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Plant Operations

Plant Operations encompass a wide range of activities aimed at maintaining efficient production, ensuring quality and maximizing productivity. General Plant areas are Continuous Improvement, Data Monitoring and Analysis, Equipment and Machinery, Inventory Management, Maintenance and Repairs, Process Optimization, Quality Control, Resource Management, Safety and Environmental Compliance.

Alexicon provides consulting services for business and manufacturing applications and data. Typically in different data domains, business and technical data can be one Company process. An example is three applications with their own processes.

It is possible to see the product flow for the entire Company and subprocesses in each application (ERP, MES and WMS). CRM could also be part of the process flow with subprocesses for Campaigns, Opportunities and Leads.

Human-Machine Interface (HMI) screens are used in Industrial Manufacturing, Water and Wastewater Treatment, Power, Gas Extraction, Oil Refinery, Chemical, Coal-powered, Nuclear Power and other industries to control industrial processes. Analytics provide Plant visibility across the entire production process and subprocesses.

Supervisory Control and Data Acquisition (SCADA) is a type of control system used to monitor and control industrial processes and critical infrastructure. SCADA systems combine hardware and software to collect and analyze real-time data from sensors and devices, enabling operators to monitor and control industrial processes from a central location.


SCADA and HMI systems integrate Smart Factories. From review of status to control, SCADA/HMI applications are realtime. Below is an "Ignition" (Inductive Automation) HMI screen on a smartphone.

SCADA Levels - Wikipedia

We look across the enterprise business and manufacturing process considering upstream and downstream analytics and control to improve productivity, quality and lead times.

  Level 3 - Production Control

Below is an Ignition SCADA simple example for a Level 3 Production Control screen (easy-to-design), which does not directly control the process, but is concerned with monitoring production and targets. This level utilizes a computer management system known as MES or manufacturing execution system. MES monitors the entire manufacturing process in a plant or factory from the raw materials to the finished product.

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Software Application Users

ERP Production Order

On the business-side, factories work on Manufacturing Work Orders which come from ERP Sales Orders (Make to Order) or from a Production Planning module for non-order specific finished goods inventory. Understanding data in factories is key to progressing towards being a smart factory. Unlike the centralized ERP, factories have many local critical software applications and machines running. In addition to SCADA efficiency and effectiveness, Smart Factories depend on quick turnaround times for engineering and production or manufacturing changes (Δ) affecting Production Order schedules.

Manufacturing Execution Systems (MES) - OpenAI | ChatGPT

MES systems should collect machine data as it plays a crucial role in improving the efficiency and effectiveness of manufacturing processes. By collecting machine data, an MES system can provide real-time visibility into the performance of machines and identify any issues or deviations from the expected performance. This information can be used to optimize machine utilization, reduce downtime, improve quality, and increase overall productivity.

The machine data that an MES system collects can include information such as machine status, production rates, cycle times, downtime reasons, and quality metrics. This data can be collected automatically from machines through sensors, programmable logic controllers (PLCs), or other data collection devices. The MES system can then analyze this data to provide insights and actionable information to operators, supervisors, and managers.

Overall, collecting machine data is an essential function of an MES system, as it enables manufacturers to make data-driven decisions and continuously improve their manufacturing processes.

Smart Factory - Wikipedia

The networking of embedded production systems and dynamic business and engineering processes enables the profitable manufacture of products, even for individual customer requests, down to a batch size of 1. The technical basis is cyber-physical systems , which means that both physical production objects and their virtual image in a centralized system (see also digital twin). In the broader context, the Internet of Things is often mentioned. Part of this future scenario is still the communication between the product (e.g., workpiece) and the production plant. The product brings its own production information in machine-readable form, e.g., B. on a RFID chip. This data is used to control the product's path through the production facility and the individual production steps. Experiments are currently being carried out with other transmission technologies such as WLAN, Bluetooth, color coding or QR codes.

Smart Manufacturing Analytics - Wikipedia

Smart manufacturing utilizes big data analytics, to refine complicated processes and manage supply chains. Big data analytics refers to a method for gathering and understanding large data sets in terms of what are known as the three V's, velocity, variety and volume. Velocity informs the frequency of data acquisition, which can be concurrent with the application of previous data. Variety describes the different types of data that may be handled. Volume represents the amount of data. Big data analytics allows an enterprise to use smart manufacturing to predict demand and the need for design changes rather than reacting to orders placed. Some products have embedded sensors, which produce large amounts of data that can be used to understand consumer behavior and improve future versions of the product.

Machine and External Data

With Enterprise Data crossing many applications and databases, understanding current business data and machine data is key to bridge the business-side to varied formats of technical data. From simple text, time and numerics to digital data.

Alexicon provides consulting services for business and manufacturing applications and data. Typically is different data domains, business and technical data is one Company process. For example, the Sales Order is created in an ERP and sent to a MES later a WMS and. Each application has

Enterprise. Orchestrated.


Enterprise Business Processes and Technical Processes for Systems and Data.

Align key processes across the Enterprise to achieve optimal performance. Sync Enterprise Applications and Analytics.

Time intelligence speeds operations and provides clear communication with Associates, Customers, Partners and Suppliers.

Formal Lean Six Sigma or streamlined methods and techniques.

Define, Measure, Analyze, Improve and Control

Statistical Process Control (SPC): Run Charts, Control Charts and Design of Experiments

Gain the process advantage and include learning in the EDW and Data Lake.


Enterprise Visualizations, Dashboards and Reports.

Data Science

Leverage Desktop Excel, SQL, R, Python and Scala code. Hyperscale activities.

Integrate Data Science activities in the EDW for Enterprise-wide computations.

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Enterprise Applications, Other Data, EDW and Data Lake.

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