Supply Chain

Statistical Forecasting for Manufacturing

Solvanni Corporation
Statistical Forecasting for Manufacturing allows for choosing the right mix of historical data streams between Booking, Billing, and Backlog. The history can be further segmented based on the density of the data per time bucket. The app also features outlier detection, corrective suggestion and manual override functions to better influence the forecast. Multiple statistical models are used to generate the forecast at the product or the product family level.

Customers can choose between multiple error measures – MAPE, MAD, etc. to select a best fit model per product/family. Finally, the app allows for the analysis of the chosen models, accuracy and bias measures of the generated forecast and compare the forecasts to previously generate forecasts.

Information

App Last Updated
Size
658 MB
Language
English
Terms & Conditions

App stats

Modules
50
Roles
2
Formulas
1,029
Reports
10
Complexity
Intermediate

Features

Choice of Historical Data
  • Customers can choose to use either Billings or Bookings history with or without Backlog combined. Also, customers will be able to choose the amount of backlog to be considered part of the history for forecast.
Product Segmentation
  • The app is designed with pre-configured scenario for segmenting products based on density of historical data per time bucket (month). Customers can change the threshold to change this segmentation.
Outlier Detection, Suggestion and Correction
  • Outliers in historical data is detected based on percentile detection method. New values are suggested and allow for manual override. Customers can choose the suggested values or override with new values. Any statistical model can be run either with such corrected history or original uncorrected history.
Waterfall Analysis
  • Forecast can be compared with previous runs by using the roll function - the forecast is saved in a previous version. Upto 5 runs can be compared together.
Multilevel forecast run
  • 11 Forecast models (including Crostons) are run for the chosen history at 2 product hierarchy levels - the most granular product level and one level up - product familiy level. This allows for comparison and analysis of forecast at both levels and downstream consumption as needed.
Forecast Analyis
  • Preconfigured reports and dashboards allow users to slice & dice the generated forecast, best fit forecast, forecast accuracy and bias. across multiple product levels, multiple statistical models and multiple errors measured. A statistical model is fit to the product based on customer choice of error measure - MAPE, MAD, MSE or MPE

Solvanni Corporation

Solvanni is an Information and Technology Services company headquartered in Silicon Valley. Solvanni specializes in Future State next generation Material and Digital Supply Chain, Supply Chain Visibility, Big Data and Predictive Analytics connecting Planning, Execution and Decision Making.

Website
Address

Solvanni Corporation

2296 Gomes Road

Fremont, California

United States 94539

Ph: +1 408-250-6579

Solvanni Technologies India Private Limited

16/27, B2, II Floor,

V.P Colony, North Street,

Ayanavaram, Chennai, Tamil Nadu

India 600023

Ph: +91 97100 30735
Email
sales@solvanni.com;sales-us@solvanni.com;sales-apac@solvanni.com;sales-eu@solvanni.com

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Terms and conditions

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