Demand Forecasting

Demand forecasting plays an active role in order to make planning correctly. Due to the increasing amount of data and diversity, classical methods are insufficient in these predictions and machine learning algorithms come into play.

In supply chain sector, product entries and exits to the factories are carried out very quickly .Demand forecasting is important to increase profitability and reduce costs by keeping up with this speed. Demand forecasting is also important for personnel, material, shift and logistics planning. In this study, it is aimed to make demand forecasting as close to reality as possible.

Due to the increasing amount and variety of data due to the age we live in, the classical methods used in predictions are replaced by new popular methods. Machine learning algorithm is one of them. Depending on the size of the data and more than one variable, machine learning algorithm after data preprocessing was used instead of classical methods. The factors affecting the demand were examined. The prediction was made both weekly and daily.

The problem of Inventory Demand Forecasting is extremely simple to understand, yet challenging to solve optimize. The classic example is a grocery store that needs to forecast demand for perishable items. Purchase too many and you’ll end up discarding valuable product. Purchase too few and you’ll run out of stock. Numerous businesses face different flavors of the same basic problem, yet many of them use outdated or downright naive methods to tackle it (like spreadsheet guided, stock-boy adjusted guessing). In this article, I’ll outline a scientific approach for inventory demand forecasting using Machine Learning.

The Forecasting service at anaplatform is tasked with providing demand forecasts for your store-item combinations every week! For example, just how much of every type of ginger needs to go to every Walmart store in the U.S., every week for the next 52 weeks, with the goal of improving in stocks and reducing food waste.

As with any data analyticsservice, our first step is to collect, interpret, and analyze your data. To implement a forecasting model, you should ideally have historic data regarding

• what products were in stock each day/week
• how many of each product were sold per day/week
• when was each product being promoted (e.g. when were products on sale)
• what days/hours was your business open

What We Offer for Demand Forecasting?

With the increase of rapidly growing data, the problem of how to combine this data also arises. On the Supply Chain, even if businesses provide visibility into the data, this visibility, action or agility. may not always be sufficient. In order to achieve agility, the data shared throughout the chain must be able to be processed/analyzed in a way that enables action and decision making. For this reason, we will be offering big data analytics (BVA) applications to businesses to manage their Supply Chain.

Web as Data Source

The web is a big data source that adds richness to demand forecasting data. More and more people use the Internet to find the information they need, and also publish some content on the Internet. These information and content reflect people's living conditions and needs. By analyzing demand-related content on the Internet, we can obtain people's demand for different products and use these data to improve the accuracy of demand forecasting. Among these Internet data sources, search engine data is the easiest to obtain and the most representative. Therefore, in the field of demand forecasting, some people have used search engine data to improve the accuracy of demand forecasting.

Important Factors in Supply Chain Analytics

The supply chain processes that companies have to manage have five critical dimensions consisting of procurement, stock, production, inventory and warehousing, and the so-called "optimal" decisions taken independently of each of them often contain elements that negatively affect the overall performance of the system. In addition, structures designed in the past, changing business conditions and competitive elements is found

• Processes
• Stock,
• Production,
• Inventory,
• Storage.
• Analyzing customer satisfaction,
• Controlling declining goods and warehouse inventory,
• Monitoring the income and expense balance between branches.

What We Offer for Demand Forecasting?

Thanks to our extensive modeling, all of the following supply plans can be determined in the same optimization run.

• Distribution network designs,
• Demand forecasting plans,
• Material procurement plans,
• Short and medium term production plans,
• Deployment plans.

The purpose of our demo is to examine the moderator effect of big data analytics in supply chain relations with a real world example.