Good to Great How : Marine Harvest Can Innovate to Grow, and Grow to Innovate Amid Imperfect Industry Structure and Regulation
Notice bibliographique
Résumé
Spatial conflicts exist between firms and farms in the Norwegian Salmon Farming industry which perpetuates sea lice and disease issues and prevents the industry from growing. Lack of growth in Norway in the face of increasing demand, increases prices and reveals national market power. As salmon are treated for sea lice more often, the fish grow less, and die more frequently thus exacerbating this effect. As a result, consumer surplus goes down and dead loss goes up as the industry becomes less efficient. Gaps in the underlying regulatory framework and lack of coordination among firms allows this to continue and thus jeopardizes sustainability of the industry. As a result, Marine Harvest Norway Farming (MHNF), being 20 – 25% of the industry volume, is disproportionately affected. This may put the Marine Harvest Group (MHG) strategy in jeopardy, as without growth, it becomes increasingly difficult for MHG to build customer and shareholder value. In this thesis I identify and analyse the spatial externalities, identify the regulatory gaps, and make suggestions for improvements. Further I analyse the effect on MHNF and how it jeopardizes the MHG strategy. A strategic analysis of MHNF reveals that they have the resources and existing strategy to deal with some of the gaps and externalities but not all, and only if they execute well and on time. Further I suggest how the strategy can be improved by focusing on a few key points including: increasing their government lobby for tougher lice rules and more coordinated site fallows, improving their RAS technology by bringing it in-house through purchase of a key supplier, focusing on wrasse culture, and by closing all waiting cages and open well boat transport. Further, I suggest forming joint ventures with smaller farmers to coordinate operations, developing the use of in-sea post smolt systems by bringing it in-house through purchase of a key supplier, and using more robust nets and sterile salmon. Next, I analyse the opportunity to expand to Newfoundland (NL), and determine that NL has the desired institutional surroundings for expansion, and that MHG has the correct resources and management to conduct the expansion, but that it is not without risks. Lastly, I conduct an innovation creation exercise, using a global cross-functional design team from MHG, to develop a hard to copy and profitable innovation for the new business unit, Marine Harvest Atlantic Canada. The product is made from pre-rigor fillet, is antibiotic free, is ASC certified, and from salmon operations monitored by the RSPCA for fish welfare. Finally, I determine that given MHG’s resources and global knowledge, they do not have to choose between growth in Norway or abroad but can do both. In Norway they can take specific actions to separate their fish in space and time from other farms, to avoid spatial conflict and therefore facilitate growth. In addition, MHG can employ cross-functional design teams in all BU’s to create buy-in across functional areas, to help develop successful hard-to-copy innovations and brands, thereby potentially moving the company from a good salmon farming company to a great seafood company, and thus building both customer and shareholder value.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».