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Improving Inventory Demand Forecasting by Using the Sales Pipeline: A Case Study

2014· article· en· W773509337 sur OpenAlexaboutno aff
Shankha Bhattacharyya

Notice bibliographique

Revue˜The œjournal of business forecasting · 2014
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueOperations Management Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSales and operations planningSupply chainFinished goodDemand forecastingBusinessValuation (finance)Value propositionSales managementSupply chain managementMarketingProduct (mathematics)Operations managementIndustrial organizationFinanceEconomicsProduction (economics)Computer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

EXECUTIVE SUMMARY | The inventory demand forecasting is a critical function in a manufacturing organization as it impacts the major financial metrics like inventory valuation, gross margin and net margin. The currently used demand planning processes often fail to incorporate input from the Sales pipeline. Failure to do so provides challenges of how to increase the lead times for the supply chain process. This can be accomplished by introducing input criteria from the Sales pipeline. This case study is an attempt to provide such mechanism.Advanced Manufacturing Inc. (AMI) is a midsized business located in Southern Ontario. It has around 35 employees and has been in business for nearly six years. Its products are engineering items that are sold to clients in Canada, the United States, and Europe. To increase the value proposition of its products, the company often acts as a reseller of third party products. It procures specialized equipment from external vendors, integrates them with AMI products, and offers the integrated unit as a final product. The organization is well structured with specific departments responsible for fulfilling their re-spective roles in the value chain. The Sales team is responsible for securing the business, and when a Purchase Order is received from the client, the sale is deemed final. After securing an order, a job is created for it. The job is passed on to the Project Management team, which then allocates resources and coordinates with the Supply Chain, Production, Engineering, Quality Assurance, Finance, and Shipping teams. Once this project management cycle is complete, the product is shipped to clients. Inquiries that arise from clients after receiving the product are handled by the Client Response team. The organization has established a reputation for high-level product quality. Most of the client inquiries are software related. Most of the jobs are defect free, and that is what has enhanced the client loyalty. This has been proven by clients who place repeat orders with AMI, and act as advocates for the organization in their business community. As a result of the successful workflow and the efficiency of the Sales department, the organization is doing well, and has future plans of expansion.AN EMERGING PROBLEMAs discussed earlier, AMI secures contracts and executes them within the time period specified in the Purchase Order. The time period required to execute the order is determined by the Applications Engineering team. It is indicated on the quote, which is a part of the response to the Request for Proposal (RFP)/ Request for Quotation (RFQ). Depending on the internal resources available for the execution of the job, the time required for a job to be executed is quoted in the form, and that ranges from 6 to 8 weeks from the receipt of a Purchase Order.It is this project-execution time period that is creating challenges for the Supply Chain team. As discussed earlier, AMI acts as a reseller to increase the value proposition of its products. This means that it has to procure specific engineering items from external vendors. This requires negotiating prices, shipping to AMI at the correct phase of the project, and establishing terms of payment that are convenient to the company. In the case of AMI, most of the projects are unique. The Supply Chain team has to contact a larger number of external vendors and negotiate terms with them. Since they are ordering items on a projectby-project basis, they are unable to provide a guaranteed volume to the vendor and secure a price break. Also, the price of the same item can go up by as much as 10% to 15% between two successive orders that may be 60 days apart. The combined effect of price uncertainty and inability to secure a price break results in lower gross margins on certain projects. What is alarming is the fact that projects with higher revenue are showing a greater drop in gross margins due to the proportionately higher amount of resale items. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,061

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,000
Communication savante0,0020,002
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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.

Tête enseignante Opus0,141
Tête enseignante GPT0,345
Écart entre enseignants0,204 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2014
Routes d'admission1
Résumé présentoui

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