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Embedded Analytics

Embedded Analytics in Manufacturing: The Real Revolution [Guest Post]

By Evan Quinn | November 21, 2019
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This post was written by Evan Quinn, Principal Director of Product Marketing at QAD, Inc.

The analytics space has experienced several technology disruptions during the 2000s, some famous, some less so. For example, big data was a major disruption. It emerged as a trendy buzzword more than a decade ago. Invigorated by open source projects, a rush of start-ups, the rapidly decreasing cost of storage, and NoSQL data models, big data contributed to the rise of data science. Over the last few years, big data has cooled to some degree, with machine learning and AI picking up the hype slack.

Hiding behind all the hype, however, is a truly major revolution in decision making—embedded analytics. How has embedded analytics, albeit relatively quietly, forever changed the analytics landscape? It delivers sophisticated analytics to the fingertips and minds of everyday users.

The challenge with big data, machine learning, and AI is you need specialists to develop and model the analytics. The demand for data gurus and data scientists has certainly grown, but their work seldom helps everyday business users make decisions. This reflects the primary challenge of analytics since its inception: analytics lacked flexibility and usability for the everyday user. Embedded analytics has remedied that disconnect.

Back in 2014, my company QAD, a software company that offers ERP and supply chain software to global manufacturers, undertook a project to determine how best to help customers improve decision making. The answer was analytics from Logi that are now embedded in our flagship QAD Adaptive ERP solution. The result has been very well received, but the project took several twists and turns. What follows is a condensed version of the story.

>> Related: How to Package and Price Embedded Analytics <<

Analytics for Manufacturing ERP Users

When I started at QAD more than five years ago, our ERP solution included reporting, some built-in dashboards, and static analytics with simplistic graphical capabilities. For an extra price, manufacturers could acquire a business intelligence solution from us that included ETL, pre-developed analytics models, and a designer tool. The BI and reporting capabilities were certainly useful but could only be implemented by specialists and gave users little control.

Our customers, however, wanted more—or at least we thought they did. With all the big data hype out there, we thought our customers might be wondering why their ERP provider, QAD, wasn’t offering an arcane big data solution. Rather than guess at what they might want, fortunately, we asked first via a comprehensive survey.

We were surprised to discover that only a few customers (less than 10 percent) wanted big data. Their reaction toward big data was mainly, “don’t put the cart before the horse,” meaning most customers were looking to improve decision making through more accessible and business pertinent analytics. The allure of big data sounded too complex and too costly to them. They wanted analytics that would help operational users every day, and, most preferred not to send users to a specialty BI solution that required considerable training and forced the user to leave ERP’s context.

Why was there such a focus on helping out the daily, operational user with analytics? Global manufacturers require complex supply chains, have unpredictable customer demand scenarios, and deal with the vagaries of logistics. In short, exceptions are the rule. Not a business day goes by without a last-minute change to an order, a supply chain interruption, or a machine on the plant floor going down. Global manufacturing also involves a wide and shifting range of compliance requirements, which vary by manufacturing industry and country. In short, manufacturing is about managing change. Those that successfully respond to and embrace change succeed.

The people on the front line of manufacturing—the supply chain and procurement managers, those on the shop floor, those in the warehouse and on the dock, those overseeing transportation and logistics— all need analytics on as near-real time data as possible and need to be able to drill into data and generate modified models to deal with daily change. Try that with big data.

The QAD Answer: Logi Analytics

The first decision we had to make regarding adding user-friendly operational analytics to our ERP platform was build or buy. Should we reinvent the analytics wheel or partner with a leading analytics provider? This turned out to be an easy answer. QAD is a manufacturing ERP and supply chain software company, not an analytics specialist. Thus, we began searching for a partner that could help us provide what our customers were looking for.

As a starting point, we turned to Gartner research. We also performed competitive analysis to determine what analytics software our competitors were using. We also had some incumbent analytics partners and naturally included them in the mix. It quickly became clear that embedded analytics from Logi deserved to be on the short list; it could easily be surfaced in the context of ERP, it had the hooks to provide the data needed by our customers, and it had a flexible yet business user friendly front-end. After performing extensive proof of concept testing, Logi stood out.

Role-based Actions Centers and Analytics

The next step was to actually implement Logi Analytics in context of QAD Adaptive ERP. Along with the embedding of Logi into our ERP, however, we were in the midst of a user experience and platform transformation. Specifically, we were developing “role-based Action Centers” which are sophisticated action-oriented dashboards designed for the roles you would find typically at a manufacturing company. For example, we developed separate Action Centers for purchasing managers, supply chain planners, fixed asset managers, etc.

The Action Center enables the applicable user to spend most of the business day inside their designated Action Center cockpit. It provides notifications, collaboration/social feeds, activity feeds, shortcuts for taking further actions and relevant analytics. It includes typical graphical KPIs associated with the role, but also the ability to modify/add KPIs, to easily perform relevant queries with related drilldown and to modify the role itself. Naturally, given Logi Analytics’ flexibility, the user could personalize their KPIs, the related analytics and data discoveries and easily share them with others.

Nothing But Favorable Reactions

Of the many positive reactions that have come back from our customers about the Action Centers which includes the embedded analytics from Logi, ease of use has stood out. Specifically, the graphical front-end is quite easy for a user—not a developer or data scientist, but a user—to manipulate. During demos to customers or prospects, the positive reactions to Action Centers with the analytics is palpable. Our demos now intentionally include exception handling, so customers can see how they could deal with an anomaly, perform the relevant data research, make a smart decision and act on it.

For our customers, change is the only constant. As their businesses adapt to the highly dynamic manufacturing industry, as roles evolve and as exceptions occur, they now have comprehensive embedded analytics to help them effectively navigate what is required. Yes, the hype around big data, machine learning and AI continues at a high volume, and certainly QAD is investing in those technologies, but the quiet analytics revolution that our customers enjoy every business day is due to Logi Analytics.

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About the Author

Evan Quinn is the principal director of product marketing at QAD, Inc. He has spent his four-decade career as a software developer, in product management and product marketing, and as an industry analyst and analyst relations professional. Before QAD, he worked at several leading companies like Oracle, Symantec, Gartner, IDC, and Chase Bank. His analyst experience mainly focused on software development and application platforms and big data analytics.

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