You are currently browsing the category archive for the ‘Sales Performance Management (SPM)’ category.

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 softwarevr_Big_Data_Analytics_01_use_of_big_data_analytics 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.

vr_Big_Data_Analytics_14_big_data_analytics_skillsOptimization 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.

Regards,

Robert Kugel – SVP Research

Our benchmark research on next-generation business planning finds that a large majority of companies rely on spreadsheets to manage planning processes. For example, four out of five use them for supply chain planning, and about two-thirds for budgeting and sales forecasting. Spreadsheets are the default choice for modeling and planning because they are flexible. They adapt to the needs of different parts of any type of business.vr_NGBP_09_spreadsheets_dominant_in_planning_software Unfortunately, they have inherent defects that make them problematic when used in collaborative, repetitive enterprise processes such as planning and budgeting. While it’s easy to create a model, it can quickly become a barrier to more integrated planning across the business units in an enterprise. As I’ve noted before, software vendors and IT departments have been trying – mainly in vain – to get users to switch from spreadsheets to a variety of dedicated applications. They’ve failed to make much of a dent because although these applications have substantial advantages over spreadsheets when used in repetitive, collaborative enterprise tasks, these advantages are mainly realized after the model, process or report is put to use in the “production” phase (to borrow an IT term).

Host Analytics Modeling Cloud is designed to address the needs of people who – often working alone – create representations of the business or portions of the business used in a collaborative planning process. These individuals often create analyses and reports that complement the planning process. To date most dedicated applications have been far more difficult than spreadsheets for the average business user to use in the design and test phases. To convince people to switch to its dedicated application, a vendor must offer an alternative that lets users model, create reports, collect data and create dedicated data stores as easily as they can do it in a desktop spreadsheet.

Modeling Cloud is designed to integrate individual businesses unit plans with a company’s financial planning, forecasting and budgeting. It attempts to address the spreadsheet problem by enabling individuals in business units to create and update plans and budgets and their underlying models in a way that is consistent with what they are used to doing, but also makes it easy to tie these together to achieve an integrated company-wide view. Compared to desktop spreadsheets, this approach better enables a company to analyze and refine plans and budgets. It also facilitates advanced modeling capabilities such as rolling quarters forecasting and contingency and what-if planning. Compared to desktop spreadsheets, with Modeling Cloud it’s much easier to consolidate the plans from multiple contributors and then drill back down into individual plans and their underlying assumptions. The software also has mobile features that enable individuals to review, contribute and approve plans and budgets on the go. Each of these capabilities increase the business value of the company’s planning and budgeting.

vr_NGBP_03_collaboration_is_important_for_planningPeople and businesses plan in order to be successful. Companies do a lot of planning – some formal and some informal – about all aspects of the business including sales, production, headcount, distribution and the supply chain. Done properly, planning is the best way to get everyone organized in executing the plan. At that point they can take advantage of collaboration, which is essential to effective planning and budgeting. Our research finds that in the large majority (85%) of companies that collaborate well in their planning and budgeting processes participants regard it as well managed. Dedicated applications work better than desktop spreadsheets when it comes to bringing individual models, plans, budgets and forecasts into an integrated companywide view. In contrast it’s difficult and time-consuming to combine desktop spreadsheets into a consolidated view, and it’s even harder and more tedious to look back into the underlying data in seeking a better understanding of important differences between individual plans and models.

Modeling Cloud is designed to address an important need in corporate planning – closely tying all aspects of business planning to financial planning and budgeting and helping organizations collaborate across business silos. Our research shows that integrated planning works better, as I have written : Two-thirds of companies in which information in individual plans is directly linked have a planning process that works well vr_NGBP_02_integrated_planning_works_betteror very well, compared to 40 percent in which the information must be copied and only 25 percent where there is little or no connection.  As a rule, providing users with a familiar environment in which to create business models, create and compare different business scenarios, analyze actuals and create reports goes a long way toward mitigating the difficulty of having to learn to use a new tool that has been a barrier to the use of dedicated planning software across an enterprise and makes it easy to directly link plans. Business planning can be more effective if individuals have software that gives them a high degree of flexibility to create models and plans in a way that works comfortably for them yet also facilitates the integration of everyone’s plans into a consolidated view.  Our research shows that dedicated planning applications can help users align their plans with strategy and the rest of the organization. For example, companies that use them said twice as often that they are able to estimate accurately one plan’s impact on others as those that use spreadsheets. In addition, two out of three that have dedicated applications said they are satisfied with their planning process and that their plans are accurate.

Information technology has the potential to make business planning more useful, as I have noted, enabling it to improve a company’s performance and increase its competitiveness. One of the necessary tools for more fully integrating business and financial planning is a software and data environment that enables business people to plan their part of the business in a way that is familiar, productive and useful to them in achieving their objectives. That environment also must enable them to communicate the financial consequences of their business plan to inform the financial forecasting, planning, budgeting and review processes. Host Analytics Modeling Cloud is designed to do that. It’s not a perfect substitute for spreadsheets, which still excel in their ability to help people quickly translate their thoughts into models and reports. But because Modeling Cloud eliminates most of the hassles and defects of spreadsheets (for example, the ability to quickly store, retrieve and consolidate data from a single authoritative source), it  is ultimately a much more attractive alternative. I recommend that Host Analytics customers assess using Modeling Cloud in their organization and that buyers of dedicated planning applications include this type of capability in their evaluation of vendors’ offerings.

Regards,

Robert Kugel – SVP Research

Twitter Updates

Stats

  • 101,001 hits
Follow

Get every new post delivered to your Inbox.

Join 75 other followers

%d bloggers like this: