Our powerful Business Intelligence dashboard of Sandman gives you an updated view of your Green Sand system status and presents analysis insights derived from available data in a way that’s easy to understand and enables better informed decision-making and data analysis. The dashboard is specially designed for foundry MIS needs.
SANDMAN enables comprehensive digitalization of a foundry’s green sand data. Foundrymen can effectively and efficiently record their sand data directly on the software or upload via excel sheets in standardised formats or even upload the data directly from the shop floor through Wi-Fi-enabled Tablets, which can be accessed anytime, from any wifi enabled computer using
As managers age, change and move on, their shopfloor experience is often moving out with them or is available in limited formats. To overcome this inevitability, a UNIQUE feature of ANNOTATIONS which enables record of shop floor, system-sand related experiences and events so as to assist in future situations for informed and legacy data based
The SPC tools will aid run-time statistical analysis of how sand parameters are performing against rejections by type, component or casting group over an unlimited time range. Early-warning alerts on SMS and emails, of emergent issues enable key personnel on the manufacturing line to take appropriate action to resolve the issue and prevent the nascent
SANDMAN has provisions which allows you to uniquely customise the software to suit your Foundry and preferences. A Foundry can add/remove users, configure your foundry line, sand categories and parameters as per your requirements.
HIP aka SANDMAN HIP analytics predicts the highest influencing sand properties co-related to rejections on any given day or over any date range. The model correlates casting rejection with sand parameters and predicts the most optimal sand parameter range in which to operate, which can result in consistently minimum optimal rejection levels It has the
Translates the optimal sand properties target derived for HIP algorithms by prescriptive analytics which prescribe near-precise quantity of additives to be mixed/manipulated to achieve consistent, sustainable and scalable optimum molding sand control . At its core, it leverages information of underlying green sand plant dynamics and historical additive consumption trends to arrive at the additive