Why the market needs Lubinpla

In the industrial chemical sector, product suitability and application stability are not just quality control issues — they directly influence operational continuity, customer satisfaction, and profitability. Unplanned equipment or process stops continue to be one of the most expensive operational failures. 

According to global surveys, industrial sites experience unplanned outages at least once per month, with the average cost of downtime reaching $125,000 per hour. In high-value sectors such as automotive, that figure can surge to $2.3 million per hour (ABB, 2023; Siemens, 2024). This harsh financial reality means that every mismatch between product capability and process conditions can trigger disproportionate losses. 

Customers Are Ready to Switch 

Today’s B2B buyers no longer tolerate poor performance or ambiguity. When disruptions occur, they actively seek alternatives.  If there are no specification constraints, they are even more willing to change. Recent research shows that over half of industrial buyers cite poor digital experiences, a lack of tracking across support channels, or slow expert access as top reasons to switch suppliers (McKinsey, 2024). Furthermore, a growing portion of these buyers are willing to make large purchasing decisions over $500,000 — even through digital channels, provided the offering is data-rich, transparent, and tailored to their specific needs (McKinsey, 2022; McKinsey, 2024).

This buyer behavior is not merely a preference shift; it reflects a structural change in industrial procurement. In fact, global sourcing teams increasingly adopt supplier diversification strategies as a hedge against performance risk, often maintaining multiple qualified sources to ensure continuity even if one fails (CIPS, 2024). What this means for manufacturers is clear: the ability to present well-documented, justifiable alternatives in real time is no longer optional — it's essential to retain the business.

Traditional Sales Tactics 

Despite these clear signals from the market, many sales and technical teams still rely on conventional selling methods — offering products based on price tiers, brand familiarity, or specification matches. These approaches often overlook the operational complexity of modern manufacturing environments, where the interplay between temperature, pressure, materials, and process speeds can drastically alter chemical behavior.

Moreover, many manufacturers continue to operate through multi-layered distribution channels, which distance them from the end-user’s real needs. While some attempt to address this gap through dealer training or sales enablement programs, these efforts are hampered by workforce challenges. With a significant portion of the skilled industrial workforce approaching retirement, and new hires often lacking field experience, technical consistency is increasingly difficult to maintain (Deloitte & The Manufacturing Institute, 2024).

This disconnect between product design and field application creates a vacuum where no one has full visibility into what is actually working, under what conditions, and why. It is in this vacuum that product failures, miscommunication, and repeated claims thrive.

What Professionals Need To Do 

To stay ahead in today’s volatile market, professionals must go beyond intuition and base every decision on structured, real-time data. The ability to prove product fit, minimize downtime, and align with customer-specific needs is no longer optional, it’s a competitive requirement.

Bridge the Gap 

Lubinpla is designed to close this operational intelligence gap. Rather than being just another database or search tool, it functions as a real-time, domain-specific copilot that aligns field data, product specifications, and historical application results.

It starts by modeling the critical relationship between what we call “Project Conditions” (actual field environment factors like temperature, material, pH, speed, etc.) and “Key Factors” (viscosity, additive composition, flash point, recommended concentration, etc.). Using this model, Lubinpla identifies not just suitable products, but optimal ones, offering side-by-side comparisons with evidence-based reasoning. These comparisons are structured around clearly defined parameters: required, recommended, and forbidden. 

From Monitoring to Root Cause Diagnostics

The platform doesn’t stop at product selection. It continues supporting users through operation by ingesting real-time data such as pH, bath concentration, fluid turbidity, and temperature, then flagging any deviations from baseline conditions. It then cross-references these deviations with known cause-action chains, allowing for root cause diagnostics that shorten response times.

In an industry where the world-class benchmark for MTTR (Mean Time to Repair) is under 5 hours, tools like Lubinpla play a critical role in making fast recovery possible. In fact, research shows that well-structured predictive maintenance strategies — when grounded in clean, unified data — can reduce downtime by 30–50% and maintenance costs by 18–25% (McKinsey, 2017; McKinsey, 2018; McKinsey, 2021; Deloitte, 2024).

Unlocking Institutional Knowledge and Breaking Down Silos

Another key strength of Lubinpla lies in its ability to turn scattered knowledge into structured, reusable insights. In many organizations, knowledge is locked away in unstructured documents, one-off emails, or the minds of senior technicians. When those individuals leave or retire, the knowledge goes with them.

According to IDC (2025), over 80% of IT leaders cite data silos as the single biggest barrier to operational digital transformation. Lubinpla breaks these silos by centralizing VOC feedback, SDS/TDS documentation, claim histories, and real-world performance data into a single interface with clear versioning and traceability. This allows sales, R&D, quality, and field engineers to work from the same page—literally.

A Platform That Also Supports Commercial Growth

By providing transparent logic behind every recommendation and revealing the economic and technical implications of product fit, Lubinpla enables commercial teams to not just retain customers, but upsell and cross-sell more effectively. During critical problem-solving conversations, Lubinpla arms representatives with comparative charts, ROI calculators, and use-case SOPs tailored to the customer’s specific project conditions. This type of consultative selling is proven to outperform transactional approaches in modern B2B settings.

Conclusion

In today’s industrial chemical market, where each hour of unplanned downtime can wipe out six figures of revenue, and where customer loyalty evaporates in the face of poor digital support, the ability to make fast, justified, and context-aware decisions is a strategic advantage. Lubinpla enables experts to do just that — not by replacing their expertise, but by enhancing it with structured data, reusable knowledge, and decision-grade logic. It transforms troubleshooting from an art into a repeatable process and turns technical sales into a high-confidence advisory role. In short, Lubinpla doesn’t just help you sell a product. It helps you prove it belongs.