Production planning and sales allocation in the salmon farming industry

232 Pages·2015·5.06 MB·English

Production planning and sales allocation
in the salmon farming industry
Are Gederaas Denstad
Miriam Lillevand
Einar Askevold Ulsund
Industrial Economics and Technology Management
Submission date: June 2015
Supervisor: Asgeir Tomasgard, IØT
Norwegian University of Science and Technology
Department of Industrial Economics and Technology Management

Problem formulation
In this thesis, a multistage stochastic optimization model for planning of production
and allocation of sales in the salmon farming industry will be developed. The model
maximizes the expected profit. The objective of the thesis is to provide decision sup-
portregardingwhentoharvestfish,howtodistributeharvestedfishbetweendifferent
product groups, how to allocate sales between contracts and spot sales in different
markets and how to transport products within the production network and to the
market.
The thesis will include the following elements:
- Presentationanddiscussionoftheproblem,includingadiscussionoftherelevant
uncertainty parameters
- Formulation and implementation of a stochastic optimization model
- Discussion of the results from a test case
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Summary
In this thesis a multistage stochastic optimization model has been developed for pro-
duction planning and sales allocation within the salmon farming industry. To the
authors knowledge, an optimization model for this problem has not been developed
before.
Thebackgroundforthisthesisisatacticalplanningproblemspanningproductionand
salesunderuncertainty. Thegoalistoidentifyprofitablesolutionsthataresufficiently
flexible in accounting for this uncertainty.
ThemodelisinspiredbytheworkdonebyHæreid(2011),butaddressesmoreaspects
of the value chain. The most important innovations in the model is that processed
productsandinventorymanagementisincluded. Furthermore,thescopeareextended
to include global operations. Additionally, the model offers a more detailed modeling
of contract and transport decisions. The model also incorporate both market and
product price dependencies.
The model is implemented in two versions: one deterministic and one three-stage
stochastic version. To illustrate how they can be used, the different models are tested
on instances inspired of Marine Harvest’s global production network. The input data
used are however based on publicly available information. To model uncertainty in
salmon price and sea water temperature, different seasonal ARIMA-models are used
to model these uncertain time series. Based on these forecasts, residual scenarios are
generated by using a copula-based scenario generation heuristic.
The developed model acts according to the authors initial assumptions and gives rea-
sonable results compared to how the industry operates as of today. The value of
including stochastic programming constitutes 5 % savings, and tests indicate that the
model holds in-sample stability.
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Sammendrag
I denne masteroppgaven har en multi-stegs stokastisk optimeringsmodell for produk-
sjonsplanlegging og salgsallokering innen lakseoppdrett blitt utviklet. S˚avidt forfat-
terene har kjennskap til, er det aldri tidligere blitt utviklet en optimeringsmodell for
dette problemet.
Bakgrunnen for oppgaven er et taktisk planleggingsproblem som omhandler produk-
sjon og salgsallokering under usikkerhet. M˚alet med denne modellen er˚a identifisere
profitablebeslutningersomertilstrekkeligfleksibletil˚ah˚andteredenneusikkerheten.
ModellenerinspirertavarbeidettilHæreid(2011),mentarforsegenlangtstørredel
av verdikjeden. De viktigste nyvinningene i modellen er at prosesserte produkter og
lagerstyring er inkludert. Videre er omfanget utvidet til˚a favne global virksomhet. I
tillegg tilbyr modellen en mer detaljert modellering av kontrakter og transportbeslut-
ninger, og lar priser avhenge av hvilket marked handelen gjennomføres i.
Modellen er implementert i to versjoner; en deterministisk og en tre-stegs stokastisk
versjon. For ˚a illustrere hvordan de kan brukes i praksis er modellene blitt testet
p˚a instanser inspirert av the globale produksjonsnettverket til Marine Harvest. Data-
grunnlagetsomerbrukterbasertp˚aoffentligtilgjengeliginformasjon. For˚amodellere
usikkerhet i pris og vanntemperatur er prognoser utviklet ved hjelp av ulike sesong-
baserte ARIMA-modeller. Basert p˚a prognosene er det generert et sett med scenarier
ved hjelp av et copula-basert scenariogenereringsverktøy.
Modellen som er utviklet oppfører seg som tiltenkt og gir resultater som er fornuftige
sett i sammenheng med bransjen. Verdien av˚a inkludere usikkerhet er vist˚a utgjøre
5%, og tester indikerer at modellen innehar in-sample stabilitet.
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Preface
This master thesis is written within Applied Economics and Operations Management
at the Department of Industrial Economics and Technology Management, Norwegian
University of Science and Technology (NTNU). This thesis is motivated by the op-
erations of the global salmon producer Marine Harvest and previous master theses
written on the same subject.
We have received much appreciated help and guidance in completing this thesis, both
from faculty at NTNU, co-students and industry contacts. We would especially like
tothankoursupervisor, professoratNTNU,AsgeirTomasgardforhispatience, guid-
ance, interesting discussions and valuable feedback. We would also like to thank Tor-
mod Johannessen and his colleagues at Marine Harvest for valuable help in answering
questions about both Marine Harvest and the industry in general.
We would additionally like to express our gratitude to Stein Erik Fleten, professor at
NTNU, H˚akon Tjelmeland, professor at NTNU, Paul T. Aandahl, analyst the Norwe-
gianSeafoodCouncilandMichaelKaut, researcheratSINTEF.Ourworkhasgreatly
benefited from the help and feedback received from all mentioned above.
Trondheim, June 20, 2015
Miriam Lillevand
Are G. Denstad Einar A. Ulsund
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