MANUFACTURING

Predictive analytics plays an important part in manufacturing, planning, warranty analysis, supply chain intelligence, and so on. Customers are demanding quality products at lower prices with faster delivery. Hence it has become vital for manufacturers to deliver a differentiated experience throughout the entire customer lifecycle. Through the implementation of predictive analytics, manufacturers can predict the tendency of a prospect to purchase and the level to which a prospect would be accepting the given offers. Additionally, by analyzing group behavior manufacturers can plan logistics and operations that are based on specific demand for a particular type of product in the given region.

With the advent of technologies such as Industrial Internet of Things (IIoT), the predictive analytics is becoming more intellectual empowering the manufacturing industry. Along with the rising demand, the manufactures are also using the capabilities of predictive analytics to enhance their production abilities. Countries such as US and China have put the use of predictive analytics for manufacturing as to improve the manufacturing at global scale. There will be prominent use of predictive analytics solutions in manufacturing vertical for predictive maintenance, asset management, and remote monitoring applications.

COMPETITIVE LEADERSHIP MAPPING TERMINOLOGY

The vendors of predictive analytics software in manufacturing are placed into 4 categories based on their performance in each criterion: “visionary leaders,” “innovators,” “dynamic differentiators,” and “emerging companies.” The top 24 vendors evaluated in the data quality tools market include Agilone, Alteryx, Inc, Angoss Software Corporation, Dataiku, Domino Data Lab, Exago, Inc., Fair Isaac Corporation (Fico), Good Data, Greenwave Systems, Inc, IBM Corporation, Information Builders, Inc., Knime Ag, Kognitio, Microsoft Corporation, NTT Data Corporation, Oracle Corporation, Qliktech, Inc., Rapidminer, Inc, SAP SE, SAS Institute Inc, Sisense, Tableau Software Inc, Teradata Corporation and Tibco Software Inc

Use Cases of Predictive Analytics in Manufacturing:

  1. Predictive Maintenance: Method of predicting maintenance requirements in machines on a factory floor using “Predictive Analytics” is called as Predictive Maintenance. Analytics is used on machine operational data by analysing maintenance patterns allowing operators to predict when maintenance will be required on any given unit, hence maintenance can be proactively planned proved to be less costly.
  2. Demand Forecasting: Usage of advanced predictive analytics, such as machine learning and explainable AI, for more accurate and dynamic projections of demand, planning production capacity and supply flows accordingly.
  3. Production Capacity Planning and Management: Predictive analytics is used to optimize production scheduling. Data from number of orders, available raw materials, inventory levels, demand
  4. Inventory Optimization: Using predictive analytics for deriving accurate forecasts from production, suppliers and customers helps in optimizing manufacturing inventory levels.
  5. Workforce Scheduling: Predictive analytics removes much of the time-intensive labour through automation. Combining a precise demand forecast with the number of resources on hand helps a planner develop a more profitable, optimized schedule.
  6. Customer Satisfaction: Predictive analytics is being used by manufacturing to derive a 360-degree view of customers that encompass satisfaction index, customer lifetime value, churn prediction and propensity to buy.
  7. Predictive Quality: Root-cause analysis over past data and deep level of “Predictive Analytics” helps in identifying the reasons behind the substandard batches. Which in turns helps to make prior adjustments to save the under-production batch.

 

Case Studies of Predictive Analytics in Manufacturing:

IBM Corporation

Case Study: IBM’s predictive maintenance solution solved IEC’s reliability issues

IBM helped Israel Electric Corporation (IEC), the primary electricity provider in Israel by offering predictive analytics solutions to keep sustainable and reliable generation of electricity specially during peak demands. The solution used the patterns of circumstances surrounding past power outages, and helped predict and prevent future failures.

Business Outcome:

  • Reduce costs by up to 20% by avoiding the need to restart turbines after an outage.
  • Saved approximately $75,000 in fuel costs per turbine by identifying inefficient fuel usage.
  • Increased the efficiency of maintenance schedules, costs and resources, resulting in fewer outages and higher customer satisfaction.
  • Provides early warning of certain types of failure up to 30 hours before they occur, instead of 30 minutes.

Microsoft Corporation

Case Study: Microsoft’s predictive analytics solution minimized the cost and disruption of maintenance for Rolls-Royce

Microsoft through its Cortana Intelligence Suite for Predictive Analytics solved Rolls-Royce problem of operational anomalies of its 13,000 engines used in commercial aircraft worldwide. The solution used wider sets of operating data and using machine learning and analytics to spot subtle correlations, which optimized various maintenance models and provide insight that improved maintenance plan and help reduce disruption for their customers.

Business Outcome:

  • Provide the most accurate overview of the health of its aircraft engines.
  • Even a 1% saving on fuel costs can save an airline $250,000 per aircraft per year.

SAS

Case Study: SAS’s demand forecasting solution is making the right product available to Asian Paint’s customers

Asian Paint’s struggle to consistently and accurately plan sales demand was solved by SAS solution of “Predictive Analytics”. The demand forecasting solution factored in the deviations from business functions like production, inventory, supply, distribution planning and accurately forecasted future pricing, promotions, events and stock out data.

Business Outcome:

  • Improvement of forecast accuracy over the incumbent process.
  • Flexibility in leveraging impact of seasonality, pricing, promotion & other key events.

SAP

Case Study: SAP helped Daikin to improve customer satisfaction by predicting quality risks

Daikin’s motive to drive customer excellence through proactively detecting air-conditioning installation quality risks was achieved through SAP’s Predictive Analytics solution. The solution optimized and renovated quality assurance (QA) process for AC units using Machine Learning and image recognition.

Business Outcome:

  • Accurate prediction of potential installation issues with air-conditioning (AC) systems.
  • Prioritized installation by quality risk, enabling Daikin engineers to maximize QA efficiency with minimal effort and increase customer satisfaction

Mu-Sigma

Case Study: Mu-Sigma helped a leading consumer goods manufacturer in reducing raw material inventory levels

A manufacturing excellence group was aiming to reduce the overall inventory levels include raw material, packaging material and finished good inventories in distribution centers. Mu-Sigma developed predictive analytics capability within factories by creating a detailed continuous improvement framework for inventory reduction.

Business Outcome:

  • Enabled lower inventory at factory levels
  • Ability to scale the solution framework globally

QlikView

Case Study: QlikView offered “single source of truth” to drive strategic decision making

Ramkrishna Forgings a manufacturing company was facing challenges related to access of information through multiple sources and make intuitive decisions. QlikView integrated its analytics platform to provide “single source of truth” which helped in strategic decision making.

Business Outcome:

  • Improved data accuracy by up to 40%
  • Reduced IT support for business intelligence by up to 90%

HPE

Case Study: HPE solve HIROTEC problem of unplanned downtime

HIROTEC Group, one of the largest private automotive manufacturing company wanted to implement advanced technologies to tackle the unplanned downtime in its manufacturing facilities. Hewlett Packard Enterprise (HPE)’s predictive analytics solution captured data from eight CNC machines and performed real-time visualization of entire production facility.

Business Outcome:

  • Gained real-time visibility into its business operations
  • Accurately predict failures in critical systems like robotic arms

Tableau Software

Case Study: Tableau advanced analytics brought operational efficiencies for Tesla

Elon Musk’s goal to produce 1 million Tesla cars by 2020 failed to meet its projections more than 20 times in the past 5 years. As part of ongoing effort to tame chaos and improve manufacturing efficiency, Tesla turned to Tableau for advanced data analytics capabilities for root-cause investigation, quality defect tracking etc.

Business Outcome:

  • Tesla could easily trace back the production defects and rectify the same in no time
  • Improved operational efficiencies by predicting the production count and yield ratio

Tibco Software

Case Study: Brembo used TIBCO’s predictive analytics solution to build a Smart Factory

Brembo an Italian-based braking systems manufacturing company wanted to digitally transform and develop a smart factory. Tibco offered an analytical solution for manufacturing, process optimization control, purchasing, quality control, and R&D. The solution was used for cluster cooling curves, predictive maintenance, and noise analysis and testing.

Business Outcome:

  • Provided 360-degree view to organizational processes thus enabled better command and control
  • The advanced R&D department used the platform to develop KPIs for noise analysis and testing

RapidMiner

Case Study:  A leading silicon wafer manufacturer was under increasing pressure of quality requirements with skyrocketing demand of its wafer. One such critical quality process is wafer polishing. The more precisely the manufacturer can determine how much abrasion will be necessary to achieve the desired result, the less waste of silicon will occur (from overpolishing) and the less likelihood of a defect occurring (from underpolishing). The manufacturer is using RapidMiner to build machine learning models that predict exactly how much polishing will be needed for each wafer.

Business Outcome:

  • Improved product quality and product yield
  • Achieved better financial results

Predictive Analytics Software in Manufacturing

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Popular Comparisons
3.6 Online
The primary USP of IBM in Predictive Analytics Software is its ability to provide customers with advanced analytics capabilities and powerful insights which can help companies to make informed decisions and identify potential opportunities. IBM leverages its Machine Learning and Artificial Intelligence solutions to provide customers with an integrated analytics platform to identify trends, uncover patterns, and predict customer behavior in order to drive process automation, increase efficiency, and improve customer experience. IBMs predictive analytics software platform provides customers with the ability to generate actionable insights faster, uncover hidden relationships within their data, and make smarter decisions quickly.
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3.5
The USP of RapidMiner Inc in predictive analytics software lies in its ease of use. RapidMiner enables users to easily build predictive models with an intuitive drag-and-drop interface. It also provides powerful data preparation and visualization tools that empower users to quickly extract insights from existing datasets. Furthermore, RapidMiner also offers support for specialized use cases such as text mining and deep learning, making it an ideal choice for organizations seeking an all-in-one predictive analytics software.
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3.3
SAP Predictive Analytics software enables users to create, deploy and maintain various predictive models. These on-premise tools can help users anticipate future behavior and outcomes and better guide the decision-making ability to help grow the business. SAP Predictive Analytics Cloud works alongside the BI and planning tools to visualize, plan and predict context. The tool uses in-memory technology and machine learning to uncover relevant predictive insights in real-time.
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ORACLE Analytics Cloud’s Database Platform allows the use of seamless predictive analytics software within the platform, giving it an edge over other vendors. ORACLE Analytics Cloud helps mine various data types, eradicate movement of data, and deliver actionable insights. Application developers deploy this analytics model along with SQL and R functions. ORACLE Analytics Cloud helps predict the behavior of customers, the gap between the demand and supply, and make better marketing strategies.
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Angoss uses data and predictive modeling to present insights that help users make better decisions faster. It uses advanced statistical algorithms for the prediction of outcomes. These outcomes are generated across all stages of model cycles. It helps improving predictive analytics for organizations looking to monetize their data.
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SAS Advanced Analytics provides users with better response time and faster insights provided by its in-memory analytics. SAS Advanced Predictive Analytics software helps organize data in a structured manner, making it easy to understand and present. It enables the user to analyze past, present, and future models using quality-tested algorithms. Automation of large-scale forecasts is also possible without the need for high levels of technical knowledge.
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Use the full potential of information to unleash the capability of the human resources of an organization.
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Information Builders WebFocus RStat is a cost-effective, robust, intuitive, and accurate predictive analytics software. WebFocus can help organizations by extracting meaningful insights from data of any kind. It creates interactive dashboards to consolidate information which increases the chances of actionable insights to be used in the everyday conduct of data-driven businesses.
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FICO Decision Management Suite is an integrated environment for development that is compatible with web as well as mobile applications. It is a platform that handles real-time streaming of data including its visualization, indexing, search, and pre-processing, based on rules that are defined in advance. The company's USP is the ability to provide models based on precise customer requirement to reduce time and cost.
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Alteryx provides information science that can viably and productively tap into a code-free and code-accommodating easy-to-use application. The predictive analytics software requires no coding, however, it is coding friendly for those interested. It has a fantastic interface without code for both analytics modelling and advanced modelling with code. It enables easy deployment and management of analytic models, flexibility, agility, and high speed. It supports visualization tools and all data sources. Alteryx helps find, manage, and understand all sort of analytic information of an organization at a high speed, thereby making better decisions and increasing productivity.
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Rapid assembling of predictive data that changes crude information into a business affecting service. This product has advantages for all types of users: analytics leaders, data scientists, IT professionals, and business analysts. It helps analytics leaders in terms of managing productivity, collaboration, coordination, and measuring team growth. Data scientists benefit in terms of automation, modelling, flexibility, and reproducibility. IT professionals gain advantages pertaining to scalability, code & integration, operationalization, and data governance; while business analysts obtain data access, preparation, exploration, and automated ML benefits.
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GoodData is a cloud-based platform with high SLA availability and maintenance. It allows for easy incorporation of already existing data warehouses. It allows the platform to be integrated into web or mobile applications. It is one of the most dominant cloud data warehouse that meets most versatile analytics platform requirements.
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Extensive set of cloud services that enables associations to address business problems, construct, oversee, and convey applications on a massive, worldwide system utilizing various tools and frameworks. Microsoft Azure ML Studio can be used to prepare and manage the data they need for machine learning. It can improve productivity through its powerful capabilities that can integrate the current model cycle with that of the app lifecycle.
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Natural language search and AI-powered bits of knowledge discovery make creating bits of knowledge a characteristic, instinctive, and intuitive experience. Spotfire has strong built-in predictive analytical methods that are smart, yet easy to use. Its intelligent data wrangling helps you clean and modify data, and auto-records it so you can edit it later as well. It is flexible and can scale secured documents as well.
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NTT Data offers effective solutions that supplement the decision-making process in an organization. This is possible across multiple business platforms and across different development and deployment capabilities. With the help of a comprehensive analytics and business insight methodology, NTT Analytics Solutions can change a client organization into an information-driven pioneer.
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Teradata predictive analytics software provides “Flip the Switch” analytics which allows on-the-fly switching of best campaign users from reverse modeling to forward prediction. Teradata Analytics for Enterprise Applications eradicates the complexity of enterprise application integration, delivers real-time access to integrated data from ERP and other enterprise applications, as well as provides transparency and visibility into business and customer insights.
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KNIME is an open source software and it helps create data science applications and services. Being open, intuitive, and able to integrate new developments, this platform makes reusable components accessible to all the users and helps understand data science workflows. The software provides actual data analysis as well as a number of processes and has productivity funtions to help operations.
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Sisense makes the analytics process easy for users right from the preparation of data to the creation of insights. Sisense is an intelligence software known for its agility and easy implementation. It can be used by varied companies. This platform offers a range of business analytics features. It is designed to make complex data preparation and visualizations simple to make better business decisions and intelligent strategies.
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Domino delivers predictive models using ML and AI techniques capturing all the dependencies of experiments. It is perfect for models across cloud databases as well as distributed systems. Powering model-driven organizations to rapidly create and convey models that drive business impact.
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Kognitio empowers data analysts and business clients to run a large number of complex questions simultaneously, providing a competitive edge to large-volume business data activities. Kognitio is an immensely flexible platform that has the strong feature of viewing images and executing complex manipulations on images using standard SQL queries. It can be deployed on both, an existing framework clusters or on a complete standalone one, on-premises or on cloud.
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