Leveraging the Legacy--Infor Continues to Invest in the Latest Technologies While Capitalizing on Their Heritage
on Apr 19, 2018
At Infor's Innovation Summit for Analysts, we heard about their new data lake, investments in artificial intelligence, retail, manufacturing, and distribution. Here we cover some of the highlights.
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We in the analyst community love to focus on the ‘next big thing’ and the latest developments. However, the majority of enterprises are not trailblazers. They tend to stick to the axiom ‘If it ain’t broken, don’t fix it.’ As a result, there are a lot of old software systems still being used. We are now about 30 or 40 years into the growth of packaged on-premise enterprise software systems, including ERP systems which saw big uptake in the ‘90s and ‘00s. That leaves a lot of companies with decades old systems, even if they have upgraded to newer revisions here and there.
In the ‘00s, we saw the emergence of Software-as-a-Service offerings. These have many advantages over on-premise systems and though it took a decade or so for the majority of companies to become comfortable with this new approach, SaaS is becoming the dominant architecture. Very few startups opt to build on-premise systems anymore and we are seeing a big shift to the cloud by established players as well.
Figure 1 – Evolution of Enterprise System Architectures
That brings us to Infor, the third largest ERP vendor in the world. I recently attended Infor’s annual “Innovation Summit” for analysts. Infor, founded in 2002, grew largely by acquisition, buying numerous manufacturing- and distribution-focused ERP system vendors. Many of these venerable ERP systems were developed in the ‘70s and ‘80s1 and focused on specific verticals or micro-verticals. As a result, Infor has accumulated over 70,000 customers (that is the # of companies, not users). Infor has also made an enormous investment over the past decade to modernize their architecture and bring along these customers into the 21st century. That includes building true-SaaS applications across their portfolio (for more on this, see Infor Alchemizing Legacy Solutions).
If you look at the vast collection of systems Infor has acquired, it represents decades of lessons learned and accumulated industry-specific IP. They are harvesting all of that very specific knowledge and combining it with latest technology. They are also acquiring new technologies, especially in analytics, like Predictix and Birst. And investing in brand new development like their Infor Retail suite and Coleman, and other parts of Infor OS. Here we’ll cover a few of the highlights from the Summit.
Dynamic Science Labs and REVEAL
Infor has their Dynamic Science Labs, located near MIT, doing customer-driven projects on new technologies from proof-of-concept through prototyping. The lab is loaded with PhD scientists, engineers, and economists and focuses on big data analytics, machine learning, optimization, and other emerging technologies. We heard from Infor’s Chief Scientist and head of the Labs, Ziad Nejmeldeen. Ziad talked about REVEAL, which is not a product per se, but rather a collection of methods and projects, developed in collaboration between the Labs, Birst, and various other Infor groups. REVEAL aims to create cross-domain correlations and answer cross-domain questions. As an example, Infor posed a scenario of exploring a line of questioning: “Did sales in product X decline because of product quality issues or because of poor sales representative performance?”… “Did the quality decline because of the shift schedule or because of employee turnover?” … “Why did employee turnover increase?” … and so forth.
The vision is to create a sort of real-time advisor and alerting mechanism; one that uses data across silos to detect anomalies, create KPIs, uncover correlation and causation, and guide recommended actions. It will be integrated into Infor’s Coleman analytic applications. REVEAL uses different algorithms for different problems. For example, clustering will be very different than classification. Specific applications they are working on include pricing, patient demand forecasting in healthcare (predicting patient inflows to manage staff and supplies using data on admission, discharge, transfers, clinical quality, and county-level health data), and KPIs for quality of care. They have about 70 potential projects they are looking at.
Hook & Loop
Infor has gone all-in on design thinking with their Hook & Loop group, a sort of inhouse user experience R&D lab which is now building products in addition to designing interfaces. We have written about Hook & Loop before (see Hook & Loop: Transformative Consulting + Creative Agency Capabilities) because it is a differentiating capability in the enterprise software world. At the Summit we heard from Nunzio Esposito, VP and Head of Experience. Hook & Loop does both projects working with customers to feed GA product enhancements, as well as inhouse projects for the various software groups. Nunzio talked about their approach: first modernizing the UI and simplifying task workflows (such as reducing number of clicks, eliminating steps). Next is driving business productivity via end user empathy with design thinking. They are also doing data-driven design, where the designer leverages vast amounts of data sets available. Some of the things they are working on:
Mobility in Context—Mobile apps with limited or zero UI; looking at all the unique touchpoints throughout work tasks to make the apps precise, to the point, and delivered in context to the user’s device(s) of choice.
Predictive UX—Looking at the specific industry, user role, behavior, using machine learning to anticipate and deliver the right user experience.
North Star Interface: ProjectMAX—The Hook and Loop folks came up with a set of tenets to try to define the optimal experience: It must be ‘approachable,’ human-centered, focused, minimize complexity, eliminate distractions, relevant and important to the specific industry, and continually learning what the user means and wants.
XM Mobile—Configuring business rules to match specific needs. The example was using Infor Expense Management to create an expense report in minutes, taking a picture of the receipt, leveraging the device location and known itinerary, auto-correlating credit card expense and then automatically creating the expense report. This is a work in progress … right now it captures receipts which the user has to classify. This summer, they will start learning from the user’s classifications of expenses. Then they will add calendar integration, ride hailing service integration, and eventually deeper intelligence and automation.
Mobile HCM—Responsive UI for employees providing notifications and meaningful business interactions which can be configured by a company. The example they gave was some companies want to encourage work-life balance and would remind employees to take a day off. They plan to add an HR chat bot, one-tap benefits enrollment, GPS-based clock-in/clock-out, and more.
Infor Data Lake
The Infor OS is a collection of common capabilities shared across their various industry-specific and function-specific systems. This includes things like security and single-sign-on, analytics, social collaboration, event management, integration, and artificial intelligence. A key piece of this is the Infor Data Lake, which was designed with three key goals/attributes in mind:
Collect any data—Including structured ERP data, XML JSON or other semi-structured and unstructured data like server logs, sensor data, social network data, text, images, and other web data. Traditional data warehouses are not built with this flexibility.
Cost effective and scalable—Because of the extremely high volumes of data, it is critical to make the system cost effective or else storage costs can get out of hand.
Consumable—Data lakes store a huge variety of raw data, but for most users it only becomes useable when the right meta-data and UI is in place to access it. Many data lakes fail because the data is not cataloged or indexed in a way to make it easy to consume. Infor intends to provide a variety of interfaces and cataloged metadata. It is also critical to ensure the data is secure and compliant. Infor is implementing SSO (single-sign-on) and role-based security within the applications and the data lake.
Infor’s data lake can get data from any of the CloudSuite applications, from third party applications, public data, and streaming IoT data from devices and systems using Infor IoT. The data is stored on AWS S3 using Apache Parquet.2 Infor is making all of their applications event driven, which means all events can be published into the data lake. Infor’s customers decide which data to push to the data lake. Customers can turn various streams and sources on or off. For IoT data, Infor provides IoT-as-a-Service, taking in JSON data, and integrates Pi Historian for timeseries data. Their IoT portal is built on top of AWS IoT.
Once data is in the data lake, is can be consumed in a variety of ways, such as JDBC. Birst (the analytics system Infor acquired last year) can consume data from the data lake into its own data warehouse to do analytics. Infor also provides Elasticsearch APIs, as well as Infor Mongoose APIs to build any application using that data lake.
They are providing Infor Catalog as a standalone service of the data lake to keep track of the relationships between items in the data lake. For example, consider data about an employee from HCM, data about an order from ERP, data about an asset from EAM, and data about a device from IoT—You may want to know that the IoT data is for a specific asset, which is related to a specific work order, which is being worked on by a particular technician. Applications can record the relationships between various data. Users can also extend and create their own relationships as well. This metadata is critical for analytics, search, and AI.
Wholesale distribution is a bedrock industry (along with manufacturing) for Infor and represents a major part of their customer base. Infor said they are working on expanding distributed order management capabilities to set rules for automating fulfillment. They are exposing the DOM capabilities via API which will enable them to use Coleman AI on top of their DOM to make highly sophisticated automated fulfillment decisions.
Turtle and Hughes
We also heard from two of their customers. First was Ajay Kamble, CIO of Turtle and Hughes, an Electrical Distributor in Power Services. About four years ago, they started replacing their 25 year old legacy ERP system with Infor CloudSuite Distribution. They sell services and expertise, not just the items. They provide integrated supply chain services for J&J and GSK, and specialty services for PSEG and NYC Department of Environmental Protection. After implementing Infor Distribution, they added CRM, Warehouse, Supplier Exchange, and Storeroom for Infor Distribution SX.e. Their top three competitors are AmazonSupply, Fastenal, and Grainger.
They said this was not about ROI, but survival. Getting their ecommerce right, creating an ecosystem that was mobile first, including IoT devices for creating demand signals, human centered design, content and digital marketing, personalizing the UX, and supply chain transparency in terms of pricing and inventory availability.
For IoT they created a separate division, across a multitude of technologies, with expertise like when and how to add room sensors in electrical fittings, or in a warehouse what does IoT-enabled pick, pack, and ship look like. They are implementing new KPIs to measure the business.
We also heard from Frans Beerkens, Director CIO/CDO of Fetim-Group, a nearly 100 year old supplier to home DIY retailers, headquartered in Amsterdam. They supply construction materials, flooring, shelving, windows, and so forth. They create value-add product selections for the retailers. Fetim was a Lawson customer before Infor acquired it and they have since transitioned to M3. They wanted to move from Capex to Opex in their IT spend. Omnichannel was changing everything for them, requiring speed and digital integration. So they are creating a digital platform in the channel to automate, but in a human way. A lot of questions don’t need a person, like what time is my truck coming, or does this item come in blue, and they need to answer those questions faster, not put someone on hold. And it needs to be 24X7X365.
They selected Infor because of the shared vision. They didn’t just want to automate their existing processes, but looked for a partner who could challenge them in optimising their workflows, etc. They are using M3 SaaS ERP, EQ-CPQ (Enterprise Quoting-Configure, Price, Quote) which went live in their self-service ordering platform. This can be used to offer both standard sized product, such as flooring, plus provide all their products also in M2M (made-to-measure), which is high value add service for their retailers. Next, they will implement Birst as a basis for improved decision making and more accurate KPIs. They are doing automated invoice scanning. They are implementing GT Nexus to be able to improve the supply chain and build out ATP (available to promise), and getting the company a highly accurate ETA for inbound orders. They plan to implement Coleman for chat bots, digital assistants, and intelligent supply chains. Distribution is a highly competitive business and Infor is helping Fetim stay ahead of the curve.
Continued Investments to Leverage Legacy and New Capabilities
We heard many other impressive developments. Infor continues their heavy investment in building Retail solutions from the ground up. We heard an amazing customer story from NY MTA using EAM (Enterprise Asset Management). There was more on Coleman, manufacturing suite, healthcare suite, other topics. You could look at Infor and say they are held back by their collection of legacy system. But I think that is wrong—Infor is leveraging those legacy systems as assets, deep reservoirs of knowledge and experience, and investing heavily in the latest technologies to stay competitive, bringing their customers along. They want to make it easier for those customers to decide just maybe it’s time to move off of their old system and onto the latest and greatest.
1 Examples include NxTrend Technologies (founded 1979, wholesale distribution ERP), MAPICS (founded 1977, discrete manufacturing ERP), Lawson (founded 1975), Lilly Software (1980), GEAC (1971), SSA (1981), Future Three (1983), Daily.Commerce (1977), Intuita (1985), Datastream (1986), and Extensity (1970). -- Return to article text above 2AWS S3 is Amazon’s ‘Simple Storage Service,’ a popular web service providing highly scalable storage (storing trillions of objects). Parquet is a columnar data store used with Hadoop. -- Return to article text above
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