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
- •Document Processing: Purchase orders, invoices, contracts
- •Product Classification: Categorize items from descriptions
- •Supplier Risk Analysis: Analyze news, reports, sentiment
- •Demand Sensing: Social media, reviews, trends
- •Chatbots: Customer service, internal queries
Product Classification with BERT
python
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
python
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
python
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
python
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