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Mohan Ponnudurai

Supply Chain

The Importance of Analytics in the Supply Chain

An embedded EQMS allows companies to be proactive with quality system data instead of reactive

Published: Friday, February 21, 2014 - 10:44

Analytics, business intelligence, and key performance indicators (KPIs) are common phrases we hear just about everywhere in the workplace. The top focus for chief information officers has been on business intelligence and analytics and will likely continue through 2017 according to Gartner Inc. Gartner also says the benefits of fact-based decision making are clear to business managers in a broad range of disciplines, including supply chain management, manufacturing, engineering, risk management, and finance.

In today’s ultra-competitive manufacturing environment, relying on traditional supply-chain execution systems and their singular or siloed repository is not enough to protect a company’s brand or to reduce risk and ensure product safety. Companies not only need to ensure product quality, but also have insight into quality processes across the supply chain.

The science of analysis

What are analytics, and why are they important?

Analytics—the science of analysis—examines raw data to help draw conclusions about information. Used in many industries, analytics allows companies and organizations to make better business decisions. Analytics is leveraged in the sciences to verify or disprove existing models and theories.

To gain greater control of quality operations, leading manufacturing companies are implementing enterprise quality management solutions (EQMS) to standardize and harmonize quality processes, systems, and data within a single integrated platform. An EQMS then centralizes and automates quality management processes, all while integrating with existing enterprise systems such as enterprise resource planning (ERP), customer relationship management (CRM), laboratory information management systems (LIMS), and manufacturing execution systems (MES). An EQMS leverages the “master data” in these systems, such as product or equipment records, and integrates this information into the quality workflow.

This in turn can generate a lot of quality data from various groups, cross-functional teams, and stakeholders. In order to make sense of these data and be proactive about quality, you need visibility into information so you can access hidden areas for competitive advantage. To ensure greater visibility and to build collaboration across the supply chain, companies leverage EQMS data to perform real-time analytics and provide ad hoc reporting, placing the power of this advanced reporting into the hands of quality-focused personnel.

For example, a manufacturer’s quality personnel can use ad hoc reporting to identify trends in nonconformance issues early on, minimizing the risk that a quality issue will extend far into the product development life cycle. When a corrective action and preventive action (CAPA) must be put into place, quality professionals can leverage the system to better manage the process and speed resolution.

Some companies in high-tech manufacturing and electronics sectors are advanced in using systems, software solutions, and enterprise applications, mainly because of where they are in the maturity curve. These companies are fast-paced with tighter product life cycles; output yield and quality are important for their survival, so business units use specific processes and manage those processes with metrics, rules, and near real-time visibility. Inaccurate data could potentially destroy their market position.

Typically problems with data integrity arise when there are multiple sources of data, and there is no single source of truth when it comes to master data. This is often the result of business growth, mergers and acquisitions, legacy systems, and fragmented upgrades to enterprise applications such as enterprise resource planning, product life-cycle management, and manufacturing execution systems. In addition, when tasked with reporting on quality data, most manufacturers rely on manual processes, such as exporting quality data into Excel spreadsheets, which requires time and increases the possibilities for inaccurate data. Typically IT is burdened with data synchronization and ensuring everyone internally (e.g., engineering, manufacturing, supply chain) is looking at the same data, a process that can impact productivity.

To overcome these complexities and inaccuracies with quality data, a manufacturer should leverage an EQMS with embedded analytics capabilities that support both business processes as well as in-depth analysis and reporting. By eliminating the need for data integration and migration for analytics, manufacturers can empower their quality people and cut costs, all while enhancing their quality management strategy.

Embedded analytics capabilities allow end-users to gain insight into every aspect of quality across the enterprise in a self-service, ad hoc reporting model that allows for greater data availability, all while reducing the dependence on IT. Ad hoc reporting allows end-users to easily build their own reports and modify existing ones with little to no training. Typically, it’s as straightforward as clicking and dragging appropriate fields into a dashboard, and selecting specific criteria via drop-down menus or checkboxes. This type of ease and flexibility frees up valuable IT resources that usually create standard reports, and gets information to end-users immediately. Ad hoc reporting also makes it possible for end-users to dynamically modify and drill into report data for additional insight and analysis. As a result, they can analyze what they want, when they want instead of relying on standard monthly or quarterly reports.

Embedded analytics allow manufacturing, supply chain, field service, and supplier partners access to actionable information as required, without having to request it from IT and wait for the outcome. Having immediate visibility can be critical when, for example, you are tracking material progression through manufacturing, managing supply demand, and forecasting trends. Quality issues can be identified, actionable tasks assigned, and inventory and manufacturing status quickly changed as required. This avoids rework and in most cases, scrap.

Ultimately, you want to address quality issues early in the product value chain, instead of finding them once the products are in the hands of the customer. An embedded quality analytics solution allows organizations to be proactive with quality system data instead of reactive, thus saving time, resources, and cost.

I will be presenting a webinar on this topic on Tuesday, February 25th at 2 p.m. Eastern time, 11 a.m. Pacific. Click here to register.

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About The Author

Mohan Ponnudurai’s picture

Mohan Ponnudurai

Mohan Ponnudurai, an industry solution director at Sparta Systems, helps to guide the company’s alignment with industry trends, needs, and emerging requirements. Additionally, he assists Sparta Systems and its clients in the electronics, high technology, and industrial manufacturing sectors to track and understand changes in the regulatory and business environments. He contributes regularly to sector blogs, industry seminars, and trade conferences. Ponnudurai has a bachelor’s degree in engineering from McGill University in Montreal, Canada, and an MBA from the University of Tampa in Florida.