Are your back office systems talking to your front office systems? Back office systems manage manufacturing functions as well as administrative functions such as quality management, accounts payable, and contract management. Examples of front office systems include customer relationship management (CRM) and help desk systems.
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Unifying these two, often opposing, sides of the organization may seem simple enough. But the reality is it doesn’t happen often enough, and the result is a negative impact on productivity, profitability, and organizational advancement—not to mention quality. Lack of integration also creates information silos, which prevents manufacturers from gaining a clear, 360° view into all of their data.
Proliferating information silos also proliferates risk
How can harmony between front office and back office systems eliminate information silos and improve quality management? Consider the case of a manufacturer dealing with global subcontractors. In the front office, the challenge exists to ensure that procurement orders leave the subcontractors with the latest versions of the specifications and schematics. The implications when parts are incompatible because they were produced based on outdated designs are enormous—ranging from product shipment delays and angry customers, to security incidents, product quality issues, profit loss, and a damaged reputation.
In the back office, product quality begins to erode quickly if manufacturing is not able to capture and process feedback from customers and sales. Likewise, if too many products are damaged during delivery because of insufficient packaging, there needs to be an easy way to report the issues so that the quality team can update and retrain employees in charge of that process in the back office.
To rectify these issues, an important first step is to understand the content and structure of the data repositories within your company. Providing easy access to this data for authorized employees can prove invaluable for minimizing risk and quality issues. And when visibility into the data is achieved, it is possible to immediately detect a disconnect between, for example, product data and customer data.
Solving the data-unification challenge
A study released by global marketing firm IDC estimates that time wasted searching for corporate information costs an organization more than $19,000 per information worker per year. That’s reason enough to convince manufacturers to be more proactive in bridging the gap between front and back office systems and processes.
Enterprise information management (EIM) systems have emerged as an effective solution for unifying front and back office systems. EIM solutions can be effective in bridging the gap between structured data systems, such as enterprise resource planning (ERP), CRM, and other database-driven systems, with other systems that store and manage unstructured content (e.g., documents, images, videos, emails) in order to deliver a level of contextual relevance that does not exist when key front office and back office systems are not connected.
For example, let’s consider one common structured data application: an ERP system. An ERP solution is involved in a variety of key business processes, such as accounting and invoicing, purchase order and work order processing, vendor management, and inventory. Hence, the ERP system provides vital information to establish relevance for an agreement that is managed in the EIM system. The agreement determines what product or service needs to be delivered, as well as when the invoice can be delivered. Enabling a seamless link from a customer in the CRM system, to an agreement in the EIM solution, to an invoice in the ERP system helps to streamline these commonly reoccurring transactional business processes. In addition, it also gives better insight to the sales team because it now has improved visibility to, for example, expiring contracts and can therefore plan its pipeline much more efficiently.
The foundation for the ability to link these systems is metadata. Metadata are the attributes, properties, and tags that describe and classify information contained in an organization’s data. They not only connect structured data with unstructured content, but they also provide insight into everything from the type of information asset, to the author, date created, subcontractor, and workflow state. Once defined, metadata help expose the value and purpose of the content, and become an effective tool for organizing, searching, and quickly locating information that can affect quality management.
By leveraging best-of-breed EIM platforms, organizations can intuitively link process structures to associated documents and objects, which equips them with the ability to proactively preempt quality issues before they strike—or at the very least, identify them early before they snowball. As a result, companies can better track enterprise risks and objectives, better manage subcontractor and other supply chain relationships, and achieve better business outcomes overall.
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