.8GB

Summary


SAP IBP Sales & Operations PlanningSummary, Challenges & Outlook

Achievements

Good cooperation with Gurobi

Performance of the Solver

Replacement of previous solvers without major problems

Challenges

Very fast response times expected for simulation purpose in S&OP

Numerics

Outlook

Test (hierarchical) multiple objectives to overcome numerical problems by pseudo-hard constraints with big-M

coefficients in the objective

Thank youContact information:

Christoph Eckert

Senior Developer

SCM/IBP/TM Optimizer Development

Products & Innovation | IoT Logistics

SAP SE

[email protected]

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