Redefining productivity with agile ERP for modern enterprises

Profile picture for user Mayumi Hiramatsu By Mayumi Hiramatsu October 10, 2019
To live up to technology's promise for the modern enterprise, ERP software must become easier to deploy with faster time to value writes Infor's Mayumi Hiramatsu

Low angle view of female athlete jumping hurdle © sirtravelalot - shutterstock

Organizations have been striving to increase productivity since the days of the cotton gin, steam power and Model T assembly lines. Today, maximizing productivity is often associated with software technology, from virtual assistants to predictive science. But, as the pace of innovation has accelerated, the practical ability to implement and monetize the exciting new technologies hasn’t always kept up. It’s time for solution providers to step up their game and take a more active role in supporting software implementation efforts.

Today’s common tactics for deploying software are flawed. In the race to out-perform competitors, enterprises have hurried into projects without careful planning. The route to go-live status has become a steep, uphill climb, riddled with delays, overwhelming amounts of data, heavily modified legacy solutions, and a hodgepodge of point solutions that don’t speak to each other. The tempting razzle-dazzle of virtual-this and networked-that has often obscured the foundational questions we should be asking: Who will deploy? What’s the impact on productivity? Where’s the payback?

Complexity wins round one

It’s not that the technology doesn’t work. It does. Monumental leaps forward in innovation have yielded heavily-hyped solutions such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and virtual reality (VR). Although they have been around for decades, next generation ERP solutions have now moved to the cloud and incorporated AI and advanced analytics into core functionality. Enterprise solutions today are powerful business tools that bring insight, automation and operational best practices.

Whether it’s tracking shipments for a global supply chain, anticipating market trends in retail, or improving the patient experience in healthcare, modern software is transforming the way we do business. Automation is freeing workers from the tedious, repetitive tasks — or the dangerous ones – so they can focus on the uniquely human capabilities: creative problem solving, innovation and relationships with customers.

The potential is nearly limitless. But, so are the complications. McKinsey recently published Agile in enterprise resource planning: A myth no more in which the authors address the vital need for end-to-end solutions — despite the frequent disappointing results:

As fundamental as they are, three-fourths of ERP transformation projects fail to stay on schedule or within budget, and two-thirds have a negative return on investment.

The Blame Game

Cynics looking for a culprit to blame can point at the technology itself and the companies that develop the science and software. There’s a lot of us in the industry. We can all share the burden of this forehead-smacking realization: Sometimes, we let our customers down. Technology has not always lived up to the potential we envisioned. CIOs struggle to reduce modifications while keeping differentiating capabilities. CFOs still worry about the reliability of data — but now there are orders of magnitude and more data volumes to consume. In manufacturing, machines still break, and customer orders still ship late, despite endless reports explaining why. And, sometimes, in healthcare, no amount of data or efficiency improvements will change whether a patient pulls through or not. Software can’t fix everything.

We wish it could. Enthusiastic about breakthrough computing concepts and cutting-edge applications, enterprises want to enact plans that will help them out-maneuver aggressive competition, overcome paper-thin margins, and thrill highly demanding customers — overnight. And, of course, boosting profits would be a nice, too. So, assisted by packs of implementation consultants, change management experts, business analysts, data scientists, and workforce management teams, they set out to reinvent processes.

Then, something goes awry. Or, a series of small delays and unexpected challenges add up, combining to create a general sense of unease and worry. Projects can wear on, extending beyond the term of the original advocates and sponsors. Proof-of-concept projects for IoT applications can take years to achieve ROI. Migrating heavily modified on-premise solutions to multi-tenant cloud can take years of untangling the essential proprietary concepts from ones that can be replaced by standard functionality. These are tough decisions for some companies.

Five common challenges

The McKinsey article points to five common challenges that can send an ERP implementation off course:

  • Misaligned incentives. All of the parties may not share the same goals. Implementation consultants can benefit from complex projects that extend past the target date.
  • Poor project management. Most organizations lack experience in managing complex IT projects with multiple vendors. Many individuals are change-adverse and can (intentionally or not) impede progress.
  • Lack of business-IT integration. Multiple lines if business must buy into the project and agree on workflows, definitions, compliance, validation — and more.
  • Missing the focus on business value. Activities and tasks tend to drive transformations, rather than value and bottom-line impact. Simplifying difficult jobs doesn’t always yield financial value.
  • Waterfall methodology. Most projects use a linear, sequential approach to the project schedule, which stretch out the timeline.

These are just some of the issues that complicate the deployment process. It’s easy to get lost in a change-order jungle, continually chasing minute issues and losing sight of the main goal. We have all seen the headlines of failed projects and frustrated enterprises that turn to the court system for resolution.

Holistic, balanced view of precautions

Deloitte also addressed risks associated with digital transformation. Cyber security is one of the top issues, the author wrote, and it can be one of the top reasons enterprises hesitate about a move to the cloud. Although the top cloud providers are experts in security, back-ups and encryption methods, companies still worry about possible breaches:

An immediate step by organizations is to have robust measures around cybersecurity and the easiest approach is to perform typical information security and/or cyber security assessments of systems.

The authors go on to stress “there is always more” which can be done, but the organization must balance costs, practical applications, available technology, and potential impact. Then the question becomes “What is enough?”

Preventing a failed project, whether it is moving the ERP solution to the cloud or deploying a new IoT initiative, requires vigilant monitoring — not only of the security precautions, but also the data integrity, governance, access protocols, user-level workflows, and adherence to industry-specific best practices. Glitches and complications can pop up anywhere, causing distractions and delays. As the saying goes, a chain is only as strong as its weakest link. So, the entire project may be implemented smoothly, but if one component — like reporting — fails, the entire project can suffer. Deloitte concludes:

Just as thinking in silos is dangerous in digital transformation, so is managing risk in silos. Risk management isn’t a departmental or project-based job. It’s an all-time job, and it needs to be baked into every aspect of digital transformation for your company to experience success.

Leadership plays a role

Forbes contributor, Daniel Newman, recently wrote about the risk inherent in digital transformation. and the preponderance of negative outcomes. “What these headlines tell me isn’t that technology isn’t working or that digital transformation isn’t worth it. It’s that companies today are rushing headlong into digital transformation without a clear idea of where things could go off course — or how to get back on track. Digital transformation needs risk management for these very reasons,” he said.

Newman suggests that the enterprise’s leadership team should take ownership of this challenge and ensure the right teams, policies, and attitudes are in place. “While digital transformation can be a miracle worker, there is no technology that serves as a fail-safe in digital transformation. Just as with any type of digital transformation your company undertakes, you need strong leadership, executive support, tech-friendly culture, data-driven decision-making, and a silo-less enterprise for digital transformation to succeed at the highest level. There is no shortcut for this, and there is no “risk management” program that can do the work for you,” he wrote.

Newman summed up the issue well, offering sound advice:

Digital transformation needs risk management because risk management provides the structure we need to understand the points at which our digital transformation projects can go wrong. But risk management doesn’t make a project succeed. Only we — leaders in the movement with a commitment to creating tech-driven culture — can do that for ourselves.

Can technology help deploy technology?

Some software providers, such as Infor, are turning to technology to help improve the ease of use and speed to implement projects. Implementation accelerators, pre-populated templates, and simplified user interfaces have been tools offered to customers and channel partners for years. But, today, these types of tools are more important than ever, especially as enterprises strive to adopt advanced concepts such as AI and ML.

This was a topic that Infor CEO, Kevin Samuelson, broached recently at Inforum 2019, the company’s annual customer conference. He told a group of analysts and media:

The practical operationalized use of AI and machine learning in the enterprise remains low because most tools are deeply technical and developer-centric. Too many of these AI tools have been designed for experimental projects and are therefore difficult to scale and repeat.

In an attempt to resolve this situation, Infor’s Coleman AI platform provides industry-specific starter packs to accelerate the development of repeatable machine learning-based AI projects. These templates give users drag-and-drop screens to bring in data and apply appropriate algorithms, making it simpler to create practical applications for AI that can be up and running in weeks, not years. The solution is designed for use by “citizen developers” who don’t have extensive data modeling skills.

This is just one example of the kind of innovation that needs to happen so that enterprises can confidently move forward with upgrades, modernization, and deployment of new digital technologies without worrying about project complications. The technology is there. The solutions are ready and able to make impressive transformations for enterprises, which are ready to grow and modernize. Now, the industry needs to focus on implementation, too, helping deliver on the promises made to customers.