Four Key Priorities for Digital Business Planning

Four Key Priorities for Digital Business Planning

In today’s digital economy, the threat of disruption is ever-present. By now, the stories of new and agile market players breaking through traditional industry boundaries to capture market share is well-known. Airbnb has disrupted the hotel industry, Uber the taxi industry, and Amazon retail.

What all these companies have in common is the effective use of data, which they use to orchestrate experiences that deliver the cost, convenience, and choice customers crave. Importantly, though, this data isn’t used by business units on an ad hoc basis. Quite the opposite. To be successful in the digital economy, companies need to run on data from the ground up, with all lines of business (finance, sales, marketing, operations, supply chain, logistics, and more) sharing data in a comprehensive manner for a common purpose: to deliver better experiences and improved outcomes for customers.

Rethinking Business Planning - A new paradigm

Companies that want to stave off disruption need to think creatively and holistically along the lines of a new paradigm that at SAP we call digital business planning.

Digital business planning seeks to reintegrate supply chain planning and management into the enterprise as a whole so that it is no longer a sub-specialty off to one side of the business. Companies pursuing digital business planning need to think in terms of four key business priorities.

  1. Develop a demand-driven business plan

To survive and thrive in the digital economy, companies should operate according to a demand-driven business plan that shifts the focus from the supply chain to the value chain. The idea is to broaden the planner’s perspective to include all players involved in delivering the final product or service to the customer. Leading companies are moving from manual and sequential planning to automate and synchronized planning where the system uses analytics to resolve problems on its own and machine learning to continuously improve. According to this model, planners are called in to solve problems only in exceptional cases, otherwise, they’re focused on creating value and generating revenue.

  1. Sense, Predict and Respond to change

As companies feel the pressure to move toward faster planning-to-fulfilment cycles, the once separate functions of planning and execution are beginning to blur. To increase agility and responsiveness, companies need a single source of data truth so that all roles can accurately evaluate conditions, simulate the impact of potential actions, and execute decisions in real time. Advanced analytics—with data often fed from embedded sensor data (Internet of Things)—can help not only avoid disruptions in the supply chain but also predict customer behaviour. Many companies are using predictive capabilities to deliver outstanding customer experiences and better outcomes.

  1. Plan holistically across the network

Planning in the digital economy requires an end-to-end approach that widens the lens on the entire value chain. Many companies start internally, integrating planning activities across lines of business. Moving outward, companies then incorporate suppliers (and supplier’s suppliers) in order to collaborate, plan, and deliver more effectively. A flexible supply network platform is also critical, making it easier to discover, onboard, and work with new suppliers to meet constantly evolving and fluctuating demand. Technology leader Microsoft, for example, followed such an approach to dramatically reduce its inventory with a new multi-tiered inventory strategy. Most importantly, leading companies see customers more as integral parts of the value chain, rather than as end points. These companies seek customer input and use advanced analytics to continuously feed it back into the planning process to unlock added value on an ongoing basis.

  1. Increase strategic agility 

Companies across sectors seek the ability to adjust supply chain strategy and portfolio dynamically in response to market opportunities and needs. Leaders on this front have moved to self-regulating, adaptive planning models that help buffer against variability. With live data, real-time analytics, and machine learning tools, for example, companies can optimize planning decisions for higher profitability. Examples include evaluating alternative sourcing and transport strategies to minimize cost and adjusting segmentation profiles to fine-tune inventory levels according to actual demand. With greater visibility into and control over real-time data, companies can now evaluate decisions quickly and drive strategic planning processes that adapt flexibly to shifting demand signals and supply conditions.

Source: All the above opinions are personal perspective on the basis of information provided by Forbes and contributor Richard Howells.

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