Guide to business intelligence for manufacturing

Business intelligence (BI) software is being adopted by a growing number of manufacturers. In the first installment of this BI guide, find an introduction to BI software for manufacturing. Read how manufacturers are using BI and find software selection tips. Learn the benefits of BI for manufacturing and discover a glossary of manufacturing BI terms .

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Business intelligence, or BI, is a valuable tool for gathering, organizing and analyzing information from all levels of an organization. For manufacturers, BI software can sift through data from across the supply chain – from orders and materials to production and shipping – and make sense of it all.

This guide contains the answers to your BI questions related to manufacturing. Section 1 offers tips for BI software selection and a glossary of frequently used BI terms. In section 2, discover how to make a business case for manufacturing BI and learn how to calculate the ROI of a BI implementation. Section 3 offers strategies for evaluating BI tools and rating vendors during the manufacturing BI software selection process. In section...

4, learn how to plan for a manufacturing BI implementation project, build a BI implementation strategy and find the right people for the implementation team. Section 5 contains manufacturing BI case studies that will help you discover how BI tools have improved data management for other manufacturers.

Introduction to business intelligence for manufacturing

Business intelligence (BI) has become the focus of more and more manufacturing software implementations. Most manufacturing managers can define BI -- a category of strategies and applications for gathering and analyzing data -- but BI software selection and implementation pose greater challenges for manufacturers.

The true value of BI software goes beyond simply gathering and storing company data. BI technology allows manufacturing executives to make informed business decisions about ever-changing market demands, sales strategy development, earnings and forecasting, materials management and more.

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For many manufacturers, getting started with BI software systems can be an overwhelming task. The first challenge is data preparation across the organization. This includes defining the data, determining how to measure it and assessing its value to the business.

The next step is to determine which data needs to be aggregated. Many traditional BI software systems start with a dedicated data warehouse. But as BI tools get better at using data from various source systems, some companies are choosing not to deploy a data warehouse. Either way, a business must have one logical place where all pieces of data can be stored and related to one another.

Finally, business data must be clean. Manufacturing companies are increasingly focused on data quality processes and technologies to ensure that BI systems display accurate data. Some are turning to master data management (MDM) or product information management (PIM) to ensure consistent data across applications and departments.

Once data preparation is complete, the BI implementation process can begin. At this point, many companies call in consultants to share BI implementation best practices, decide which metrics to measure and which data should be shown in dashboards, and identify potential pitfalls. A functional BI system must be able to adjust to new business requirements and changing data metrics.

Benefits of business intelligence (BI) for manufacturers

When BI software is used correctly, it can increase the efficiency of all parts of the manufacturing process. Many manufacturers are discovering the benefits of BI software, seeing process improvements on the plant floor, throughout the supply chain and beyond.

Manufacturers can gain much from transforming plant data into business intelligence. An emerging category of software, known as manufacturing intelligence (MI) or operational performance management (OPM) is helping manufacturers apply actionable metrics to production processes and plant-floor operations.

MI and OPM software transforms raw plant data from a variety of sources into manageable information. The data is packaged in the form of highly accessible exception reports, Web-based dashboards, scorecards or other delivery methods showing key performance indicators (KPIs) and other important metrics. Whether the BI user is a quality manager or plant floor executive, he needs to be able to view information in context, in order to make informed decisions quickly.

By leveraging the visibility of such an operations-oriented decision support system, manufacturers can establish links between operational KPIs and critical business metrics. As a result, they gain insight into everything from asset utilization to machine uptime and plant-floor productivity while also monitoring energy usage, uncovering the cause of quality problems, and ensuring consistent production across multiple lines.

BI can also help to improve efficiency at manufacturing organizations. Globalization and new competition, in addition to weak economies, are forcing manufacturers to run leaner and meaner. At the same time, they must produce a greater number of types of products, if not custom products, as well as maintain increasingly difficult standards.

The manufacturing sector's growing interest in BI has not gone unnoticed by ERP software vendors. In response, many vendors are adding manufacturing intelligence capabilities to their ERP suites, enabling manufacturers to swap out manual processes and rudimentary homegrown reporting solutions for new best-of-breed offerings and expanded functionality from their tried-and-true enterprise software providers.

Along with traditional ERP software providers such as SAP, automation giants like Rockwell Automation and Invensys Wonderware are building out their software suites to include manufacturing intelligence capabilities, while ERP software startups such as ActivPlant, Informance, iGear and myDials are attempting to carve out a niche in the market for MI.

BI technology is also starting to show up in manufacturing supply chains. However, BI software is having a hard time keeping up with the task of analyzing data from increasingly global supply chains, especially when supplier data is contained in several places, including the manufacturer's ERP software, multiple supply chain management (SCM) systems, and best-of-breed add-ons.

Glossary: Understanding the terminology of BI for manufacturing

Any manufacturing organization embarking on a business intelligence implementation has to understand key terms and definitions for manufacturing BI projects. Below is a short glossary with definitions for commonly used manufacturing BI terms.

Business intelligence: Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.

Corporate performance management: Corporate performance management (CPM) is the area of BI involved with monitoring and managing an organization's performance, according to key performance indicators (KPIs) such as revenue, return on investment (ROI), overhead, and operational costs. For online businesses, CPM includes additional factors such as page views, server load, network traffic, and transactions per second.

Key performance indicator: A key performance indicator (KPI) is a business metric used to evaluate factors that are crucial to the success of an organization. KPIs differ by organization: Business KPIs may be net revenue or a customer loyalty metric, while government might consider unemployment rates. KPIs are applied in BI to gauge business trends and suggest tactical courses of action.

Data warehouse: A data warehouse is a central repository for all or significant parts of the data that an enterprise's various business systems collect. Typically, a data warehouse is housed on an enterprise mainframe server. Data from various online transaction processing (OLTP) applications and other sources is selectively extracted and organized on the data warehouse database for use by analytical applications and user queries. Data warehousing emphasizes the capture of data from diverse sources for useful analysis and access, but does not generally start from the point-of-view of the end user or knowledge worker who may need access to specialized, sometimes local databases. The latter idea is known as the data mart.

Data quality: The reliability and effectiveness of data, particularly in a data warehouse. Data quality assurance (DQA) is the process of verifying the reliability and effectiveness of data. Maintaining data quality requires going through the data periodically and scrubbing it. Typically this involves updating it, standardizing it, and de-duplicating records to create a single view of the data, even if it is stored in multiple disparate systems. There are many vendor applications on the market to make this job easier.

Master data management (MDM): A comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, which provides a common point of reference. When properly done, MDM streamlines data sharing among personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications.

Enterprise manufacturing intelligence (EMI): Software used to bring a corporation's manufacturing-related data together from many sources for the purposes of reporting, analysis, visual summaries, and passing data between enterprise-level and plant-floor systems. As data is combined from multiple sources, it can be given a new structure or context that will help users find what they need regardless of where it came from.

Operational performance management (OPM): A set of processes that help organizations optimize their business performance. It provides a framework for organizing, automating and analyzing business methodologies, metrics, processes and systems that drive business performance.

This was first published in August 2009

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