Why More Data Still Hasn’t Fixed Operational Visibility in Retail & Logistics 

Retail and logistics executives are drowning in dashboards, reported metrics, and alerts – but continue to be caught by any failure to meet the promise of on-time delivery, product out of stock, and overrun SLA. 

The irony? ERP, WMS, TMS and IoT is already available in most organizations. The data exists. But real-time operational transparency does not. 

Leaders still struggle to answer basic questions in the moment: 

• Where exactly are bottlenecks today? 
• How long are key shipments expected to delay?  
• What are the SKUs that are causing lost sales to stockouts? 

The problem is not data scarcity. It is fragmentation of data and unintelligence that transforms raw operational signals to an insight that is ready to make decisions. 

1. The Visibility Paradox 

The amount of global data is projected to grow to 175 zettabytes by 2026, even though currently only a small portion of retail and logistics data (approximately 32 percent) is being analyzed and used to make decisions.  

The issue isn’t data volume. It is the incapability to convert piecemeal signals into usable intelligence.  

The reasons are structural: 

• Systems generate data but do not communicate with each other 
• Leaders receive periodic reports rather than real-time insights 
• Alerts are often outdated by the time they are reviewed 

There is no lack of data in organizations. They lack intelligence that can really be utilized.  

2. What Operational Visibility Really Means 

  Operational visibility refers to the ability to understand what is occurring throughout your operations – and be able to act on it – in real time, in order placement all the way to the final delivery. 

Key signals leaders need include: 

• Order status at every operational node 
• Inventory levels across warehouses, stores, and channels 
• Carrier performance and delivery exceptions 
• Customer demand patterns and shifts 
• Returns, quality, and service-level data 

In the absence of these signals in a single perspective, there is reactive rather than proactive decision-making. 

3. Why Current Systems Fall Short 

Each system was made to maximize an activity – not to provide an end-to-end operational intelligence. This makes the leaders perceive the fragments rather than the whole picture.

Typical technology stacks include: 

• ERP systems 
• Transportation Management Systems (TMS) 
• Warehouse Management Systems (WMS) 
• Point-of-sale platforms 
• IoT sensor data from warehouses and vehicles 

Every system runs on a separate set of data and schedule.  

Data exists across the enterprise, but there is no unified intelligence layer to normalize, correlate, and interpret it in real time. 

4. The Cost of Visibility Gaps 

Issue and business impact: 

• Poor inventory visibility results in up to 1.75 trillion dollars lost globally each year due to stockouts and overstock 
• Inefficient logistics drives 20 to 30 percent cost increases from delays, re-routing, and missed SLAs 
• Lack of real-time insight causes decision lag, customer dissatisfaction, and lost revenue 

For most organizations, these losses show up quietly – in missed revenue targets, rising fulfilment costs, and frustrated customer.  

5. Common Operational Visibility Pain Points 

• Latency in data flow 
  Reports are generated daily or weekly, leading to decisions based on outdated information. Leaders respond to yesterday’s issues rather than today’s risks. 

• Disconnected alerting 
  Alerts are triggered within individual systems with no consolidated view, creating noise instead of clarity. 

• Lack of predictive signals 
  The majority of dashboards are historical.  

Most dashboards are backward-looking. Without predictive signals or risk scoring, teams are forced to guess instead of anticipating disruptions.  

6. What True Operational Visibility Looks Like 

Organizations with strong operational visibility rely on real-time intelligence signals rather than static reports. Instead of reacting to alerts, leaders operate with foresight. 

These capabilities include: 

• Unified operational dashboards across systems 
• Predictive risk scoring for inventory and logistics 
• Cross-system alerts enriched with business context 
• Scenario-based forecasting 
• Automated exception prioritization 

A simplified intelligence flow looks like this: 

This is the difference between reacting to problems and preventing them. 

7. Why Traditional BI Tools Are Not Enough 

Business Intelligence tools are effective for historical reporting, but operational visibility requires more. 

It requires near real-time processing, cross-domain context, actionable signals rather than charts, and predictive prioritization. 

Traditional BI answers the question of what happened.  

Operational visibility answers what is happening now and what is likely to happen next. 

8. How AI Enables Operational Visibility 

Artificial intelligence changes how operational data is used by: 

• Mapping patterns across siloed systems 
• Predicting disruptions before they occur 
• Translating raw data into ranked, actionable priorities 
• Continuously learning from operational outcomes 

The only thing that makes AI valuable is that it improves operational decisions directly – not because it can bring additional dashboards. It is an intelligence layer over the systems and one that transforms disjointed data into visible, prioritized actions. 

9. Key Benefits for Retail and Logistics Leaders 

• Decision signals are real-time making it easier to respond quickly.  
• Consistent operational perspectives minimize interruptions and firefighting. 
• Forecasting helps enhance planning and minimize stock outs. 
• Computerized prioritization reduces operational expenses.  
• Customer satisfaction and confidence is enhanced with end-to-end transparency. 

Perimattic’s Solution Approach  

The operational visibility problem has no solution involving the replacement of systems, but rather it includes the addition of intelligence over the systems. The traditional systems can generate only data; to get actionable insights based on the data, one must have an AI-Powered Intelligence Layer that can enable him/her to transform that fragmentation into clarity. 

At Perimattic, we focus on building AI-driven intelligence layers tailored specifically for retail and logistics environments. 

Our approach includes: 

• Integration between the current ERP, WMS, TMS, IoT, and POS.  
• Normalization of fragmented data on the operational activities.  
• Use of AI models to produce quality real-time and predictive decision response.  
• Delivery of dashboards and alerts that emphasize relevance and business impact rather than volume 

Perimattic’s intelligence layer assists organizations in transforming fragmented data into actionable insight, rank exceptions by impact, have real-time and predictive visibility, and achieve transparency across the entire value chain of operation. 
For leaders looking to move from reactive operations to predictive control, operational visibility is no longer optional.