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

Supplier Risk Sentiment Analysis

from transformers import pipeline

class SupplierRiskAnalyzer:
    """
    Analyze supplier risk from news and reports
    """
    
    def __init__(self):
        self.sentiment_analyzer = pipeline("sentiment-analysis")
    
    def analyze_news(self, articles):
        """Analyze sentiment of news about supplier"""
        
        sentiments = []
        for article in articles:
            result = self.sentiment_analyzer(article['text'])[0]
            sentiments.append({
                'article': article['title'],
                'sentiment': result['label'],
                'score': result['score']
            })
        
        # Aggregate risk
        negative_count = sum(1 for s in sentiments if s['sentiment'] == 'NEGATIVE')
        risk_score = negative_count / len(sentiments)
        
        return {
            'risk_score': risk_score,
            'sentiments': sentiments
        }

Chatbot for Supply Chain Queries

from transformers import AutoModelForCausalLM, AutoTokenizer

class SupplyChainChatbot:
    """
    Chatbot for internal supply chain queries
    """
    
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
        self.model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
    
    def respond(self, user_input, chat_history):
        """Generate response to user query"""
        
        # Encode input
        new_input_ids = self.tokenizer.encode(
            user_input + self.tokenizer.eos_token,
            return_tensors='pt'
        )
        
        # Generate response
        chat_history_ids = torch.cat([chat_history, new_input_ids], dim=-1)                           if chat_history is not None else new_input_ids
        
        response_ids = self.model.generate(
            chat_history_ids,
            max_length=1000,
            pad_token_id=self.tokenizer.eos_token_id
        )
        
        response = self.tokenizer.decode(
            response_ids[:, chat_history_ids.shape[-1]:][0],
            skip_special_tokens=True
        )
        
        return response, response_ids

Tools & Libraries

• transformers (Hugging Face): BERT, GPT, T5
• spaCy: industrial NLP
• NLTK: text processing
• Gensim: topic modeling
• OpenAI API: GPT-4 integration

Related Skills

• ml-supply-chain: general ML
• **supplier-risk-management`: risk analysis
• demand-forecasting: demand sensing from text