Today, customers have high expectations regarding choices, availability of products, and the brand’s service quality. This shift in expectations has significantly impacted how OEMs manage their aftermarket operations. It makes it necessary for OEMs to ensure a smooth and continuous supply of spare parts, making spare parts distribution a crucial aspect of supply chain management.
With advancements in technology, innovations like Artificial Intelligence have emerged. AI is resolving the everyday challenges OEMs face in handling spare parts distribution. This is helping OEMs transform several aftermarket operations, including spare parts distribution.
This article will discuss how embedding AI in spare parts distribution helps OEMs simplify the supply of parts across their dealer network. We will also explore how an AI-powered spare part catalog software, such as Intelli Catalog, streamlines this distribution better.
Challenges in Spare Parts Distribution
Ensuring an uninterrupted supply of spare parts is necessary, but OEMs struggle to execute it in the real world. Challenges like fragmented or inaccurate parts information and growing demand hamper the smooth flow of spare parts.
Ensuring Accuracy in Stock Information
OEMs source spare parts from numerous suppliers and supply these parts to a large dealer network. This gives rise to difficulties in managing stock information in real-time while ensuring accuracy. Incorrect or outdated parts information leads to frequent out-of-stock instances that increase equipment downtime or overstocking that drains capital.
Managing the Availability of Diverse SKUs
Spare parts come in multiple sizes and variants. OEMs may find it challenging to manage a wide variety of spare parts, as each spare part requires different lead times. While some parts are more crucial than others to ensure the equipment operates smoothly, it is essential to prioritize their availability.
Minimizing the Risk of Counterfeit Parts
OEMs work with a large pool of suppliers to fulfill the growing demands. Handling this diversified supplier network is a challenge for OEMs, which makes it difficult to ensure the authenticity of spare parts. The distribution of poor-quality spare parts may damage the machine and increase the risk of equipment failure. Unregulated parts distribution leads to consequences like increasing return rates and also damages OEM’s brand reputation in the market.
Meeting High Demand
It’s essential for OEMs to maintain a continuous supply of spare parts with higher priority and demand. When such parts are unavailable, it may prolong equipment downtime and affect service efficiency. However, OEMs struggle to fulfil high demands due to various reasons, including supply constraints at the supplier’s end or the low efficiency of logistics teams.
Prevalence of AI in Spare Parts Distribution
Artificial Intelligence has flooded the aftermarket with innovations and enabled OEMs to optimize their operations better than ever. It has transformed multiple aspects of spare parts distribution, including stock optimization, documentation of sales demonstrations, and communication between OEMs and their dealers, distributors, and retailers.
This section will discuss the characteristics of AI in spare parts distribution.
Spare Parts Demand Forecasting
AI tools analyze the spare parts sales data to forecast the upcoming demand for parts shortly. These tools go through the historical data, patterns of usage, and other external factors such as weather conditions, seasonal trends, and machine utility. This analysis helps OEMs identify patterns and trends to identify which parts experience higher demand than others and take measures to ensure their availability. This predictive approach helps in maintaining a continuous supply of parts and improving spare parts distribution efficiency.
Stock Optimization
AI tools analyze parts catalogs and evaluate each spare part to determine its criticality. This provides a clear understanding of how essential a specific part is to the operations. It also helps the OEM understand the need to maintain the stock of specific spare parts optimally.
Machine learning classifies spare parts into fast-moving, slow-moving, and obsolete. OEMs can easily develop a stocking strategy and prioritize parts availability according to this classification. It ensures that each spare part is just-in-time, i.e., ensuring part availability while reducing high storage costs.
Automated Procurement
Based on demand forecasts, AI tools help OEMs in automatically placing orders for spare parts. OEMs can set triggers for specific parts, and the system places orders once those parts are below a pre-specified stock value. This automation helps in ensuring that parts with higher lead times are ordered well in time and minimize stockout instances.
Supplier Evaluation
AI agents use metrics like parts history, delivery speed, and supplier reliability to evaluate supplier performance. Based on the findings of this evaluation, AI agents recommend suppliers with better reliability and service to the OEM. These recommendations help in ensuring OEMs choose only the supplier with the highest authenticity, minimal delivery time, and those offering the best prices.
AI agents also analyze data to monitor supplier performance over time and detect inconsistencies in their supply operations. This analysis enables OEMs to make negotiations for pricing or delivery deadlines while also ensuring compliance with the brand’s procurement policies.
Visual Recognition for Identifying Parts
AI systems use vision recognition to identify any shortcomings in spare parts using images or 3D scans. These shortcomings may include broken, damaged, or worn-out spare parts. AI algorithms identify the replaced part to check if it matches the original one and ensure that the right part is being replaced.
This accurate identification using images and 3D visualisations also helps in reducing the manual errors involved in spare parts selection. This use case is specifically useful for the field technicians who need to reorder spare parts but have limited knowledge of part details, such as VIN/serial number. They can simply scan the part using a smartphone and instantly check if the replacement is correct.
Integration of Predictive Maintenance
In the aftermarket operations, certain spare parts might have higher demand than others due to their functionality and design. It is because these parts are exposed more to external surroundings, and factors like temperature and humidity conditions greatly impact them.
In integration with Internet of Things (IoT) sensors, AI tools monitor real-time functionality data. This data may include factors like surrounding temperature, equipment vibration, noise, and others. Monitoring real-time data allows OEMs to detect wear and tear instances and the risk of machine failure early. AI algorithms analyze this data and recommend the spare parts that are more likely to wear out.
Improved Back-Office Support
During peak demand for spare parts or parts supersessions, the back-office teams might get overwhelmed while handling numerous support tickets. AI-enabled chatbots and virtual assistants help OEMs to overcome this problem and improve efficiency in communication.
These AI aids enable open communication between technicians and back-office teams. This assists the support team in guiding technicians through troubleshooting steps, suggesting the correct parts as per the details provided.
Intelli Catalog for Seamless Spare Parts Distribution
Intelli Catalog is an AI-powered spare parts catalog software that streamlines how OEMs manage and distribute spare parts across their supply chain. With innovative AI capabilities, Intelli Catalog has transformed the spare parts distribution for OEMs.
AI Features of Intelli Catalog That Facilitate Spare Parts Distribution
The following AI features offered by Intelli Catalog help OEMs streamline the distribution of spare parts across their supply chain:
1. AI-Powered Parts Identification
Due to incomplete and fragmented available information on spare parts, it's challenging for distributors to identify the right spare part with accuracy. Intelli Catalog overcomes this challenge by using AI-aided computer vision to recognize spare parts through images.
This feature allows technicians to easily identify the damaged or outdated part simply by uploading its image using their smartphone. Even with low quality and clarity of the image, AI algorithms analyze and accurately match it with the correct SKU in the catalog. This accurate parts identification helps in reducing errors and minimizing parts return rates.
2. Demand Forecasting and Smart Stocking
AI-enabled machine learning algorithms in the Intelli Catalog analyze data across multiple verticals and help OEMs predict which spare parts are likely to be in demand. These verticals may include data from sales patterns, service histories, and seasonal trends. The insights derived from this extensive analysis empower dealers and distributors to regulate the in-store stock levels optimally. This data-driven predictive approach helps prevent stockouts and also avoids overstocking of slow-moving spare parts.
4. Natural Language Search
Traditionally, technicians need to enter exact part numbers or technical inputs into parts catalog systems to search for a given spare part. To mitigate this, Intelli Catalog allows dealers and distributors to use their everyday natural language to search for the required part.
For instance, instead of entering the VIN/serial number, the dealer can simply enter ‘steering rack and gearbox of the 2022 SUV model. AI algorithms analyze the natural language to interpret the intent and deliver the right part quickly.
5. AI-Powered Image Cleansing
Due to frequent supersession of spare parts, OEMs need to update the details and images of parts to ensure the latest information is available to the dealers for correct identification. The AI-enabled image cleansing feature of Intelli Catalog enables users to update spare parts images in real-time. It comes as a mobile app to allow users to capture images easily through their smartphone and upload them to the system.
The AI algorithms enhance these images to studio quality by removing background noise and adjusting brightness and sharpness. Users can also add branding elements like logos and adjust color palettes to customize the images as required. This innovative feature ensures that dealers can easily identify parts through clear product images, even if they are clicked in places like a warehouse that has poor lighting conditions.
AI-Powered Distribution in Secondary Sales Network
In addition to the primary sales channels, AI tools also assist OEMs in managing their secondary sales channels for the distribution of spare parts.
1. Route Optimization
AI tools optimize the traveling route with real-time visibility on traffic. This assists the sales representatives in reaching the right retailers within time and making sales proceedings.
2. Assist in Parts Pitching
Insights derived from AI-powered tools help the representatives understand the ongoing market demand. This analytical understanding enables them to pitch the right parts at the right time and improve conversion rates.
3. Complete Visibility into Retailer Performance
Using AI-driven analytics, OEMs get complete visibility into sales data and current trends in the market. This enables them to evaluate the performance of each retailer based on figures. This also helps them provide constructive feedback to the retailers, highlighting both strengths and areas of improvement.
Conclusion
Spare parts distribution is a crucial aspect for OEMs to regulate aftermarket operations. An efficient distribution network helps OEMs meet the emerging demand for spare parts in the market. This continuous distribution of spare parts helps them prevent stockouts and build brand reputation. It also enables OEMs to avoid overstocking spare parts that cost a high standing capital.
Continuous technological innovations and the evolution of Artificial Intelligence have transformed how OEMs handle the distribution of spare parts. AI-powered spare parts catalog software like Intelli Catalog solves the challenges OEMs face and simplifies spare part management.
AI algorithms analyze historical data to understand usage patterns and break down instances, and predict the demand for spare parts. This spare parts demand forecasting helps OEMs to ensure that such parts with higher demand are readily available.
To understand more about how Intelli Catalog can help your business streamline spare parts distribution, connect with our experts today.
