Vivek Joshi

Picture this: a manufacturing manager, stressed and overwhelmed, navigating multiple data systems and spreadsheets, trying to ensure that all machines are operational, serviced on time, and that repair works don’t take too long. Inefficiencies pop up everywhere, stemming from outdated processes, scattered data and lack of an unified view of all relevant data. This scenario is all too common in the manufacturing sector, where traditional B2B service and support relies on manual, time-consuming processes to piece together information. This often leads to delays, errors and low customer satisfaction.

The breaking point comes when one of his critical machines suffers from unexpected maintenance issues. Attempting to order the correct part, the manager faces a multitude of challenges. The company’s ecommerce platform fails to provide timely, accurate information and personalized recommendations, highlighting the need for a transformative change.

The Traditional Landscape of B2B Commerce for Machine Maintenance

The existing traditional ways of B2B ecommerce are quite inadequate and rely heavily on human intervention. Persons in charge of machine maintenance (like our manager) has to go through one of the following two methods to get the required parts:

1) Call the Salesperson – The entire workflow of this is shown in the following diagram:

Each of these steps is time-consuming and prone to errors.

2) Engage with an existing ecommerce platform, where, to find the basic parts a user has to go multiple steps (pages and clicks) which is anything but user-friendly.

Without access to comprehensive asset data, these processes are further complicated by missing or inaccurate information. This lack of data exacerbates inefficiencies, causing delays and increased costs. As a result, our manager’s company could be forced to halt production while waiting for a critical part, resulting in lost revenue and decreased productivity. The reliance on manual processes also means valuable data about customer interactions and purchasing patterns is often lost or underutilized.

The Demand for Change

As modern workers, who are essentially digital natives, enter the workforce, their expectations for B2B rcommerce are shaped by their experiences in the B2C world. They expect the same level of convenience, speed, and personalization in their professional purchases. These digital natives will increase the pressure on OEMs to provide a better, smarter experience. When this expectation is not met, it leads to frustration, highlighting the gap between current capabilities and user expectations.

Market data supports this shift in expectations. According to a survey by Fictiv, 88% of manufacturing leaders have implemented AI in their operations, and 87% agree that integrating AI into manufacturing is crucial for future success.
Another point to consider is that nearly 60% of machinery executives see their industry’s future as circular, a business model that involves such aspects as designing products for greater longevity and finding new uses for materials that would otherwise go to waste landfills, creating possible new revenue streams, according to Bain Research. This puts an extra emphasis on customer experience and satisfaction.

These statistics support adoption of AI tech and focus on customer satisfaction, underscoring the growing need for digital transformation based on customer and asset Data in the B2B manufacturing sector.
To meet these new expectations, high-quality data is essential. This enables manufacturers to provide accurate, timely, and personalized services, transforming the customer experience and driving operational efficiency.

The Shortcomings of Current B2B Ecommerce Platforms

Current B2B ecommerce platforms often fall short of modern expectations. Described as “dumb” shopping portals, these platforms do not provide intelligent recommendations and insights. The customer does not get any insightful information about their machine or parts on these platforms, especially information relevant to their application, usage type, etc.

A major issue is the lack of a unified view of the customer’s interactions. The root of this problem often lies in fragmented and poor-quality historical asset lifecycle information. When data is scattered across various systems and formats, it becomes challenging to create a unified view of customer and asset information. For instance, a user might interact with different departments such as sales, support, and maintenance, each using separate systems to record interactions. This siloed approach leads to fragmented data, making it difficult for OEMs to have a comprehensive understanding of customer needs and behaviors.

An internal survey of customers revealed that users often navigate between three to five systems (ERP, CRM, FSM, spreadsheets, etc.) to gather all the information about their installed base of data. This fragmentation leads to inefficiencies and missed timelines due to lack of accurate data visibility for the customer. Without having access to accurate data, a customer is not able to plan and predict when they might need a replacement part or service, resulting in poor maintenance and machine downtime.

For example, consider a user who has repeatedly reported issues with a specific part. Without a unified view, this information may not be effectively communicated between departments, leading to repeated support calls and frustration on the user’s end.

Lack of comprehensive historical data also limits B2B ecommerce portals’ capabilities in recommending the right spare parts quickly. To get the correct spare part recommendations, one has to be aware of the entire history of any equipment. This can only be possible with good, clean, and high-quality data.

A survey from Blumberg Advisory Group highlights that 46% of companies believe it is extremely or very difficult and time-consuming to access their customer data, impacting their ability to provide effective service and support. This statistic underscores the critical need for high-quality data to drive effective digital commerce strategies.

The Opportunity for OEMs

Despite these challenges, there is a significant opportunity for OEMs to enhance their ecommerce platforms by leveraging intelligent insights and recommendations. By integrating AI and machine learning to analyze historical customer and asset data, OEMs can transform their customer experience. This not only improves customer satisfaction but also drives additional revenue.

Accurate and comprehensive business intelligence data is crucial for delivering these insights. For example, an OEM that implemented AI-driven insights based on such data saw a 20% increase in sales by identifying previously untapped opportunities. By leveraging high-quality data, OEMs can offer personalized recommendations, anticipate customer needs, and provide proactive support. This approach not only meets but exceeds customer expectations, leading to increased loyalty and repeat business.

Data provides real-time insights into customer behavior, equipment performance, and potential service needs. By analyzing this data, OEMs can identify new sales and service opportunities, segment customers based on usage patterns and service requirements, and develop tailored marketing and service strategies.

For example, AI/ML algorithms can predict when a piece of equipment is likely to need maintenance, allowing OEMs to offer timely service and avoid downtime for their customers. Moreover, these insights can be integrated into e-commerce platforms to provide personalized recommendations, streamline the purchasing process, and enhance the overall customer experience.

About the author:

Vivek Joshi is the founder and CEO of Entytle Inc., a provider of a customer and asset data management platform for original equipment manufacturers. Prior to Entytle, Joshi founded and was CEO of sensor manufacturer LumaSense Technologies Inc. Among other positions, he has also served in executive management at manufacturers including Sun Microsystems and General Electric.

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