The business world has changed dramatically with the advent of the digital era. Yet planning across business operations in large companies remains stuck in the 1990s — and that poses a huge risk to execution, performance and profitability.
Companies continue using high-touch processes, tools and techniques to plan across the full lifecycle of procurement, manufacturing, inventory, distribution and logistics. It’s a massive amount of data-intensive manual work that’s slow, costly and imprecise.
And it doesn’t scale to today’s new normal of high velocity and complexity. Large companies are trying to manage hundreds of thousands of SKUs, dozens of sites and partners, and many thousands of customers, all in a highly volatile global environment.
Customer satisfaction, revenue and profitability are on the line. But it’s harder than ever to satisfy customers when digital native companies like Amazon have stoked expectations for faster delivery, more channel choices and more product variations.
Meeting those challenges requires effective planning across time horizons that are typically annual, quarterly, monthly and weekly. That depends on the industry, organizational maturity and how specific teams orchestrate planning, from procurement to logistics, like cogs in a high-performance engine, all running at different speeds and gear ratios.
Critically, planning needs to adapt swiftly to inevitable changes in demand signals, raw materials availability, even unanticipated economic and political events. That’s where legacy planning falls flat on its face because it’s simply too manual, too stagnant and cumbersome.
At Aera Technology, we use cognitive automation, the foundation for “touchless planning,” to understand data, recommend actions, predict outcomes and take action. Also known as autonomous planning or digital planning, this model offloads the humanly impossible work of data aggregation and analysis to machines.
Here’s how touchless planning works: Cognitive automation starts with thousands of daily Google-like data-crawls across applications to create a single top-level layer of virtualized data. The data is indexed, cleansed, normalized and enriched to provide planners across all interdependent functions with real-time, end-to-end visibility not otherwise possible. Then, powerful artificial intelligence/machine learning (AI/ML) algorithms are applied to analyze the information and deliver recommendations that could range from inventory reallocations to increasing production capacity, reducing prices and switching suppliers or logistics providers.
It can take employees and supply chain planners weeks to get to a decision point, after spending 50%-70% of their time on data aggregation and analysis. With cognitive automation, decision time is reduced to minutes. Moreover, decisions can be based on exceptional granularity down to the SKU and location level, a degree of detail that can’t be achieved with conventional methods. The final mile is to close the decision loop by taking automatic actions to orchestrate the decisions in various transactional systems.
By leveraging the touchless planning model, teams are able to devote that newfound time to more strategic and creative initiatives. They can focus on exception management, collaborate with internal and external stakeholders, experiment with what-if analytics and “think outside the box” to devise innovations that improve service levels and reduce working capital. That higher-value work is far more rewarding than the repetitive data work that prevails in status quo planning processes.
Transitioning to a touchless approach requires a few critical considerations.
As we’re already seeing, successful adopters begin with pilot projects that overlay cognitive automation atop transactional systems, with no need to rip and replace legacy infrastructure. They experiment, they tune and they validate. Moving from prototype to production, these innovators are creating a future of planning that’s both touchless and incredibly effective at generating transformational improvements across the supply chain.
This article originally appeared on Forbes.