According to Toby Brzoznowski, the Chief Strategy Officer at LLamasoft, a rough rule of thumb is that a 1% forcast improvement improvement leads to a 2.5% reduction in the amount of inventory that needs to be stored at any point in time. This is a big deal for any organization and of course, a sign of a good demand planning practice!
Organizations today are heavily dependent on the accuracy of demand forecasts and the agility of subsequent planning as market trends are changing at breakneck speed due to shorter lead times and evolving customer needs. There are a lot more demand forecasts that are being made rapidly these days than what used to happen even two years ago, thanks to dynamically shifting customer needs.
Traditionally, demand planning applications relied on order shipment or sales history alone to make demand forecasts. This is changing today, with an objective to improve forecast accuracy. In fact, more and more planners are turning to real-time data as a way to improve their forecasting accuracy.
However, and despite best efforts, most companies continue to generate demand forecasts with a relatively high margin of error ranging between 11 and 28 percent. This can have a ripple effect on both, the inventory turnover and revenue generation as inaccurate demand forecasts will cause the production facility to purchase either too much or too little material in anticipation of the forecasted demand.
Therefore, having the right demand planning solution can help supply chain planners to control the amount of material they require and manage inventory accurately to meet the incoming demand.
And if you’re on the quest to find a demand planning solution that best suits you, here are the top 5 features you need to look for:
Five Features to Look For in a Demand Planning Solution
- 1. Data Integration
Data integration is critical in a demand planning solution because it helps centralize and consolidate all the relevant data from various sources such as sales history, orders, shipments, inventory levels, and customer feedback. By bringing all this data into a single repository, you can access and analyze it more efficiently and accurately. This will enable you to identify patterns and trends that can help improve the accuracy of demand forecasts.
A demand planning solution with data integration capabilities can also help you identify data quality issues and resolve them proactively. It can help identify missing or incomplete data, incorrect data, or inconsistent data across various sources.
Furthermore, data integration in a demand planning solution enables collaboration among different departments and stakeholders within an organization. By providing access to accurate and up-to-date information, it can help align demand planning with other business functions such as sales, marketing, and production. This can help ensure that demand forecasts are based on a more comprehensive view of the market and customer needs, leading to better decision-making.
- 2. Efficient Supply Chain Optimization
With the help of a demand planning solution that supports efficient supply chain optimization, you can prioritize demand based on specific rules such as product, customer segment, region, brand, etc. This will enable supply you to make decisions that will help to optimize inventory levels, production schedules, and product distribution scenarios.
Moreover, an efficient supply chain optimization feature in a demand planning solution can provide you with real-time recommendations on optimal multi-location inventory levels, production volume, and schedules. It can also help supply chain planners to identify potential supply chain disruptions, such as delays in delivery or production, which could impact demand. By enabling supply chain planners to make informed decisions in real-time, an efficient supply chain optimization feature can help organizations to respond to changes in customer demand faster and with greater accuracy.
- 3. Accurate Demand Analysis
A good AI-based demand planning solution will provide you multi-dimensional sensitivity analysis to assess the exact impact of different variables including product, price, stock availability, seasonality, trend variations etc. It will also provide an analysis of demand changes and identify key internal and external demand drivers behind the change.
A superior solution will also be able to give you Machine Learning driven analysis of the impact of non-recurring historical events classified as historical demand data exceptions, which need not be considered when forecasting demand. For instance, you can easily calculate price elasticity of demand to measure the impact of price changes on demand across various product and customer segments.
- 4. Effective Replenishment Planning
The right demand planning solution ensures seamless replenishment planning which makes sure your inventory is in the right locations and quantities, to meet the demand. It provides the right roadmap towards achieving demand-driven inventory replenishment planning which enables organizational efficiencies. An AI-powered demand planning solution is a key requisite for holistic replenishment planning as it involves building an accurate forecast of future demand. This is based on supply drivers, to meet customer needs as precisely as possible with the right amount of inventory levels.
- 5. Collaborative Demand Monitoring
An efficient demand planning solution provides you with configurable drill-down dashboards for real-time demand monitoring based on products or product segment, the selling channel, area or region and the variety of promotional events. It helps ensure real-time tracking of actual vs. planned demand along with timely alerts to you on the demand exceeding or dropping below the pre-defined thresholds.
Connect the Missing Dots in Demand Forecasts with ThroughPut’s AI-Powered Demand Planning
The first step toward reaping the benefits of collaborative demand planning would be to start building an internal data ecosystem that facilitates accuracy and eliminates delays in business processes. Artificial Intelligence fuels accuracy, efficiency, and reliability across your supply chain.
AI-powered demand planning uses Machine Learning to take supply chain demand forecasting to the next level by using internal and external data sources such as demographics, weather, online reviews, and social media trends. Using external data and modern Machine Learning algorithms, organizations can outperform networks that are managed manually and adapt to external changes faster. This can create significant impact across the value chain.
To explore how AI-based demand planning can benefit your organization contact us today.