https://store-images.s-microsoft.com/image/apps.42492.f3080ecc-bf7b-4542-8530-7a5a816823ec.02d505b9-7f97-4af9-9b4b-22327b9be7bb.c697231c-e724-46d9-88c1-7bfa77191e8b
SmartRDM - Predictive Analytics for Manufacturing data
ConnectPoint Sp. z o.o.
SmartRDM - Predictive Analytics for Manufacturing data
ConnectPoint Sp. z o.o.
SmartRDM - Predictive Analytics for Manufacturing data
ConnectPoint Sp. z o.o.
Empower your manufacturing processes with data driven real-time predicitve analysics
Predictive analytics for manufacturing processes
Analyse time series data comming from production, combine with integrated systems (ie. ERP) and external data sources to execute predictions.
Provide users with prediction results as recommendations or provide strict user instructions.
Must have technology for
- predictive quality management
- predictive and preventive maintenance
- predictive energy efficency (dynamic tarrifs)
Preventive and predictive maintenance
- Minimize breakdowns and unnecessary maintenance costs in manufacturing planning
- Collect all history data that may have direct or indirect impact on past machine breakdowns or malfunctions and predict future machine malfunctions
- Provide maintenance teams real-time insights (including future preditctions with % of probabilities) comming from machines to take actions when needed
- Create actionable tasks for those responsible for maintenance to dispatch tasks and track statuses (including acceptance workflows where needed)
Example:
-> based on the history of executions and maintenance steps, is is known the specific machine malfunction is primarily caused by 3 separate factors (2 direct and 1 indirect)
-> all 3 factore are defined for edge/critical values that when achieved may cause machine malfunction
-> all 3 factors are being monitored in the productions process and likehood of malfunction in % (dedicated algorithm) is being calculated and reported
-> when malfunction probability exceed a given level, maintenance team is provided with maintenance recommendation with a list of recommended mainteannce steps
-> steps execution and status is being tracked
-> execution history data base is being updated to improve future analytics
Predictive energy efficiency
- tailor your production plans with dynamic tarrifs and changing media costs
- predict energy costs for energy market or own renewables and provide manufacturing plan recommendations to minimize energy costs per unit produced
- calculate energy consumption per unit produced and compare between production lines, shifts or factories to find out best practices
Example:
-> there are 3 types (A,B,C) of similar products manufactured in a given production site
-> product C (due to its excesive weight comparing to A and B) requires additional heating cycle (50% additional heating media costs per unit produced vs A and B)
-> based on the history of executions - unit costs of energy, per kilogram of product is being calculated
-> production is running 24/7 and it is possible to change production plans between products A,B,C within a day
-> based on weather forecasts, predicting energy costs (energy market or own renewable source) for next day production, production of Product C is setup for production to the lower predicted energy costs
Predict production quality based on history execution
- Minimize waste based on the history of executions
- Raw materials quality, production site temperature or humidity, best mix of ingredients and multiple other factor may impact expected waste
- Create production process "digital twin" to simulate multiple scenarios and "what if" analysis
Example:
-> new manufacturing order is comming that comprise of raw material mixtures, forming, cutting and drying processes
-> raw materials needed for the order are close to expiry date and raw material silo temperature is acceptable but abnormaly high (due to external conditions)
-> due to specific conditions, based on the history of executions, all similar executions from the pasts are analysed to identify these where wast level was the lowers
-> produciton operators are provided with produciton setup recommendations to minimize waste with a current conditions
-> execution data is being collected to improve further analytics
-> new manufacturing order is comming that comprise of raw material mixtures, forming, cutting and drying processes
-> raw materials needed for the order are close to expiry date and raw material silo temperature is acceptable but abnormaly high (due to external conditions)
-> due to specific conditions, based on the history of executions, all similar executions from the pasts are analysed to identify these where wast level was the lowers
-> produciton operators are provided with produciton setup recommendations to minimize waste with a current conditions
-> execution data is being collected to improve further analytics
About this Offer
This offer relates to a SmartRDM pilot implementation for a selected manufacturing process or subprocess (acquisition and management of up to 1.000 data points).
About Smart RDM product
- developed by ConnectPoint to shorten real-time production monitoring implementations
- deployable on cloud, private cloud or on-prem
- empowered with Azure analytics and hyper scalable technologies for multiparallel calculations
- trused by Danone, Twinings, Mondi, Veolia and many more
자세한 정보
Product Websitehttps://store-images.s-microsoft.com/image/apps.2357.f3080ecc-bf7b-4542-8530-7a5a816823ec.02d505b9-7f97-4af9-9b4b-22327b9be7bb.ab4b3812-4920-4e7c-a463-8b64ceb03d25
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