BI differs from other common manufacturing software systems that offer analytical capabilities -- such as ERP, product lifecycle management, manufacturing execution systems and customer relationship management (CRM) -- in that it takes data from those systems, as well as external sources, if desired, and sends it to a data repository known as a data warehouse, where duplicate and inaccurate data is removed. Once the data warehouse, is populated, the BI analytics tool can be used to run queries -- such as, how many orders did not ship as expected this week? -- in order to gain actionable insights. With BI, the queries are run against data from all of the relevant systems, as opposed to just one system.
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For example, if a user runs an analytical function resident in the ERP system, it will only query that system, instead of presenting the bigger picture, according to William McKnight, president of McKnight Consulting Group in Plano, Texas.
“Your ERP might provide analytics, but only in that system,” McKnight said. “You have to bring that data together with data from other systems to create an overall picture.”
Let’s say a customer has asked for a new product. This request will naturally give rise to a number of questions, including: What components and materials will we need to make this? Do we have the right people to do the work? Where will we produce the new product? and Can we meet our customer’s demand in timely way?
“Those are the kinds of questions that make or break a manufacturer,” McKnight said. “You want to make the right decision based on data, not just gut feeling.”
To answer these questions requires accessing data from a number of sources inside the four walls of the organization, as well as supply chain partners and even, potentially, customers. If a manufacturer tried to rely on analytics within just one of the core systems, it would not be able to get the full picture. Plus, analytics capabilities embedded in enterprise applications are likely not as easy to use as a standalone BI analytics tool, which has been built for ease of use, according to McKnight.
Most enterprise applications, such as ERP and CRM, have been created to put data in, as opposed to getting it out, he said. In other words, the core transactional systems are more than adept at accepting data, such as new orders, customers and schedules for their resident databases.
However, these systems were not designed to provide an integrated data store upon which analytical queries can run. BI tools were crafted to get data out of critical systems and put it in one place for querying, allowing business users to get the data they need to make better decisions.
McKnight makes another point in favor of using a dedicated BI tool for analysis, as opposed to ERP or a similar system: If a user attempts to run an analytical workload on the production systems, the overall performance could suffer drastically.
“You will not be able to do a modern analytic workload on a system that runs your business; it’s just too much,” he said. And operational data is prone to be much “dirtier” -- that is, duplicative or inaccurate -- than data in a data warehouse, so decisions based on operational system data may not be as valid.
Jesper Vork of Spokane, Wash., a former BI consultant who now works for a BI vendor, has seen situations where thoughtful implementation of BI has helped the organization avoid a major ERP upgrade. “We did a BI project for a manufacturer that wanted to replace [a legacy ERP system] with [a modern system]. We helped them avoid that by getting the right data out of their old system,” Vork said. “This way, they can make better decisions without having to take the risk of migrating.”
BI software purchases can give business users the ability to make better decisions, leading to greater agility, which drives competitive advantage. “This is where business competition is today,” McKnight said. “Everything rides on your ability to use information to make better decisions, to drive the business and operate in a more resilient manner.”