factory operations modelling / scheduling...
TRANSCRIPT
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
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Unilever in a glance!
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You may not know “Unilever”…
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But you know our products!
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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
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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.
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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
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Agenda
MotivationClassical process & challengesScheduling Model design
Factory structureMaterial flowConstraints / change-oversMulti-stage scheduling model
Results and concluding remarks
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Motivation
Operational scheduling is Production when needed
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Motivation
Operational scheduling is Flexibility in production
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Motivation
Operational scheduling is Choose when/where to
produce
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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.
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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.
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Case studyIce cream production plant
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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
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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
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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
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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.•
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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
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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
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Reduced complexity model to evaluate commercial scheduling software
Packing Line 2
4000 kg
F2
Packing Line 1
F1
Process Line
4500lph
8000 kg
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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
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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
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So far only INFOR could produce a feasible schedule
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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
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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
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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
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Change-over information
Packing lines
PasteuriserColour, allergens
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Factory model overview
Productionlines
Ageing tanks
Pre-mixerspasteuriser
Time axis
Shift pattern
Non working time
Production order or batch
Change-over
Emptyingwaiting
ageingfilling
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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
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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)
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Thanks!