By Rob Douglas, VP for UK & I for Adaptive Insights
The pace of business is accelerating and, as such, there is a need for companies to plan with agility to remain competitive.
However, planning system architectures have long had challenges dealing with large and complex models, forcing many businesses to compromise on user experience and comprehensive modelling, hindering the deliverance of scale, performance, and ease of use. With business data nearly doubling every year* and organisations wanting to involve more business users and functions in their planning, things are only going to get worse.
Out with the old, in with the new
Traditional ways of doing planning, reporting and analytics have been static, manual, siloed, and error-prone. Users have had to live with an array of limitations on model scale and flexibility, slower analytics, and offline storage of older, less-used calculations. Legacy planning platforms have also limited the number of possible scenarios that can be analysed, constrained dimensionality, offered insufficient amounts of cubes for efficient reporting and analysis, and placed restrictions on how detailed calculations can be. Simply put, existing planning tools are not suited for modern businesses.
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As the gap between what planning systems offer and what businesses need continues to widen, the key to catching up is to adopt a limitless and active approach to planning. As organisations need to do more ‘what-if’ analysis across multiple business scenarios and get in-depth, real time, insight into outcomes and dependencies across business functions, a crucial development is the availability of single unified platforms that can support rapid planning even as model complexity mounts and the number of dimensions, versions, and users multiply.
While making such a transformation to a modern sales planning platform might seem overwhelming at first, it boils down to ensuring that your system has four critical things: scalability, optimised calculations, dynamic caching, and a diverse set of modelling options.
The continuously growing amount of data that organisations are having to analyse means that any planning platform must be able to adapt to even the most rapid increases in scale to remain sustainable. Static planning, which doesn’t access real-time data, simply does not lend itself to doing personnel planning for, say, 100,000 employees, or running multiple scenarios in weighing a possible merger or acquisition. In practice, scalability can be achieved by having the computing engine be a tightly integrated component of the platform, so that workloads can then be distributed dynamically across servers. Through highly optimised protocols, the servers can communicate with each other, as well as give models the compute and memory resources they need, whenever they need them.
With the pace of business today, organizations can’t afford to spend extra time and resources whenever larger sets of data have to be analysed. For example, most businesses will at certain points want to run ‘what-if’ scenarios on the impact of total labour expenses, the cost of goods sold, and gross profits on the profitability of rising labour costs. Having to wait for a complete re-modelling each time a model like that is altered, even in the most miniscule way, slows the process down in a way that quickly becomes inefficient and forces users to compromise on how much and how often to model. A modern sales planning platform therefore needs to be capable of optimising calculations so that only the changed elements of the model are recalculated whenever adjustments are made to it, speeding the process.
For any Software-as-a-Service (SaaS) application, response time is everything and it can be ensured by what is known as “dynamic caching.” A cache is a temporary storage area that uses fewer amounts of faster memory to improve the performance of recently accessed or frequently accessed data, saving the user time and the computing engine the burden of additional traffic. Caching is dynamic when a processing engine learns and optimises on the fly what it needs to store in the temporary storage area, what it needs to calculate, and when it needs to do so in parallel. For a retailer using a modern planning platform, this would mean being able to forecast sales of millions of stock-keeping units by dimensions, such as location or sales channel, all under the hood, while remaining transparent to the modeler or her business partner.
A modern planning platform will also accommodate a diverse set of modelling techniques. Sophisticated analysis of multi-dimensional sales data will include a mixture of tabular or cube formats, which demands a flexible programme that allows for the user to switch back and forth in a flexible and speedy way. For example, a company might want to model personnel or contracts in a tabular format, while exploiting its dimensionality and model revenue as a cube with dimensionality—and even link them all together.
The future of planning
Modern planning is now entering uncharted territory as businesses are having to solve problems at a pace they’ve never encountered before. And at the very moment when businesses must operate with new levels of agility, a new way of planning has arrived. New technologies that can accommodate the data deluge, combined with a new process that leverages real-time data is changing the way today’s businesses can and should plan. Because now more than ever, planning itself is the strategic advantage businesses have been waiting for.