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Products
Manufacturing Execution System (MES)
Manufacturing Execution Suite (MES) is a comprehensive manufacturing software solution that provides real-time, paperless production management to drive enterprise-wide compliance, quality, and efficiency.
Quality Management System (QMS)
Quality Management System (QMS) is a cloud-based digital quality solution for manufacturers that standardizes and automates quality documentation, processes, and measurements.
Enterprise Resource Planning (ERP)
Enterprise Resource Planning (ERP) is a full-featured, scalable, cloudbased ERP that automates front- and back-office processes.
Supply Chain Planning (SCP)
Supply Chain Planning (SCP) combines data from your Plex ERP and multiple departments across your business to sync up demand and supply planning to improve inventory accuracy and production management.
Connected Worker
Connected Worker empowers frontline manufacturing teams with real-time digital tools to improve productivity, reduce errors, and enhance collaboration. It connects your people, to purpose, people, productivity, and processes to drive continuous improvement and workforce agility on the shop floor.
Production Monitoring
Production Monitoring provides seamless connectivity to machines on the plant floor, delivering transparent, real-time operational KPIs and dashboards to drive continuous improvements.
MES Automation & Orchestration
MES Automation & Orchestration connects your Plex MES to the plant edge to control information flow, processes, and workcenter setup adding efficiency, saving costs, and eliminating manual errors.
Asset Performance Management (APM)
Asset Performance Management (APM) combines process, operational, and machine-level data through highly visual dashboards to proactively monitor machine and plant health to ensure optimal uptime, throughput, and maintenance needs.
Finite Scheduler
Finite Scheduler is an advanced production scheduling tool that helps manufacturers optimize resources, reduce bottlenecks, and improve on-time delivery. It enables dynamic, constraint-based scheduling to align operations with real-world capacity and demand
Platform
Smart Manufacturing Platform Overview
Discover how our integrated platform connects, automates, tracks, and analyzes your operations.
Cloud Infrastructure & Security
Enterprise-grade cloud infrastructure with industry-leading security and compliance.
Mobile Application
Access critical manufacturing data and controls from anywhere with our mobile apps.
Availability & Performance
Industry-leading uptime and performance with 99.5% availability guarantee.
Manufacturing Automation
Seamless integration with Rockwell Automation and other manufacturing systems.
Industries
Aerospace Auto and Tire Food & Beverage Industrial Manufacturing Plastics & Rubber Precision Metalforming
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Demand and supply planning was as much an art form as a science for many decades. The process was guided by human intuition, gut feeling, and experience and was aided by tools such as calculators and spreadsheets. These were all combined to produce forecasting that was an assumption at best and highly variable at worst. Software increased forecasting accuracy, reducing the height of the peaks and depths of the valleys in terms of variance. But despite the improvement, and aside from common issues like human error and missing data, demand and supply forecasting were still highly biased because they relied on human analysis of yielded insights and manual data entry. The rise of advanced software introduced tools to help eliminate remaining human inputs. Automated data, alongside systems more open to integration with other platforms, eliminated data silos. And now, platforms with advanced machine learning (ML) algorithms are addressing bias forecasting.

What is Machine Learning?

Machine learning uses trends and interprets incoming data to hone and improve computer predictions. Using complex algorithms, computers are "taught" to learn the value of incoming data inputs and return them to the user in a relevant way. The greater the volume flow on critical data points, the more accurate the machine becomes.

How Machine Learning is Linked to AI

Machine learning is often considered a subset of artificial intelligence but is capable of providing high value in advanced software platforms without AI. The most common link between ML and AI is the use of at or near real-time data to render actionable insights immediately. ML is always considered a type of AI, but not all AI applications are the result of machine learning, and ML systems can deliver high value as a standalone feature of the software.

Types of Forecasting Bias

Calculating forecast bias can reveal how accurate the forecast is to actual demand. Bias may be positive to forecasting demand, or it may be negative, resulting in under forecasting. In the end, bias will always impact the analysis of the data under review. There are many types of bias forecasting, including:

  1. Modeling Bias – Modeling bias occurs when a biased set of assumptions are used to build a model. One example would be a model that recreates the current state. Because the current state of demand has been steady or predictable, the model may have little room to account for the impact of disruption. Machine learning can identify and learn from trends to adjust the model.
  2. Complexity Bias – This bias assumes that if some inputs are good, more and more complex inputs are better. But not all incoming data is relevant to creating an accurate forecast. Machine learning can identify the most important data and filter out anything irrelevant.
  3. Confirmation Bias – When humans analyze complex data sets, they often zero in on what they’re more inclined to prefer as indicators. If people are accustomed to looking for certain variables, they may miss other signals in favor of their "favorites." Machine learning removes these predispositions.
  4. Innovation Bias – Companies continually develop new products to remain competitive, and each of these products has a unique demand curve. Human analysis risks the observer using assumptions that may result in biased demand variables. Machine learning handles each product's variables while managing complex combinations and overlaps for material requirements.
  5. Anchor Bias – Everyone has sat in meetings where managers firmly state a specific growth factor. The problem is that human planners may come away with bias that leads them to manage inputs to cluster at or near the number. Machine learning forecasting manages these inputs for relevance and shows actual growth or reduction.

Using Machine Learning for Accurate Forecasting

Plex DemandCaster has released a new machine learning enhancement to improve demand forecasting accuracy and help planners interpret data more efficiently. Plex DemandCaster’s machine learning capabilities can help you unlock more accurate data-driven forecasting insights, address disruption, and automate your demand and supply planning. Contact us to learn more about our machine learning capabilities and how they can help you.

Topics: Supply Chain Management Planning IIOT Industry 4.0

Plex Team
Plex Team
Plex, by Rockwell Automation, is the leader in cloud-delivered smart manufacturing solutions, empowering the world’s manufacturers to make awesome products. Our platform gives manufacturers the ability to connect, automate, track and analyze every aspect of their business to drive transformation. The Plex Smart Manufacturing Platform includes solutions for manufacturing execution (MES), ERP, quality, supply chain planning and management, Industrial IoT and analytics to connect people, systems, machines, and supply chains, enabling them to lead with precision, efficiency and agility.

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