Redefining Inventory Optimization with Cognitive Automation

A balanced inventory is vital to a healthy business. Not only is it a critical component of service levels and customer loyalty, but it ensures promises to customers and consumers are kept. The buffers protect against uncertainty in the supply chain, achieving just the right balance of too much or too little.

Both uncertainty and variability have negative effects on inventory. Excess buffers impact cost flows and create waste, while insufficient buffers impact service and depress revenue.

The changing markets and global challenges outpace the ability to balance inventory. As segmentation increases, complexity in determining best actions grows. Speed and complexity make it impossible to make just-in-time decisions.

Current business approaches don’t fix the problem, and instead, days of inventory continue to rise across the industry, even with advances in technology. Enterprises are struggling to cope, as siloed organizations, fragmented IT, inconsistent data, and the lack of the right talent affect their ability to keep up.

Cognitive automation digitizes and automates processes, and then delivers them through skills, which can be effectively applied to myriad systems, including inventory balance.  

How does Cognitive Automation Impact Inventory?

When businesses use cognitive automation to help balance their inventory, four things occur:

1. Cognitive automation helps enterprises understand the inventory levels and classification.
2. Cognitive automation recommends optimal inventory levels and adjustments.
3. Cognitive automation predicts demand and supply volatility.
4. Cognitive automation acts by updating stock sizing, adjusting inventory classifications and modifying budgets.
   

When comparing legacy technologies and systems with intelligent automation, driven by artificial and machine learning, it’s easy to see the benefits of cognitive automation in managing inventory:

Data & Analytics

• Legacy systems: Companies are using ETL and manual mapping, which are rigid and inflexible.
• Cognitive automation: Using common cognitive data, cognitive automation provides real-time proactive recommendations, and maintains full data granularity and histories, all enriched by external data.
  

Planning

• Legacy systems: Planning pace is at a very low clock speed, with batch orientation, and aggregate levels.
• Cognitive automation: Real-time clock speed, as well as high performance analytics, modeling, and prediction in a single engine, with both memory and granularity.
  

Transactions

• Legacy systems: Transactions functioned under a structured process flow, running manually in silos.
• ‍Cognitive automation: Manual work is augmented and automated, with recommendations for adjustments provided using one interconnected model driving decisions.
  

Data

• Legacy systems: Complex models with an extensively rigid user experience, does not allow for learning or history capture.
• Cognitive automation: Provides data-driven recommendations, and is constantly learning and accessible, via voice, mobile, and browsers.
  

That’s why so many businesses are turning to cognitive automation, which is moving enterprises from an era of people doing work supported by machines, into an era where machines do the work guided by the expertise of people.

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