What is an "Industrial Plant Data Set"
The "Industrial Plant Dataset" is a complex amalgam of synchronous and asynchronous data types and data sources which must be collected, checked, structured and organized to service the business and operational scenarios of users, applications and business process.
- Operating Conditions: Flow, pressure, temperatures, levels which represent the state of materials and equipment at any given time or for a given time interval
- Product Quality Data: lab results typically coming from LIMS, out of order relative to the operating conditions often crossing across reconciliation periods. It includes also s analyzer results and any property sensor (e.g. viscosity, density, etc)
- Inventory Data: volume to mass conversation and compensation, strapping tables, etc.
- Material Transfers and movements: list of all material movements (receipts, shipments, internal transfers, feed-ins, rundowns) with their respective start time, end time, origin and destinations and material transferred.
- Utilities, Chemicals, Catalysts and Energy Consumptions: all ancillary measurements made on systems supporting mainstream operations. Similar in nature to all of the above but originating from different physical systems or networks.
- Planning & Scheduling Data:Â data about the expectations which can related to any of the above categories and can help assess the actual vs. planned results.
- Design data of equipment and systems: to understand the capability of the plant, create a reference for checks and balances and understand performance.
Assembling these disparate data types into a usable dataset requires skills, knowledge, and experience. This is the job description of Sigmafine and core compentency of Pimsoft.
A well conditionned Industrial Plant Dataset is the only way to enable users, applications and business processes to perform according to expectations and beyond.
"Only trustable data, available at the right time to the right people which can take decisions are really useful to increate the company's income"
Walter Mantelli, Technical Director, IPLOM S.p.A, Sigmafine Users Meeting - 2015
Trusting Data for Action
In process and manufacturing, information systems should be implemented with a system of checks and balances to maximize the quality of data and the economic potential of data. Creating an environment where data can be trusted is not an afterthought. Data quality, similar to product quality, needs to be built into our business and data management activities so that we can trust data for action at all times.
Transforming Data into Digital Assets
Being digital refers to data and information stored digitally. Being an asset refers to having control of the data and information to produce positive economic value. Being a digital asset consequently refers to data & information we own and control and from which we can generate sustainable and tangible benefits. This has been the job of Sigmafine and its users for almost three (3) decades now.