Every delayed decision carries a hidden cost. Research carried out by Anaplan and Incisiv has quantified this burden. Supply chain organizations are losing over five cents on every dollar they earn in revenue. We call this the “latency tax” — the cumulative financial impact of slow decision-making.
Bottom line: Indecision has a direct impact on your bottom line.
68% of supply chain organizations estimate they lose 3% or more of their annual sales because they cannot respond quickly enough to changes in demand. This may not show up as an item on the P&L, but it’s manifested in missed revenue from unmet demand and write-off expenses for obsolete inventory.
Our survey of supply chain and logistics executives across North America and EMEA has revealed the key challenges behind the delays in planning and execution.
Reliance on manual intervention
73% of respondents still depend on manual or alert-based exception handling. The system detects the signal, but the response relies on a chain of humans which slows the process of determining and executing the optimal course of action.
If demand increases but can’t be matched to inventory in time, the result is revenue and margin leakage.
Augmenting human decision-making with autonomous, system-generated insights will align the organization tightly to demand shifts and allow plans to be adapted on the fly as the context changes.
Lagging forecasts
Execution must move at the speed of the market, not the seasonal planning calendar. However, 65% of supply chain organizations only refresh their demand forecasts monthly, or even less often, leaving them in a perpetual reactive state.
Frequent forecast updates are critical for decision accuracy as well as speed. Evolving from monthly review cycles to event-triggered forecast updates should be the goal. Leaders in this area are those who use AI to model complex, enterprise-wide scenarios in real time.
Misaligned execution
Only 3% of supply chain organizations work toward cross-functional performance targets. Without shared KPIs and connected data, organizations struggle to respond with speed and alignment.
A foundation of real-time master data — which can be accessed by supply chain, finance, and commercial teams via a single platform — will enable insights to be assessed, analyzed, and acted on fast.
Limited AI investment
Survey respondents state that AI has a critical role to play across every core supply chain use case. However, their investments have largely targeted just one: enhanced demand forecasting.
As yet, AI remains under-deployed across the execution layer, inventory optimization, exception management, real-time network rebalancing, and supplier response orchestration. It’s these capabilities that will have the greatest impact on accelerating response times. Organizations that operationalize AI across planning and execution will be better positioned to respond faster and protect margins.
Compressing the sensing-to-action cycle
In the quest for supply chain efficiency, lack of information is no longer the key constraint. It’s the speed at which organizations can act on it.
Overall, AI is still being used to enhance visibility; not to accelerate response times and build decision confidence. The supply chain organizations that now decide to do things differently — turning intent into actual investment — will benefit from continuous, intelligent planning that adapts to real-time insights.
Through increasing the frequency of forecasts and adjustments, they will respond quicker and more precisely to changing demand signals, condensing complex decision cycles from days to minutes. They will also be able to proactively identify growth opportunities and operational efficiencies earlier.
A unified, predictive supply chain
Minimizing your organization’s latency tax will help transform your supply chain from a cost center to a revenue-enabling engine.
An AI-driven scenario planning and analysis platform helps supply chains protect and reclaim profit margins by:
- Orchestrating planning and execution across the enterprise
- Anticipating disruption with predictive insights
- Automating alerts and proactively surfacing risks and opportunities
- Aligning teams with one shared, real-time data foundation
- Simulating the impact of actions by modeling fast “what-if” scenarios
