IBM Accelerator Catalog
IBM Accelerator Catalog

Manufacturing Analytics with Weather

Use The Weather Company data to help you understand how a manufacturer can quickly identify the reasons why there are high amounts of scrap rate to save money and deliver quality product. This demonstration shows how weather was the key driver leading to reducing scrap rate using statistical analysis and dashboards using Cloud Pak For Data (CP4D).

Slide Two

Key Challenges

What is an accelerator?
Industry Accelerators are a packaged set of technical assets used to help you tackle your next data science project by addressing your most pressing business challenges. With sample data, notebooks, scripts, a sample application and more, you can kickstart your own implementation and leverage the power of Cloud Pak for Data.

  • It is difficult to determine the reasons why large amounts of scrap rate exist
  • Factors such as weather data need to be considered during the statistical analysis

Expected Business Outcome

Icon1Icon2Icon3

Understanding the reason behind high scrap rate generation

Resource Optimization and Profit growth

Weather and its influence

Help organizations to understand the times of the year where higher scrap rate exists and what is the reason for it to be generated

Recommend a new schedule to build the products with more profit and reduced waste such that production period or resources could be re-allocated to get optimal results

Understand the role weather has to play in manufacturing industries

Key Features

Information architecture

End-to-end AI Ladder application

Modular framework

Icon4Icon5Icon6

An architecture enabling you to extract meaning from your data

From cataloging data through a glossary of terms to model development and deployment, simplify the lifecycle of your AI project

Composable and extensible pattern that can be applied to new data and industries

Get Started

Start experimenting today. Download and use this accelerator in your Cloud Pak for Data instance.

Get Accelerator Now

Highlighted Products for this Accelerator

IBM Cloud Pak for Data

Built on Red Hat OpenShift Container Platform, IBM Cloud Pak for Data accelerates your journey to AI to transform how your business operates with an open, extensible data and AI platform that runs on any cloud.

ViewLearn More

Watson Knowledge Catalog

Help your data users quickly find, curate, categorize and share data, analytical models and their relationships with other members of your organization.

ViewLearn More

Watson Studio

Empower your data science and AI teams to refine data and visually build and deploy models, using data on the desktop for anytime, anywhere access.

ViewLearn More

Watson Machine Learning

Deploy, monitor, and optimize models quickly, easily, and at scale.

ViewLearn More

IBM Knowledge Accelerators

IBM Knowledge Accelerators offer pre-created, extensive, curated glossaries to improve data classification, regulatory compliance, self-service analytics and other governance operations.

ViewLearn More

SPSS Modeler

You can use SPSS Modeler flows to build machine learning pipelines that you can use to iterate rapidly during the model building process.

ViewLearn More

Related Accelerators

Sales Prediction using The Weather Company Data

Use machine-learning models and The Weather Company data to help you predict how weather conditions impact business performance, for instance prospective sales.

ViewLearn More

Retail Analytics with Weather

Use The Weather Company data to help you understand how a retail inventory manager, marketer and retail sales planner can quickly determine the optimal combination of store, product and weather condition to maximize revenue uplift, know what to keep in inventory, where to send a marketing offer or provide a future financial outlook.

ViewLearn More

Connect With Us

Additional details

  • Accelerator typeCloud Pak for Data industry
  • IndustryRetail
  • Business functionAny
  • Product and version Cloud Pak for Data,Watson Knowledge Catalog,Cognos Analytics - Current
  • Author typeIBM
  • Company nameIBM
  • Author name IBM
  • Last modifiedSeptember 28th, 2020
  • Language English