Building a demand planning tool tailored to your company’s specific needs is possible with the right mix of cloud services, back-end and front-end development, data pipelines, and ERP integrations. Here’s a step-by-step guide to help you create a scalable, customizable solution.
Before diving into the technical details, start by clearly defining your business requirements:
Type of demand planning: Are you focusing on short-term or long-term forecasts?
Integration needs: Do you need to connect with ERP, CRM, or other systems?
User roles: Which teams will use the tool? (e.g., planners, sales, finance)
Leveraging cloud platforms like AWS, Google Cloud Platform (GCP), or Microsoft Azure offers scalability, security, and flexibility.
Compute resources: Use services like AWS EC2, Google Cloud Compute Engine, or Azure Virtual Machines to handle back-end computations.
Storage: Store large datasets (sales, forecasts, inventory) using AWS S3, Google Cloud Storage, or Azure Blob Storage.
Database: For transactional and time-series data, consider relational databases like AWS RDS, Google Cloud SQL, or Azure SQL Database. For unstructured data, look at NoSQL options like MongoDB.
The back-end is where all the heavy lifting happens. Here are the key components:
Forecasting models:
Time-series models: Use methods like ARIMA, Holt-Winters, or Exponential Smoothing for stable, time-based data.
Demand sensing models: Incorporate real-time data such as orders, promotions, and POS data to enhance short-term forecasts.
Server-side logic: Write back-end code using languages like Python (great for data science) or JavaScript (Node.js for scalability). Use frameworks like Flask or Django to build APIs that interact with the front-end.
Data pipelines: Use ETL (Extract, Transform, Load) pipelines to gather and clean data. Tools like Apache Airflow, AWS Glue, or Google Cloud Dataflow can automate these tasks.
Reliable data pipelines are essential for collecting and processing historical sales data, market trends, and external factors.
Source data: Pull data from systems like ERP, CRM, or marketing platforms using AWS Glue, Google Cloud Pub/Sub, or Azure Data Factory.
Streaming data: For real-time data such as sales orders or demand changes, use streaming tools like Apache Kafka or Google Pub/Sub.
Data preprocessing: Use Pandas (Python) for data cleaning, or cloud services like AWS Lambda or Azure Functions for on-the-fly processing.
The front-end is where your users will interact with the tool, so it should be intuitive and easy to navigate.
Web frameworks: Build a responsive interface with modern frameworks like React.js, Vue.js, or Angular.
Dashboards & visualization: Use visualization libraries like D3.js, Chart.js, or Plotly to create interactive dashboards that display forecast results and trends.
Authentication & authorization: Secure access using OAuth2.0 or JWT (JSON Web Token) authentication protocols. Store user credentials securely with AWS Cognito or Firebase Auth.
Seamless integration with ERP systems like SAP, Oracle, or Microsoft Dynamics is key to ensuring smooth data flow between departments.
APIs: Use RESTful or SOAP APIs provided by most ERP systems to pull in data like sales orders, inventory levels, and invoices.
Middleware: If needed, use middleware solutions like MuleSoft or Dell Boomi to manage data flow between the ERP and your demand planning tool.
Webhooks: Set up webhooks to trigger actions—like updating forecasts—whenever new orders are placed or stock levels change.
As your business grows, your demand planning tool should scale with it.
Containerization: Use Docker for containerization and orchestration platforms like Kubernetes for auto-scaling.
Automation: Automate processes like updating forecasts and running models using tools like AWS Lambda or Google Cloud Functions.
Once your tool is ready, deploy it using services like AWS Elastic Beanstalk, Google App Engine, or Azure App Service to ensure easy deployment and scaling. Monitoring and maintaining system health is critical, so set up monitoring tools like Prometheus or AWS CloudWatch to track performance and handle any issues as they arise.
An Introduction to Time Series Forecasting with Generative AI (interesting topic but haven’t seen any successful use case of this because of the specificity that is needed): https://www.databricks.com/blog/introduction-time-series-forecasting-generative-ai#timegpt Time series transponders like TimeGPT & Chronos
Time Series Made Easy in Python with Dart: https://unit8co.github.io/darts/ & with PyTorch: https://pytorch-forecasting.readthedocs.io/en/stable/
Advanced models & their implementations in MXNet & PyTorch - Gluon: https://ts.gluon.ai/stable/getting_started/models.html