we create chemistry with optimization · pdf filewe create chemistry with optimization w. alex...

March 23-24, 2017

Doing It Right

SYMPOSIUM

We Create Chemistry with Optimization

W. Alex MarvinBASF Corporation

Advanced Business Analytics

[email protected]

Big Data Symposium – March 23-24, 2017

BASF – 150 years at a glance

2

1865-1901

The age of dyes

1902-1924

The Haber-Bosch-Process and the age of fertilizers

1902-1924

New high-pressure syntheses

1945-1964

From new beginnings to the plastic age

1965-1989

The road to becoming a trans-national company

1990-2015

Sustainable start to the new millennium

4th Big Data & Business Analytics Symposium – March 23-24, 2017

BASF – We create chemistry

Our chemistry is used in almost all industries

We combine economic success, social responsibility and environmental protection

Sales 2016: €57,550 million

EBIT 2016: €6,275 million

Employees (as of December 31, 2016): 113,830

6 Verbund sites and 352 other production sites


BASF worldwide: sites

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SingaporeKuantan

Hong KongNanjing

Freeport

Florham ParkGeismar

LudwigshafenAntwerp

São PauloRegional centers

Selected sites

Verbund sites

Selected research and development sites


BASF’s segments

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Crop Protection

PerformanceProducts

Dispersions & PigmentsCare Chemicals

Functional Materials & Solutions

AgriculturalSolutions

Nutrition & HealthPerformance Chemicals

Oil & Gas

Oil & Gas

Chemicals

Monomers

Intermediates

Petrochemicals

Functional Materials & Solutions

ConstructionChemicals

Coatings

Catalysts

Performance Materials


Digitization will support BASF’s growth and efficiency


Advanced Business AnalyticsTurning BIG data into SMART data

BIG Data

Volume: Scale of Data

Velocity: Streaming Data

Variety: Different Forms of Data

Veracity: Uncertainty of Data


Advanced Business AnalyticsInnovative solutions for all areas in business and operations

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Methods &

Modeling

BASF Group

Operating Divisions

Functional and Central Divisions/Units

Tailored

Scalable

Understand Create Deliver

Analysis

Scope

Solution type

Marketing & SalesProduction

Procurement Supply Chain

Planning & Controlling Finance

…

Explorative Ideas

Business Needs


Providing insights and decision support for various fields in supply chain managementExamples

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Inventory & Demand Planning Analytics

Safety & Target Stock Optimization incl. Advanced SNP Optimizer

Dysfunctional Inventory Analytics

Demand Planning Analysis ….

Network optimization

Optimal design of supply chain networks

Covering e.g. distribution, inventories, raw materials, taxes and duties

Scenario analysis …

Demand Forecasting

Advanced forecasting applying predictive methods on aggregated and disaggregated level

….

time

demand

Value chain optimization

Optimal value-based medium- and long-term steering of value chains

What-if scenarios, e.g. capacity increases, low price strategies

Sensitivity analysis, e.g. CM sensitivity on demand, raw material costs

…SNP: Supply Network Planning, CM: Contribution Margin


Global Business AnalyticsValue based network design and operation

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Increase transparency and facilitate medium-/long-term decision-making for margin, volume and capacity optimizationObjective:

Production complexity - bottleneck situations - investments - volume-margin tradeoffs production sites in different regions joint ventures and tollers >10 production stages, coupled production

>100 intermediates, >50 by-products,>100 sales products

customers from different industries in all regions

Optimization-based scenario-tool for strategic planning & controllingSolution:4th Big Data & Business Analytics Symposium – March 23-24, 2017

Prerequisites

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Database

Execution

ERP system

Make sure data is correct

Make sure KPI definitions fit

Make sure all relevant information is considered

Optimization tool

Database

ExecutionMake sure people believe in the concept

Make sure people trust the system

Make sure plans are understood

Make sure plans are executed

Reporting tool


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Find optimal plan under complex constraints

Production

Customers InventoryTransportation & Logistics

Raw materialscontribution

marginPackaging

CapacitiesCapacity restrictions, safety stock levels, coverage

Capacities

Availability, minimal runsChangeovers

Service levelsSourcing restrictions

Availability4th Big Data & Business Analytics Symposium – March 23-24, 2017

Production sites

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Optimal Sales & Operations Planning

Network setting

10 sites 30 production units 800 products 50 sales regions

Objective

Maximize contribution margin for next 3 months


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Approach and Results

Optimum in terms of Optimal product flows Capacity utilization Sales and distribution

Benefits Cash Flow increase Reduction of stock levels by 24% Increase of service reliability of 8%

Results and Benefits

Too complex to be done intuitively Use mixed-integer programming

60,000 decision variables 30,000 constraints

Solution approach


Planning in a matrix

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Supply Chain Planning Matrix based on Fleischmann et.al., 2008

During implementation of Enterprise Resource Planning (ERP) systems, planning tasks have been defined according to software modules

S&OP is the task to ensure that planning processes are synchronized


From Unlinked data models

Reactive

Limited view

To Integrated data model

Proactive

Constraints visible

S&OP requires change in many areas

Process/Data

Technology/Tools From Mostly Manual

Multiple data sources

Cumbersome what-if analysis

Limited data visibility

To Automated

Single database

Quick simulations

Broad data visibility

People/OrganizationFrom Functional view

Independent

Metrics-functional

Many fragmented decisions

Unit focused

To Cross functional view

Ongoing communication

Metrics-process cycle time

Executive decisions

Margin focused

Senior Management commitment is essential


Specialties of planning problems in the process industry

Process challenge

Inverted Bills

Multiple Recipes

Multi-Level Planning

ImplicationProducing products to fulfill demand generates other products that must be sold or further processed. By-products, co-products and waste must be taken into account and matched to existing demand or used to meet other demands. Waste products consume capacity for recycling or disposal.

Products can be made in multiple ways, and often with different ingredients depending on what is available and what current costs are. That leads to multiple trade-off scenarios to produce a single item.

Production of bulk material must be planned concurrently with packaging operations. Demand for products often comes in different packaging configurations. When fixed or economical batch sizes are produced, this amount of bulk material typically does not match exactly packed demand, so inventory must be stored in tanks, packed off in product or intermediate containers, or disposed of.

4th Big Data & Business Analytics Symposium – March 23-24, 2017 18

Process challenge

Changeover / Sequencing

Tanks / Silos

Implication

Process plant productivity is very sensitive to product transitions, and properly sequencing these transitions are extremely important to improving yields and reducing changeovers. Cleanups must be taken into account, and product wheels and changeover matrices provide critical planning criteria when planning product mix and sequencing. Some process plants cannot be shut down between products and produce transition products during changeover. These transition products must be handled as push products.

Inventory and work-in-process material is often stored in tanks or silos. Planning for production in tanks and silos must not only address the time required for the operation, but also the volume of the material. The inflow from one operation and the outflow from the next dictate when the tank is available, and upstream and downstream operations have to be scheduled synchronously to ensure that the tank does not overflow.



Process challenge

Variability

Push-Pull

Implication

Material used and produced often have variable qualities based on their chemistry and structure. Organic products, in particular, are often difficult to procure and produce in consistent composition, and planning must take into account this unpredictability on both a proactive and reactive basis.

When bulk materials are produced, they must be packaged, further processed, sold, or disposed of. Push-pull scenarios can come from an integrated plant that continuously provides feedstock that must be processed, inverted bills leading to by-products, co-products or waste, unexpected products or material grades that come from difficult to control processes, transition products, or other sources.



we create chemistry with optimization · pdf filewe create chemistry with optimization w. alex...

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