Factory operations modelling / scheduling /implementationegon.cheme.cmu.edu/ewo/docs/EWO_Seminar_09_29_2011.pdf · Factory operations modelling / scheduling /implementation Peter

Factory operations modelling / scheduling /implementation

Peter M.M. Bongers

Structured Materials and Process ScienceUnilever Research and Development VlaardingenThe NetherlandsChemical Engineering and ChemistryEindhoven University of TechnologyThe Netherlands

Based on ESCAPE 17-18-19 contributions by Bakker & Bongers

An industrial case study

2011-09-29/PMB Unilever Confidential 2

Unilever in a glance!


You may not know “Unilever”…


But you know our products!


UNILEVEROur mission is to add vitality to lifeWe meet everyday needs for nutrition, hygiene, and personal care with brands that help people feel good, look good and get more out of life


Unilever – the Vitality Company!

150 million times a day, in over 150 countries, people are using our products at key moments of their day…that’s 150 million opportunities to make a positive difference to people’s lives.


Unilever R&D organisation

Discover/Design

• 6000 employees• 900 million Euros (2.3% turnover; 2006)• 6 Global Research centres• 15 Global Product Development centres• Regional & country centres

Design/Deploy


Agenda

MotivationClassical process & challengesScheduling Model design

Factory structureMaterial flowConstraints / change-oversMulti-stage scheduling model

Results and concluding remarks


Motivation

Operational scheduling is Production when needed


Motivation

Operational scheduling is Flexibility in production


Motivation

Operational scheduling is Choose when/where to

produce


The GAP between theory and practice

In spite of the fact that during this last decade many companies have made large investments in the development as well as in the implementation of scheduling systems, not that many systems appear to be used on a regular basis. Systems, after being implemented, often remain in use for only a limited amount of time; after a while they often are, for one reason or another, ignored altogether

Pinedo, M. (1992). Scheduling. In G. Salvendy (Ed.), Handbook of Industrial Engineering (2nd edition). Chichester: Wiley.Interscience.


The GAP between theory and practice

Complexity- The real world aspects/constraints/rules/assumptions that are relevant

for the scheduling problem, and the relationships between those.Robustness- Robustness avoids nervousness in scheduling in situations with

uncertainty. Nervousness should be avoided as much as possibleOrganizational embedding- Alignment of the scheduling decisions with the business

Availability and accuracy of data- If this condition is not met, the scheduling model will be incorrect

Interaction with human scheduler - It is recognized by many authors that the human scheduler will remain

an indispensable factor in the scheduling process. However, manytechniques do not account for interaction with the human scheduler.


Case studyIce cream production plant


Complexity

12 packing linesLimited availability of staffConstraints on auxiliary equipment- Fruit feeders, secondary packers

Buffer tanks per line vary in number and sizePacking lines share buffersTwo process lines to feed all packing lines- Different capabilities

146 SKUs- SKU’s can be produced at multiple lines

150 recipes- Fresh dairy ingredients (shelf life)- In-house cone production (as well as bought-in)

Stringent cleaning regime on process- Allergens

Minimum and maximum standing time in buffersMandatory Cleaning In Place- 24 hour cycle on process- 72 hour cycle on all other equipment


We want to be hereMinimize capitalIncreased flexibility

Low (<50%)utilisation

High (>70%)utilisation

Single stageproduction

(dedicated resources)

Multi stageproduction

(shared resources)

Single product

Complexproduct mix

Long (=1week)production runs

Short (=1shift)production runs

large capacity marginfor future growth

De-bottle neckingexisting site

ORrationalisation manyplants into only one

“Guesstimate”inventory, storage

& delivery

Controlled inventory, storage & delivery

Modelling (Multi-stage Scheduling)

Engineering rules or rules-of-thumb

Why beyond engineering rules


Overview commercial scheduling software

http://hpsweb.honeywell.com/Cultures/en-US/Products/OperationsApplications/PlanningScheduling/RPMS/default.htm

HoneywellRMPS11

http://www.haverly.com/hsched.htmHaverly SystemsH/SCHED10

http://www.haverly.com/grtmps.htmHaverly SystemsGRTMPS9

http://www.aspentech.com/core/aspen-orion-xt.aspx

AspenTechAspen Petroleum Scheduler

8

http://www.aspentech.com/core/aspen-pims.aspxAspenTechPIMS7

http://intelligen.com/schedulepro_overview.shtmlSchedulePro6

http://www.oracle.com/us/products/applications/jd-edwards-enterpriseone/solution-manufacturing/index.html

In 2005 acquired by OracleJD Edwards5

http://www.infor.com/product_summary/scm/scheduler/

Infor Advanced scheduler

4

http://www.jda.com/company/i2-acquisition/In 2010 acquired by the JDA software group

i23

http://www.jda.com/company/manugistics-acquisition/

In 2006 acquired by the JDA software group

Manugistics2

http://help.sap.com/saphelp_apo/helpdata/en/7e/63fc37004d0a1ee10000009b38f8cf/frameset.htm

The Production Planning and Detailed scheduling component of the SAP Advanced Planning and Optimizer

SAP APO PP/DS1

Web-pageCommentVendor or Product Name


Necessary conditions to enable successful implementation

Communication with the other factory software, such that master data is only stored in one location to prevent errors. Master data contains information such as specific capacities, bill of materials, routing, alternatives, etc.

•

The software need to be able to deal with break-down of the equipment, and that buffer vessels might be partially full.

•

The schedule of the complete plant need to be generated in a relative short period of time (faster than appr. 15 min. or one cup of coffee) in order to deal with decisions on the factory floor.

•

The solution, or schedule, need to be robust rather than optimal to the last decimal digit. One important item of the robustness is that the same schedule is generated even if there are small changes in the inputs.

•

The software should be usable by the plant scheduler, who, in general does not have an academic grade.

•

The graphical user interface for the plant scheduler needs to be intuitive and represent the plant.

•

The model of the plant need to be a sufficient accurate representation of the real situation.•


Why look outside SAP PP/DS

SAP is leading and there is only ONE plan !- Everything that is critical within 24 hours has to be handled

outside SAP PP/DS:The material requirements in SAP are generated for a complete run OR 24 hours (which ever is the shortest should be selected)

- Some processes are not suitable for SAP PP/DS:Buffer vessels or intermediate buffersShared resources

» Retorts, mixers, mechanics, CIP, …Multiple manufacturing levels

» Packing, ageing, pasteurising, pre-mixing, …- Everything that is non-operational has to be handled outside

SAPScenario’s, Change-of-plans


Scheduling complexitySh

ared

res

ourc

es a

t le

vel 3

2

1

1 2 3

Buffer vessels at level

Increased scheduling complexity

SAP-PP/DS


Reduced complexity model to evaluate commercial scheduling software

Packing Line 2

4000 kg

F2

Packing Line 1

F1

Process Line

4500lph

8000 kg


Information

22000mix HSKU H

22000mix GSKU G

22000mix FSKU F

21750mix ESKU E

11500mix DSKU D

11000mix CSKU C

11500mix BSKU B

11750mix ASKU A

packing LinePacking rate[kg/hr]

composition

product

722mix H722mix G722mix F722mix E720mix D723mix C723mix B721mix A

Maximumshelf-life [hr]

Minimumstanding time [hr]

product

24000SKU H48000SKU G12000SKU F112000SKU E8000SKU D32000SKU C48000SKU B80000SKU A

Quantity [kg]product

SKU information mix information

Production demand


Change-over information

03030300000120SKU H60030300000120SKU G60600300000120SKU F60606000000120SKU E00000303030120SKU D00006003030120SKU C00006060030120SKU B00006060600120SKU A1201201201201201201201200idle

SKU HSKU G

SKU F

SKU E

SKU DSKU C

SKU B

SKU A

idleto from

055530303030120mix H1505530303030120mix G15150530303030120mix F151515030303030120mix E303030300303030120mix D303030303003030120mix C303030303030030120mix B303030303030300120mix A1201201201201201201201200idle

Mix H

mix Gmix Fmix E

mix D

mix C

mix BMix A

idleto

from

Packing

Process


So far only INFOR could produce a feasible schedule


Methodology Multi-Stage Scheduling

Specifications

DATA

InterviewsAnalysis

BoM

Routing

Change-overdata

ProductionSchedule

IntermediateSchedules

IngredientCall-offSchedule

Sourcing-UnitSimulation ModelINPUTS

ProductionPlan

Inventory

PurchaseOrders

software implementation

‘paper’-model

Change-overStructure

SourcingUnitModel

Constraints

FactoryStructure

PFD InterviewsSOPs RoutingIntermediate

BoM

Material FlowStructure

InterviewsSOPsPFD

‘meta’-modelSystems theory

modellingprocess engineering


Factory structure

Constraints on Packing• Hardening Tunnel• Glue machines• Fruit feeder• Secondary packing machine

F650 Rollo2

F670 F680 F660

Rollo3

F570 F600

Rollo4

F640 SL1

F560 F620

SL2

F580 F750

SL3

F740

F720 ILF1, ILF2, Viking1

F730 F530

ILF2

F710 F540

mini Vienn.

F550 F700

Viking2, ILF1

F590F610

Viking1, ILF1, Vienn.

F630

Ingredients

storage

PA1 6000lph

PA2

4500lph

Rollo1 Rollo3

SL1

Rollo1 Rollo2 Rollo3

Rollo1

Rollo2

Rollo4

SL2

Rollo3

SL2

Rollo1 Vienn .

Rollo2

Rollo4

Rollo2

Rollo4


Material flow

Packing lines (SKU/CU)

Pasteurisers

AgeingVessels

Conesinventory

IngredientInventory

Cone Bakers Sauce Mixer

SauceVessels

Choc Grinders

ChocolateVessels

JellyVessels

FestiniVessels

Mix Supply Line

Freezers

Purchase Orders

Flavour Mixer

FlavourVessels

ConesSKU

CU inventory

Packing lines (MP)

Choc.SKU


Change-over information

Packing lines

PasteuriserColour, allergens


Factory model overview

Productionlines

Ageing tanks

Pre-mixerspasteuriser

Time axis

Shift pattern

Non working time

Production order or batch

Change-over

Emptyingwaiting

ageingfilling


Conclusions

Implementation of operational multi-stage scheduling leads to:- Feasible route to reduced cost/tonnes manufactured

products by:raw material waste reduction increased available capacity (upto 30% on factory level)

- In CONTROL of MAKE


Barriers / challenges

General- Problems occur at the interfaces- Factory data is not reliable, in-consistent, or not-

existentSpecifications, Flow diagrams, line-speeds, shift variations, …

Operational implementation- BSc level needed for running the system- Cultural change necessary

Packing line operators no longer “in charge”Master data maintenance (also now in SAP)


Thanks!

Documents

Factory operations modelling / scheduling /implementationegon.cheme.cmu.edu/ewo/docs/EWO_Seminar_09_29_2011.pdf · Factory operations modelling / scheduling /implementation Peter