Analytics Enterprise Services

Improve Demand Forecasting with YASH

Publish Date: December 15, 2017

Over the years businesses have accelerated growth at an astounding rate Increasing size of business leads to greater complexity in supply chains and an efficient decision making process with respect to the future demand requirements is an important cog to ensure smooth flow of process from production to logistics.In such scenarios forecasting demand using the traditional methods are no longer an option due to several limitations like their inability to capture seasonality, trend and randomness in historical data which are integral in understanding the underlying pattern of the movement of data. In addition, several macroeconomic factors and latent factors aren’t even accounted for; leading to discrepancy. This is why enterprises worldwide are shifting towards more robust and reliable demand forecasting solutions.
Demand forecasting helps the enterprise to accurately and efficiently allocate resources for production to meet the future customer demand by integrating diverse data sources like IoT and social media. This prevents underproductions or overproduction; saving wastages and increasing the net income for the companies.
Demand Forecasting Solution By YASH
The Demand Forecasting Solution by YASH focuses on enabling an enterprise to accurately and efficiently allocate resources in such a way that anticipated demand is fulfilled through efficient production planning and inventory replenishment strategies. The focus is to minimize wastage and optimally utilize resources to maximize profit.
Approach And Methodology
YASH demand forecasting solution uses several time series, Machine Learning and Deep Learning methods to provide extremely high accuracy in the forecasts. Our solution performs casual analysis by incorporating various variables from a wide array of unstructured and structured data sources. With the help of effective exploratory data analysis, outliers are shortlisted and variable importance analysis is performed to identify the most significant variables that influence net sales patterns. Advanced ML methods including lasso, multivariate and ridge regression is used for forecasting demand for finished goods. Furthermore, sensitivity and scenario analysis is done to find out the effect of endogenous factors on sales and perform what-if analysis.
Benefits:

  • The task of managing demand is done in a structured manner to facilitate undisrupted supply throughout.
  • Integration of various data sources includes IoT sensors and social media account apart from other traditional sources is possible.
  • Maximization of profit is ensured with the identification of critical parameters and having better monitoring and control of the same.
  • Scenario analysis provides critical insight into endogenous factors impacting sales and profit.
  • Availability of strategic data empowers management to slice and dice capabilities easily for making adjustments required to fulfill urgent high orders.

For more inputs on demand forecasting

watch_video

Hemanth Kanakagiri- Solution Architect @YASH Technologies

Related Posts.

BI , Big Data , Business Intellegence , Data Analytics
Analytics , Fiori Applications In SAP S/4HANA , SAP , UX
Process Modelling , Project Management , SAP , SAP S/4 HANA Transition , SAP Solution Manager , SolMan7.2
Business Performance , Digital Disruption , Glocal , IT Applications And Infrastructure , IT Outsourcing Partner , Re-imagine The New Normal
Analytics , Cloud , Serverless Data Lake
AMS , Customer Engagement , Engagement Models , Managed Servies
Analytics , Big Data , Digital Transformation , Enterprise Data Management
Analytics , Business Intelligence , Microsoft , PowerApps , Talent Management
Digital Strategies , IT Leadership , IT Support Tasks , Technology
Business Management , Co-creation , Customer Retention , Customer Success Management
Analytics , BI , Data Analytics , Decision Making , Six Thinking Hats
BMP , Business Functions , Business Growth , Business Process Management , Business Value Model

Add Comments