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November 29, 2015 in Big Data, Business Performance Management (BPM), Cloud Computing, Financial Performance Management (FPM), Human Capital, Mobile Technology, Operational Performance Management (OPM), Social Media, Supply Chain Performance Management (SCPM), Uncategorized | Tags: Accounting, Analytics, BI, Business Intelligence, CFO, close, closing, cloud, Collaboration, Controller, dashboard, data, ERP, Finance, Financial Performance Management, FP&A, FPM, IBM, Intacct, Microsoft, NetSuite, Oracle, Reconciliation, report, Reporting, SAP, scorecard | by Robert Kugel | Leave a comment
The enterprise resource planning (ERP) system is a pillar of nearly every company’s record-keeping and management of business processes. It is essential to the smooth functioning of the accounting and finance functions. In manufacturing and distribution, ERP also can help plan and manage inventory and logistics. Some companies use it to handle human resources functions such as tracking employees, payroll and related costs. Yet despite their ubiquity, ERP systems have evolved little since their introduction a quarter of a century ago. The technologies shaping their design, functions and features had been largely unchanged. As a measure of this stability, our Office of Finance benchmark research found that in 2014 companies on average were keeping their ERP systems one year longer than they had in 2005.
Recently, however, we have seen signs of change. The evolutionary pace of technologies that shape the design of ERP systems has been accelerating over the last couple of years. In addition to the cloud there are in-memory computing; analytics and planning integrated into transaction processing systems; mobility; in-context collaboration; and more intuitive user interface design. While ERP vendors generally acknowledge these innovative technologies, our research and conversations with ERP software users indicates that they are just beginning to make their way into product design and thus far have had little impact on the market.
Then there’s the buzz about “consumerized” ERP and other business applications – fresher designs that look and interact with the user like consumer software such as mobile apps on smartphones. Established screen layouts and process designs often are legacies of technology limitations that no longer exist. In addition, increasing numbers of users don’t want or need to interact with their business applications through desktop or laptop computers. Support for mobile devices has become common, but gestures and other new user interface conventions that expand and improve the ways in which users can interact with their system on other devices such as laptops are a likely future capability, especially as touch screens become common on all devices. Voice interaction, a potentially powerful advance, is still in its infancy. Notifications and approvals increasingly will be accessible from wearable devices and mobile technology watches. Since all business is collaborative, we expect in-context collaboration capabilities to evolve rapidly to improve productivity in every business function, enabling greater responsiveness to customers and speeding the completion of core processes.
Despite the growing popularity of cloud-based systems, the issue of where ERP systems should reside is not settled. The cloud is likely to account for a substantial portion of the market. But it’s useful to remember that even though our research shows that resistance to cloud-based ERP is ebbing and that cloud ERP vendors’ sales have been growing faster than on-premises vendors, the cloud still has a small share of the installed base. A significant challenge for vendors of multitenant software as a service (SaaS) is that the key benefit is also a constraint. Because buyers configure the features and capabilities rather than customizing the core code base, implementations can be faster and less expensive. In issuing new releases or modifications to the software, the vendor makes those changes to the code that everyone is running, either immediately or after a grace period. This requires far less work for the customer than having in-house IT personnel update on-premises versions and patches.
The constraint, however, is that the software cannot be customized. As I’ve noted, the primary barrier to making ERP software more configurable is the inherent complexity of the business processes the systems manage. ERP systems must be able to handle the specific needs of users, which can differ considerably from one industry to another and even between specific micro-verticals that might span multiple business units in a range of industries, locations and jurisdictions. If the software cannot be configured to meet the customer’s feature, functionality and process requirements, and if the customer cannot adapt its operations to these limitations, a cloud-based product isn’t a feasible solution. Many manufacturing and product-centric businesses have found it difficult because their requirements are often too specific and diverse. Unlike with on-premises software, there is no option to customize multitenant SaaS offerings to the needs of a single customer unless the vendor is willing to make the necessary changes to the core code base and the timing of those changes is acceptable to the customer.
Some new supporting technologies will enhance the business value of ERP applications as companies adapt their business processes to take advantage of new capabilities. For instance, in-memory computing platforms and big data likely will change how organizations – especially in finance and accounting – work with computers. Processes can be executed faster, and transaction processing systems can include analytic capabilities. Increasingly, ERP vendors will incorporate performance measurement and monitoring as well as building optimization functionality into business processes.
In-memory processing promises a much more interactive experience while big data management will underpin the sophisticated use of analytics to develop actionable insights, alerts and performance measurement from the masses of data accumulating in ERP systems. Mobile technologies, ubiquitous among the new generation in the form of smartphones and tablets, will drive demand for the availability of on-the-fly analytics and dynamic planning to enhance forward visibility and deepen situational awareness to guide transaction processes. Similarly, the emerging Internet of Things (the network of physical objects embedded with electronics, software, sensors and connectivity to enable objects to exchange data with other connected devices) extends the possibilities for expanding the ERP system’s capabilities in automating the handling of physical assets and the associated record-keeping, analysis and process management.
It’s not just technology. Users of ERP systems are changing, and this is shaping ERP system design. Fresher screen designs and reduced screen clutter are some of the initial improvements. The demographic shift taking place in the ranks of senior executives and managers, from the baby boom generation to those who grew up with computer technology, is creating demand for software that is both more capable and more usable. Soon, to be competitive, ERP systems will have to deliver three major improvements: lower total cost of ownership, a better user experience and greater flexibility and agility.
Despite these growing demands concerning how it works, though, buyers’ expectations for what ERP software should do haven’t changed much so far. But change almost certainly will accelerate over the next five years. Companies’ selection processes are driven largely by their experience with the last generation of products and the pain points they experienced. They view these systems as notoriously time-consuming and expensive to set up, maintain and modify. Indeed, in our ERP research only 21 percent of larger companies said that implementing new capabilities in ERP systems is easy or very easy while one-third characterized it as difficult.
Unlike in the shift from mainframe financial and manufacturing management applications to client/server ERP, this time the larger incumbents will be less vulnerable to disruption. One important reason is that their large maintenance revenue streams provide greater development firepower compared to upstarts. Nonetheless, all vendors will be challenged in the market if they fail to evolve to meet the expectations of a new generation of executives and users. Smaller ERP vendors, whether mainly on-premises or cloud-based, will need to invest in enhancing their software at a faster pace than has been necessary over the past decade.
The ERP software market is poised for the first significant transformation since the 1990s and is the rationale for our new benchmark research we will conduct on this topic. A combination of new technologies and changing user demands will drive changes in system design. The result will be systems that are easier to use and easier to modify to suit the needs of customers. A new generation of users will demand software that makes doing their jobs easier, supports their ability to collaborate and work with the system anytime, anywhere. Change is coming slowly, but the landscape of ERP a decade from now will be very different.
August 13, 2015 in Big Data, Business Analytics, Business Collaboration, Business Performance Management (BPM), Customer Performance Management (CPM), Financial Performance Management (FPM), Information Management (IM), Operational Performance Management (OPM), Sales Performance Management (SPM), Social Media, Supply Chain Performance Management (SCPM) | Tags: Analytics, Performance Management, Price optimization | by Ventana Research | Leave a comment
Optimization is the application of algorithms to sets of data to guide executives and managers in making the best decisions. It’s a trending topic because using optimization technologies and techniques to better manage a variety of day-to-day business issues is becoming easier. I expect optimization, once the preserve of data scientists and operations research specialists will become mainstream in general purpose business analytics over the next five years.
Optimization was first adopted by businesses in the middle of the 20th century, aided by the introduction of digital computers. The first technique that gained broad for a few specific purposes was linear programming, one of the most basic optimization methods. Linear programming enables analysts to quickly determine how to achieve the best outcome (such as maximum unit volume or lowest cost) in a given situation. They do so using a mathematical model that captures the key variables that go into the decision and any constraints that may affect that decision. A food processor, for instance, may use three types of cooking oil to make a product. To maximize its profit, the company needs to determine the exact proportions of the three oils that result in the lowest production cost. However, it can’t just choose the cheapest of the three in every case because for flavor and shelf-life requirements there’s a limit to the maximum percentage of each oil that it can use. Linear programming using the simplex algorithm quickly solves the problem.
As computing capabilities became increasingly affordable, companies could use more complex algorithms to handle ever more difficult optimization problems. For instance, the airline industry used it to determine how best to route aircraft between two cities and to staff flight crews. Not only can software find the best solution for scheduling these assets in advance, it also can rapidly re-optimize the solution when weather or mechanical issues force a change in how aircraft and crews are deployed. Airlines were also in the vanguard in the 1980s when they started using revenue management techniques. In this case, the optimization process was designed to enable established airlines to compete against low-cost startups. Revenue management enabled the large carriers to offer low fares to price-sensitive but flexible vacationers without sacrificing the higher fares that the less flexible businesspeople were willing to spend. The same approach was adopted by hotels in pricing their rooms. Starting in the 1990s markdown management software, which I have written about gained ground. It enables retailers to make more intelligent pricing decisions by monitoring the velocity of purchases of specific items and adjusting prices to maximize revenue. To be feasible, each of these optimization problems require large data sets and sufficient raw computing power.
We’re now on the cusp of “democratizing” what I call optimization analytics. Big data technologies are making it feasible and affordable for even midsize companies to work with much larger data sets than they have been able to in the past. Our benchmark research on big data analytics finds that about half of participating companies already use analytics with big data. This is partly the result of more powerful and affordable data processing resources but also because companies have invested in systems to automate many functions. The rich data sets created by these business applications provide corporations with the raw material for analysis. This data has the potential to enable businesses to make more intelligent decisions. From a practical standpoint, though, the value of these large data sets can only be realized by moving optimization analytics out of the exclusive realm of data scientists and into the hands of business analysts. These analysts are the ones who have a sufficient understanding of the business and the subtleties of the data to find useful and repeatable optimization opportunities. Three-fourths of companies in our research said that they need these business skills (“domain expertise”) to use big data analytics successfully.
Optimization analytics is a breakthrough technology with the potential to improve business performance and create a competitive advantage. You can’t do optimization in your head, and it’s not feasible in desktop spreadsheets for anything but the most basic use cases, such as linear programming optimizations on relatively small data sets. This is a good reason for almost any company to consider adopting optimization software.
Another reason why companies will find it attractive to apply optimization analytics broadly is that the results of applying optimization routines may be superior to using common rules of thumb or relying on instinct and experience. One of the most important lessons for executives about optimization analytics is that optimal solutions are sometimes (but – crucially – not always) counterintuitive to established norms. For instance, in markdown management, retailers often have found that smaller, more frequent price reductions maximize profits and produce a considerable improvement in sales over the end-of-the-season price slashing that was once considered to be the best practice. In financial services, charging your best customers more for loans and other services turns out to be the optimal choice for the bottom line of financial institutions. Another important insight from our collective experience with optimization is that while the value of these analytics as realized in a single event or transaction may be small, it can have a measurable impact on profitability and competitiveness when applied broadly in a business.
At this point optimization analytics is in a dual mode. On the one hand, there are proven examples of the narrow application of optimization such as those mentioned above. On the other, bringing optimization analytics to the masses is only beginning. Some vendors have made progress in simplifying their analytics, but mainstream products are only on the horizon. It’s also important to recognize that, as with past breakthroughs in information technology, there are bound to be more duds than success stories in initial attempts at using optimization analytics. Experience suggests that a small number of companies that have strong analytical skills and a rigorous approach to managing company data will prove to be the leaders in finding profitable opportunities for applying optimization technologies and techniques. Others will do well to find these examples and consider how to apply them to their own organizations.
Robert Kugel – SVP Research