Demand Forecasting in Dynamics 365 Supply Chain Management

supplychain

Businesses today are moving at a fast pace. However, as fast as it goes, there are also tools and technologies keeping companies on the top because of the forecasting. They predict their needs simply through historical analysis.  Microsoft Dynamics 365 also gives companies and industries a way to set their forecasting in the right direction through Demand Forecasting. 

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What is demand forecasting?

A happy customer means more business. The demand forecasting is exactly helping you make your customers happy. This process helps get the right estimations about your customers’ needs in the future.

In order for managers to make wise decisions regarding pricing, corporate growth plans, and market potential, effective demand forecasting provides firms with vital information about their potential in both their current market and other markets.

Benefits:

As the need increases for demand forecasting, more companies are inclined to get advanced demand forecasting software. Various benefits are offering you more room for customization. 

It gives better input on the inventory and other needs. Once you have accurate data, you can analyze your product needs and reduce the chance of understocking or overstocking.

Ultimately, it helps companies to balance their costs hence reducing maintenance costs.  

By calculating the accurate time of getting products from the supplier, you also decrease the lead time.

Analysis of the delays from the supplier can help you plan your products or stocks. Moreover, based on the sales analysis, you can provide your suppliers with the information on when to expect the reorders. 

Accurate projections can improve supply chain operations. Understocking would indicate that the business is not operating to its full capacity. On the other hand, overstocking results in higher costs for managing inventories.

When demand forecasting is used, point-of-sale systems’ instantaneous retrieval of recent and historical data can easily boost estimates’ accuracy. In fact, a 0.5 % to 3% boost in income can be attained by improving forecast accuracy.

Dynamics 365 Forecasting Architecture

To understand dynamics 365 supply chain management better, you must understand the forecasting architecture. 

Dynamics 365 forecasting architecture

The above picture clearly depicts how Dynamics 365 forecasting works in the back end. We have the following 9 steps, and each has its functionality. 

D365 generates and uploads historical demand and sends the message to the Azure data lake storage. It further runs the storage aggregated historical demand. 

Further, Dynamics 365 is responsible for triggering the ML experiment. Once the ML is triggered, it goes to the Azure Machine learning for further processing, generating the demand forecast and report. It then sends reports to the storage to store the demand forecasts. 

Back to the Dynamics 3565 Supply chain management, it runs further processes, including:

Key features

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Major Themes in Demand Forecasting

As you get to use demanding forecasting as a warehouse management module, you must know the 3 major themes of it as well. 

Microsoft 365 inventory management gets easier with this as demand Forecasting is modular. It means it is easy to configure. You have the authority to turn the functionality on and off simply from the Trade > Inventory forecast > Demand forecasting.

By utilizing algorithms written in the R or Python programming languages and a straightforward drag-and-drop interface, Machine Learning, a component of the Microsoft Cortana Analytics Suite, enables you to quickly and easily create predictive analysis experiments, such as demand estimation experiments.

This functionality lets you predict dependent and independent demand at any decoupling point. It is the foundation for demand forecasting.

Basic Flow in Demand Forecasting

Following is the flow of demand forecasting that you need to understand. Let us help you through it:

Basic flow in demand forecasting

Dynamics 365:

The process of creating demand forecasts begins with supply chain management. A staging table is supplied with historical transactional data from the Supply Chain Management transactional database.

Microsoft Machine Learning:

Later, a machine learning service receives data from this staging table. You can connect different data sources into the staging table with very little customization. 

The data sources can be Microsoft Excel files, CSV files, data from Microsoft Dynamics AX 2009 and Microsoft Dynamics AX 2012, and comma-separated value (CSV) files. As a result, you may create demand projections that consider previous data spread across many systems. The master data, such as item names and measurement units, should be consistent across all data sources.

In order to determine a baseline forecast, the Demand forecasting Machine Learning experiments compare the performance of five-time series forecasting techniques. In supply chain management, the parameters for various forecasting techniques are controlled.

Dynamics 365:

Supply Chain Management then has access to the projections, historical data, and any adjustments made to the demand forecasts in earlier iterations.

The baseline forecasts can be modified and visualized using supply chain management. 

Dynamics 365 for operations:

Before the projections may be utilized for planning, manual changes must be approved.

Now, if you are having trouble implementing Demand forecasting or need more consultation on dynamics 365 warehouse management and business processes, DFSM is here to assist you. Besides, we can help you customize your solutions according to the business need. Just get in touch now!

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