AI Visibility: The Display Scoring Turning Point for Lubricants

27/10/2026

Why Are Lubricant Shelves 'Blind' to Managers?

I clearly remember an afternoon in August last year in Hanoi. A traditional channel manager for a major foreign lubricant brand sat across from me, looking frustrated. In front of him was a stack of photos taken by field sales representatives. There were many images, but a quick glance revealed the issues: tilted angles, dim lighting, some blurred, and others obscured by other products. He complained that scoring planogram compliance (correct placement and share of shelf) was entirely subjective. Sometimes, if a representative didn't take the photo from the right angle, the old system couldn't recognize it, and the score was unfairly deducted. As a result, tensions between the office and the field team grew increasingly strained. That was the moment I realized: in the lubricant and auto parts industry, where products have highly similar packaging and are often intermixed with other brands, manual monitoring has hit its ceiling.

The market is shifting. If AI Visibility was previously only mentioned in security or factory automation projects, it is now creeping into display management. Multinational corporations in Mexico and Thailand have started applying display scoring to control distribution channels. The implication for Vietnamese businesses is clear: information processing speed determines competitive advantage. You cannot compete with the speed of modern retail chains if your monitoring system still runs on Excel and the naked eye.

Three Critical Decision Points in Implementing Display Scoring

Integrating AI into display management is not simply about installing another app. It requires businesses to choose a path, and every choice comes with a price. Below are three key decisions I frequently discuss with managers.

Decision 1: Absolute Accuracy or Response Speed?

Many businesses believe that 100% accuracy is the gold standard. In reality, in a chaotic retail environment, achieving absolute accuracy is nearly impossible without a standardized photo-taking process. With VTraks, a platform developed by AIVISION, recognition accuracy reaches 99.97% (according to vtraks.com), processing in under one second per image. This figure is reliable enough for bulk scoring. However, the trade-off is that businesses must accept that AI will not 'see' the most subtle violations if the photo quality is poor. A distant, blurry photo cannot be analyzed accurately, no matter how good the algorithm is. Therefore, the first decision is to build a rigorous photo-taking process rather than just focusing on the software. If you choose the path of 'just snap a photo, the software will handle it,' you will be disappointed.

Decision 2: Comprehensive Monitoring or Strategic Focus?

The lubricant and auto parts industry is characterized by a fragmented distribution channel. There are large dealers, small repair shops, and grocery stores. Monitoring all of them simultaneously is a major cost and processing capacity challenge. Some businesses choose to focus AI Visibility on the top 20% of key accounts to optimize resources. This approach helps improve display quality where revenue is highest. Conversely, if you choose broad coverage, you will have good macro data to understand the market landscape, but may lack detailed depth. In Vietnam, with over 1 million retail stores (according to vtraks.com), choosing a 'point' strategy rather than a 'surface' strategy is often more effective in the early stages. It allows the sales team to receive faster feedback, rather than waiting for end-of-month summary reports.

Decision 3: Data for Control or for Forecasting?

This is the deepest decision point and is often overlooked. Many businesses only use display scoring to penalize employees for non-compliance with planograms. This is a passive use. The true value of AI Vision lies in turning display data into a forecasting tool. For example, if AI detects a continuous decline in the display rate of premium lubricants in a specific area over three weeks, the system can provide an early warning of potential market share loss to competitors, rather than waiting for monthly sales to drop before investigating the cause. This requires the system not only to recognize objects but also to analyze trends over time and space. AIVISION, with products like VTraks, aims to provide accurate raw data so businesses can build forecasting models tailored to their specific strategies.

Quick Answers

How does display scoring differ from manual inventory?

Display scoring uses AI Vision to automatically recognize products, positions, and compliance from photos, providing immediate and objective results. Manual inventory relies on humans, is time-consuming, prone to errors, and difficult to standardize across regions. AI helps standardize the process and provides real-time data.

Do I need to replace my entire sales team?

You don't need to replace people, but you do need to change processes and habits. Sales representatives still perform their existing tasks, but the way they take photos and report will be digitized. They will receive immediate feedback from the AI system, helping them adjust their on-site behavior faster. This is support, not replacement.

Is implementation cost the biggest barrier?

Software cost is no longer the main barrier when compared to the cost of channel management errors. The bigger barrier lies in standardizing operational processes and shifting management mindset from 'post-hoc control' to 'real-time advisory.' If a business is not ready to change its processes, the technology will just be an expensive tool that fails to deliver proportional value.

Where Could This Be Wrong?

I want to be honest about the limitations of this technology. First, AI Vision is not a magic wand. It is only as good as the input data quality. If your distribution channel is too complex, with thousands of small SKUs and packaging that changes frequently with each shipment, recognition accuracy will be affected. You need a continuous process for updating product master data. Second, corporate culture. If leadership only uses this tool to pursue accountability, employees will find ways to 'game the system' with intentionally staged photos, distorting the data. AI can detect fake images, but it cannot prevent fraudulent intent if the organizational culture lacks transparency.

Third, the integration issue. Display scoring only works when it is connected to sales data, inventory data, and marketing data. If it stands alone, it is just a beautiful but useless photo archive. Many businesses buy the software but do not invest in data integration, leading to 'data silos.'

Finally, the regional context. In markets like the Philippines or Thailand, technology acceptance and internet infrastructure quality differ from Vietnam. A solution that works well in a major city may face difficulties in rural areas with unstable connectivity. Businesses need to assess infrastructure reality before committing to a deployment scale.

The lubricant and auto parts market is entering a phase where information transparency is a competitive weapon. AI Visibility is not just a control tool, but a platform to better understand the point of sale. The question is not 'Should I use AI to score displays?', but: 'Is my organization ready to act on real-time data, or are we still relying on outdated reports from last month?'

There is more here than one article can hold. Keep reading on the AIVISION blog, look at our display scoring solution, or get in touch.