Using Predictive Analytics for effective Capacity Management
Posted by Julian Diaz
May 04, 2016
Capacity management describes a company's ability to meet present and future demands for its products and services. This involves a wide set of roles, responsibilities, processes and functions that all depend on their successful execution and interplay between one another. Although the working parts are many, the end goal behind capacity management is a shared one: to beat the competition in delivering the best products and services to the customer.
Fortunately, technology has given rise to new methods of aligning business operations with shifting market spaces. Gartner Vice President, Will Capelli, comments about the importance of agile capacity management in the modern era: “It is virtually impossible to use traditional capacity planning – capacity management techniques – to effectively ensure that the right resources are available at the right time.” Capelli also makes special mention of predictive analytics as a central tool to running businesses as efficiently and profitable as possible.
In this blog, we'll touch on how predictive analytics platforms are delivering key insights to improving capacity management and opening avenues to more efficient operations and bigger profit margins.
Looking back to understand future capacity demands
With all roads leading to Joe Consumer, it makes sense to base a large portion of capacity management strategy on customer insights. Predictive analytics is enabling retailers to place customer journeys, preferences, product demands and behavioural patterns in greater context by revealing details otherwise invisible to the “naked eye”. For example, in-store shopper movement, placement of competing products, store and shelf layouts are all factors that influence customer engagement with new products in outlets. Predictive models are able to dissect each of these touch-points and also reveal correlations between them more acutely. FMCG giant, Proctor & Gamble, uses predictive analytics Predictive models are able to dissect each of these touch-points and also reveal correlations between them more acutely. FMCG giant, Proctor & Gamble, uses predictive analytics to determine optimal placement of new products in stores by combining 3D imaging software and predictive modelling based on customer, store, product and competitor data, to determine the optimal placements of new products with relation to these influencing factors. By helping brand managers “test design effects and understand how shoppers move, where they look and how they interact with the shelf, the products, and the advertising,” predictive analytics is increasing the likelihood of customers engaging with their brand.
As the supporting backbone of the business and larger online world, networks are the vital facilitators of a multi-billion Rand e-commerce industry. This places predictive insights at the centre of the monitoring, management and optimisation of technological infrastructure. For example, analysis of historic website traffic can prepare online retailers for spikes in visits to their portal when a new product launches. The now infamous Obamacare website’s technical woes and iTunes server outage of 2013 (the latter caused by an album release by singer Beyoncé that drew 80 000 frustrated fans to the Apple retail site) are both good examples of how lack of capacity planning can undermine the success of a new product or service.
Predictive analytics is making its impact felt far and wide
Predictive analytics and capacity planning are a perfect fit in the sense that the former enables businesses to peak around the corner and pool resources optimally to meet demands. By using our data, we’re able to better align our products to the needs of our customers, or meet a specific demand to their exact specifications – at the right time. To learn more about how predictive analytics can boost your organisation's ability to compete and adapt in an era where change is the only constant, contact us or visit our website.
Julian Diaz is Head of Marketing for Principa. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets.