5 Reasons why you need to start using Machine Learning for Demand Forecasting
Our strength in artificial intelligence and focus on automating the entire predictive analytics pipeline is the foundation of our data science initiative, where we, at mltrons, provide easy data merging, essential visualizations and intuitive automated machine learning for you to step up your forecasting game.
Here are the five reasons why mltrons’ machine learning will dominate forecasting methods in supply chain management:
1. Highly Accurate Forecasts
Stakeholders who care about forecasting in demand planning value a higher forecasting accuracy as even a small decimal point difference can have a big impact on the profitability or success of a product. Forecasting accuracy is vital in the planning process as its an objective measure and demand planning executives are familiar with the economic impact of inaccuracy and guesswork.
Therefore, getting the forecasting accuracy right is of utmost importance. Machine Learning forecasting, through multiple iterations and case studies, has proven to be more accurate and far more efficient than the currently used methods. Mltrons’ automated machine learning is not a black box: the influence of model inputs can be visualized and understood. Mltrons also provides a blueprint of each and every step that the model takes so that the model results are thoroughly understood, even without any data science background.
2. Ability to analyze large amounts of data and integrate external data sources
The ability of machine learning to ingest large amounts of data from disparate data sources and leverage that information at a granular level to improve SKU level forecasting is one major contributor to high machine learning forecasting accuracy. Simply put, if we have the SKU level data from Point of Sale or Point of Distribution, the machine learning algorithms will leverage the information to produce highly accurate forecasts.
The mltrons dp2’s (dp-square) automated machine learning module allows users to import and join data from disparate sources (PoS, online sales data) and then find the most accurate machine learning model automatically with a few clicks. The algorithms leverage the information at a granular level to improve SKU level forecasting and help companies make better planning decisions. Mltrons’ dp2 also allows users to incorporate prices & discounts information, weather information, social media data to reduce bias and improve overall forecasting accuracy.
3. Ability to constantly & automatically improve models with time
Constant improvement makes machine learning very attractive for companies with rapidly changing business situations, for example, fast fashion retailers. In simple terms, it means updating the forecast based on aggregate data on a daily, weekly or monthly basis. Mltrons dp2 automatically retrains the model based on the most recent actuals and generates an updated and improved forecast: decision makers can compare the new forecasting accuracy and improve their production decisions accordingly.
Users can adjust demand planning by leveraging forecast accuracy trends. This ‘constantly improving’ forecast monitoring, combined with dynamic and customer-level pricing and promotions, can be tuned to identify price sensitivity among customer segments, and can also serve as a foundation for an online recommendation system. Once the daily forecast and customer transaction history are merged with a transactional website recommender system, users can unlock the value of the recommender system and drive incremental consumer purchases.
4. Higher processing speed: faster results!
An additional advantage of machine learning is data processing speed. mltrons dp2 is deployed on high-performance GPU’s powered by Amazon Web Services, squeezing in more calculations per second, making the best use of in-memory storage, and getting forecasts at the speed of light. With mltrons dedicated web services and enterprise solution, users can generate up to thousands of forecasts per hour (data extraction, machine learning modelling, scoring and deploying the models). This saves demand planner hours and effort currently put into building the right statistical model and manually adjusting the forecasts.
5. Positive impact on your business profitability
Overall, forecasting accuracy has an enormous impact on the profitability of a retailer so getting it right is of utmost importance. Getting the forecasts right increases the overall sales outlook of an entire product line, increases profitability, lowers down the excess inventory costs and, with real-time monitoring also reduces stockouts that can help retailers save money on lost sales. Moreover, mltrons dp2 (dp square)can also be used to understand customer buying patterns and predict customer churn. Overall, this technological upgrade will enable companies to become better decision makers, agile and profitable.
Do you want your enterprise to become better agile, profitable and better decision makers? Contact us now at firstname.lastname@example.org or visit our website at mltrons.com and sign up for our Beta Testing.
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