fruiSCE® Demand Planner is an overall demand planning solution that gives the best-fit forecasting logic to the planners through robust integration of all kinds of data sources which include ERP and other supply chain solution suites. Supply chain leaders often perform forecasting based on increasing or decreasing trends, seasonality, and data set size. The components of time-series are as complex and sophisticated as the data itself. With increasing time, the data obtained increases and more data doesn't always mean more information, but a larger sample avoids the error that arises due to random sampling. The forecasting engine is powered by AI, ML, and Process Mining capabilities to do the planning as accurate as possible.
Reduce time to capture historical data and consolidation from multiple data sources
Facility to adjust and modify data flexible user interface as per the requirement
Reduce forecasting & demand planning cycle time by 100%
Supports best in practice forecasting techniques through a variety of choice
Data can be effortlessly traced and used for forecasting and demand planning
With the help of historic data, trends and patterns are recognized. Planning and forecasting parameters can be effortlessly determined
All the data at various significant data points is accurately recorded and it can be managed and used as per the requirement
The Demand Planner is embedded with a powerful forecasting engine which analyzes the data and provides with the predictions
Transforming data through aggregation and disaggregation allows to gather additional information for improved forecasts
New Product Introduction is effectively done through demand planning module by aligning with the demand behavior of similar products with seasonality predicted by AI/ML model and completely aligned with regular forecasting and demand planning process.
Once the planning and forecasting parameters are concluded, promotions can be planned accordingly
Based on the ABC analysis which is a inventory categorization technique, managing the materials becomes easier
Causal forecasting data can be used to determine the cause-effect relationship between multiple independent variables
By collecting the data from the internet through browsing, social platforms and other applications used by consumers, one can understand what they are really looking for
User security is a top priority and the data across various platforms, networks, and devices is provided with atmost security
What we can do for you
Consulting for introduction forecasting and demand planning processes to generate best fit forecast by considering best practice methods which include Autoregressive Integrated Moving Average, Exponential Smoothing, Holt Winter’s Addictive & Multiplicative Methods, Holt Linear Trend, Croston’s Method, etc.
Implement forecasting and demand planning process for collecting the data from various sources and get the system to generate the forecast, create demand plan based on inputs, aggregate and disaggregate, new product introductions, sensitivity analysis, and get the best fit forecast.
Integration with all complementing solutions and data sources which include ERP, Excel, LoB solutions, eCommerce portals, and others to have a seamless flow of data to make data available on time to enable better decision making.
software solution which is comprehensive, easy to implement, easy to maintain, built on the latest technology stack, empowered by AI/ML, process mining, mobility, API enabled, Cloud empowered, and with user friendly interface.
fruiSCE® Demand Planner is designed to cater services to various industries and the following are a few of them.
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ATTRIBUTES |
STANDARD |
BUSINESS |
PREMIUM |
|---|---|---|---|
| Planning & Forecasting Parameters | ✔ | ✔ | ✔ |
| Historical Data Management | ✔ | ✔ | ✔ |
| AI/ML based Forecasting Engine | ✔ | ✔ | ✔ |
| Autoregressive Integrated Moving Average | ✔ | ✔ | ✔ |
| Single Exponential Smoothing | ✔ | ✔ | ✔ |
| Double Exponential Smoothing | ✔ | ✔ | ✔ |
| Triple Exponential Smoothing | ✔ | ✔ | ✔ |
| Holt Winter’s Addictive & Multiplicative Methods | ✔ | ✔ | ✔ |
| Holt Linear Trend | ✔ | ✔ | ✔ |
| Croston’s Method | ✔ | ✔ | ✔ |
| Best Fit option | ✔ | ✔ | ✔ |
| Aggregation | ✔ | ✔ | ✔ |
| Disaggregation | ✔ | ✔ | ✔ |
| Sales Executive Manual Input | ✔ | ✔ | ✔ |
| New Product Introduction Work Bench | ✔ | ✔ | ✔ |
| Promotions Planning | ✔ | ✔ | ✔ |
| Demand Planning | ✔ | ✔ | ✔ |
| Version Control | ✔ | ✔ | ✔ |
| ABC Analysis | ✖ | ✔ | ✔ |
| Casual Data Analysis | ✖ | ✔ | ✔ |
| Sentiment Analysis | ✖ | ✔ | ✔ |
| User Security | ✖ | ✔ | ✔ |
| Master Data Management | ✖ | ✔ | ✔ | Process Mining | ✖ | ✖ | ✔ |
"Achieved robust transformation of supply chain planning and budgeting process through Enterprise Performance Management implementation".
Plot No. 1-11-301/3, 3rd Floor
Unnathasan Reddy Towers
Begumpet, Hyderabad – 500016
Telangana, India
