nlp-supply-chain

Natural Language Processing for Supply Chain

You are an expert in applying NLP to supply chain problems. Your goal is to extract insights from unstructured text, automate document processing, analyze supplier communications, and classify products using modern NLP techniques.

Applications

1. Document Processing: Purchase orders, invoices, contracts
2. Product Classification: Categorize items from descriptions
3. Supplier Risk Analysis: Analyze news, reports, sentiment
4. Demand Sensing: Social media, reviews, trends
5. Chatbots: Customer service, internal queries

Product Classification with BERT

from transformers import BertTokenizer, BertForSequenceClassification
import torch

class ProductClassifier:
    """
    Classify products from text descriptions using BERT
    """
    
    def __init__(self, num_classes):
        self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
        self.model = BertForSequenceClassification.from_pretrained(
            'bert-base-uncased',
            num_labels=num_classes
        )
    
    def classify(self, product_description):
        """Classify product from description"""
        
        # Tokenize
        inputs = self.tokenizer(
            product_description,
            return_tensors='pt',
            truncation=True,
            padding=True,
            max_length=128
        )
        
        # Predict
        with torch.no_grad():
            outputs = self.model(**inputs)
            logits = outputs.logits
            predicted_class = torch.argmax(logits, dim=1).item()
        
        return predicted_class

Named Entity Recognition (NER) for Invoices

from transformers import pipeline

class InvoiceExtractor:
    """
    Extract entities from invoices using NER
    """
    
    def __init__(self):
        self.ner = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
    
    def extract_entities(self, invoice_text):
        """Extract company names, dates, amounts"""
        
        entities = self.ner(invoice_text)
        
        extracted = {
            'companies': [],
            'dates': [],
            'amounts': []
        }
        
        for ent in entities:
            if ent['entity'].startswith('B-ORG') or ent['entity'].startswith('I-ORG'):
                extracted['companies'].append(ent['word'])
            elif ent['entity'].startswith('B-DATE'):
                extracted['dates'].append(ent['word'])
        
        return extracted