Managing spare parts is an essential task for any company that manages different sectors. The spare parts category has a higher level of uncertainty in demand compared to other products. Within the lifetime of machinery that is designed to last for more than a decade, there are chances of a part malfunctioning or getting worn out.
Different parts are categorized internally on the basis of their demand. For example, a part that has a long period of time between two repairs is set to one category, parts that need to be changed frequently are placed in another, etc. To sustain customer satisfaction, it is essential for OEMs to maintain their spare parts inventory cautiously.
What is Spare Parts Planning
In the manufacturing industry, OEMs have to pay extra attention to planning and prediction of the supply of spare parts. This coordination with the suppliers is crucial for handling a smooth management process. Only through the right execution can manufacturers ensure timely repairs and services.
Spare parts planning is far more than having parts available in the inventory. It can affect the performance of dealers, directly affect the downtime of equipment, and affect the performance and reliability of the company. It also helps in managing costs, as having too much inventory can lead to high costs. While falling short of inventory can result in unnecessarily costly delays.
Importance of Spare Parts Planning
Operational efficiency of OEMs is closely related to spare parts planning and management. Without a proper plan or prediction data in place, OEMs can end up with surplus or scarcity of parts. Either way, such management can be costly.
A shortage of parts can lead to delays in repair, extend downtime for the equipment, and frustrate the customer, damaging the reputation of the company. The need for spare parts inventory management is severe with products that have diverse spare parts. For example, a product like an automobile has about 30,000 spare parts, and managing such a vast inventory is a huge challenge.
Managing vast data of spare parts has been a huge challenge for OEMs. To keep up with the demands and manage the surplus pressure on inventory, robust spare parts inventory management software are actively being used by OEMs.
Why OEMs Found the Need to Adapt Inventory Prediction and Demand Forecasting?
OEMs have been constantly shifting towards more convincing and compelling techniques for manufacturing to keep up with the market. Inventory prediction and demand forecasting techniques were actively adopted by OEMs for the following reasons:
- Complex Supply Chain
There are several uncertainties associated with the expanding market. Global expansion of inventory and supply adds multiple layers of complexities along with international logistics.
After the pandemic, supply chain disruptions have become common, along with the need to take risks. Inventory prediction tools have helped OEMs in anticipating and taking necessary steps before they even occur.
- Uncertainty in Demand
Demand for spare parts for customers is uncertain and can depend on seasonal trends, new technology, and economic conditions. Traditional methods of managing inventory could not take these factors into consideration and often left companies with delay and additional expenses.
A product’s demand for spare parts, in most cases, highly depends on where it is in its lifecycle. As, towards the end of the lifecycle of a product, the need for repair and spare part replacement increases.
- Increasing Inventory Costs
There were several challenges that OEMs faced when it came to inventory. Overstocking led to increased inventory costs, and understocking led to stockouts and lost sales. Overstocking tends to tie up OEM’s funds on inventory charges, which could be used somewhere else.
- Need to Improve Efficiency
With the help of predictive inventory management, just-in-time manufacturing becomes effortless. Very precise inventory management is required to carry out JIT manufacturing, as the required part must be available at the required time. Along with JIT manufacturing, lean manufacturing is also practiced to minimize waste and improve efficiency.
- Regulatory Compliance
All industries and companies are required to manage their inventory in such a way that it causes minimum or zero damage to the environment. With JIT and lean management, the impact it has on the environment has drastically decreased.
There are several parts that are always in need, as safety is directly dependent on them. With the help of demand forecasting, such parts can always be in stock, even in times of surge.
The Role of Accurate Inventory Prediction and Demand Forecasting
Why is Accurate Demand Forecasting Important?
Maintaining a balanced inventory is essential for the smooth functioning of the aftermarket. Inventory prediction and spare parts demand forecast plays a crucial role in anticipating which part is in demand. Keeping an accurate stock of products based on their demand without overstocking is called demand forecasting.
With the help of accurate inventory and parts forecasting, OEMs can manage their production schedules for smooth functioning. With accurate implementation of this, OEMs can cut down on inventory costs and make sure that the parts are available on time.
The Role of Spare Parts Inventory Management Software
With the help of advanced spare parts inventory management software, OEMs have found the right resources to move away from traditional forms of inventory management. The long bookkeeping methods, with regular updates and frequent mismanagement, have now been replaced with faster and more accurate technology.
Today, these procedures are replaced by smart software that works with real-time data, machine learning, and predictive analytics. With the help of these smart software solutions, OEMs can easily manage their inventory, help in spare parts forecasting, and adjust to changes more smoothly.
With the help of machine learning algorithms, patterns and data can be easily analyzed and used for the improvement of inventory predictions. These software solutions can not only process accurate inventory predictions, but they can also forecast a spike in demand for a certain product. Such predictions are highly useful for companies to adjust to sudden changes and maintain operations smoothly.
Spare Parts Forecasting Techniques
Accuracy can be achieved through various techniques that are available in the market. Most common ones are listed below:
- Time Series Analysis
This is one of the most commonly used methods for time series analysis to forecast the demand for spare parts. This is executed by analyzing the past data, identifying patterns in stock sales and seasonal demand, and identifying noticeable patterns in the same. Moving averages and ARIMA (AutoRegressive Integrated Moving Average) are two common methods in time series models. This method is only useful for parts that are stable and have a predictable demand pattern.
- Casual Models
This model uses the data of spare parts such as age, environmental conditions, usage rates, and economic indicators of demand. This model of parts forecasting is only valid when there is a defined relationship between demand and influencing factors.
- Machine Learning Software
This method of parts forecasting has gained popularity due to its ability to process huge amounts of data in less time. It is also proficient in handling proficient relationships between variables. Machine learning analyses and learns from historical data and identifies patterns that cannot be identified otherwise. It uses techniques such as trees, random forests, and even neural networks.
Describe the Role of Predictive Inventory Management?
Inventory management for predictive spare parts include the process of restocking the needed parts based on data analytics. It functions by analyzing the current data and optimizing the current inventory accordingly.
Benefits of Predictive Inventory Management
- Better Customer Satisfaction
With improved systems in place to manage inventory, companies can work to provide a better customer experience. With reduced downtime and attending to customers on time, the company can increase their accountability.
- Accurate Forecast
Predictive inventory management utilizes past data and real-time data with the help of sophisticated algorithms to forecast demand. This forecasting enables the company to run out of stocks and overstock to ensure that they have the right parts when in demand.
- Operational Efficiency
Better management helps in making the inventory process smoother. With the help of managed and streamlined inventory, companies can focus on their production and customer services.
- Cost Reduction
Managing excess inventory costs more, but with the help of optimized inventory management, this cost can be reduced. In cases of running out of stock, ordering emergency stock can be expensive and also increases the down time of the equipment further causing damage to brand reputation.
- Proactive Risk Management
Predictive models used by companies can help in identifying and forecasting potential supply chain disruptions or sudden hikes in demand for certain parts. This allows companies to take necessary actions beforehand and be prepared for such changes in the future.
What is the Future of Spare Parts Planning & Inventory Management?
The future of inventory management and spare parts planning is rapidly growing and changing. Staying ahead of the game and competing with other OEMs is essential to maintaining a competitive edge in the aftermarket industry. Here are some emerging trends that have taken the aftermarket by storm.
AI and Machine Learning
- Real-Time Decision Making
With the help of AI, OEMs can effectively manage their inventory and enable real-time decision-making. AI algorithms can automatically adjust the quantity of spare parts in the inventory while reducing the risk of overstocking and stockouts.
- Predictive Analytics
AI and ML have changed how OEMs predict the demand for spare parts. These intelligent technologies are trained to examine huge amounts of data in a short period of time and analyze patterns. With the help of these patterns, OEMs can effectively improve their accuracy in demand forecasting.
- Personalized Services:
AI has helped in improving customer service by predicting the spare parts and repairs needed in the near future with the help of real-time wear and tear data.
Internet of Things (IoT)
- Quick Diagnostics
IoT not only keeps the user updated with the performance but also provides insights and diagnostics. When an issue is detected by the machine, it allows OEMs to ensure that the necessary spare parts are available before sending a technician.
- Real-Time Monitoring
With the help of special devices equipped on the machinery, it can access in real-time the optimal performance of the machine. This data is then used to assess when the part needs to be changed, allowing OEMs to manage their inventory accordingly.
- Enhanced Data Collection
IoT has enhanced data collection with the help of equipment on the machine that provides real-time data. This data is essential in assessing the lifespan of the parts, further helping OEMs to predict the replacement cycle of the part accurately.
Blockchain Technology
- Streamlined Process
Blockchain can very systematically streamline the procurement process between suppliers and OEMs. With the help of smart contracts, stocks are self-assessed and automatically placed an order for when they reach a certain threshold. This helps in keeping the process smooth and reducing the responsibilities from the administrative department.
- Supply Chain Transparency
Blockchain technology has offered transparency across all the parts of the supply chain. In the case of OEMs, it denotes complete transparency regarding every transaction made on record from production to delivery. With the help of this transparency, OEMs are able to build more trust among their customers.
What to Expect From Inventory Management?
- Predictive Accuracy
The integration of AI, blockchain, and IoT has greatly increased accuracy, enabling OEMs to predict demand with precision. This has helped in reducing the cost and risk involved in inventory management.
Along with this, the accuracy of IoT in analyzing the real-time performance of the parts has enabled predictive maintenance. Due to this, downtime of equipment has significantly reduced, parts are available anytime, and the overall process is streamlined better.
- Enhanced Sustainability
With the combined efforts of AI and predictive analysis, huge inventory costs have been reduced along with the drastic effects that it has on the environment. This reduced the carbon footprint associated with the product of unnecessary production.
The predictive analysis of this technology allows OEMs to anticipate the next repair and plan their maintenance as well as stock accordingly. Blockchain and AI provide transparent records of materials, ensuring that OEMs can track their effects on the environment.
- Collaborative Supply Chain
The integration of technologies such as AI, IoT, and blockchain has contributed to increasing customer satisfaction and efficiently improved the coordination in the supply chain.
With the integration of these technologies in the aftermarket, data can be shared effortlessly within the chain. This transparency is crucial, especially in industries that prioritize the quality of spare parts.
The quality of the product is increased with the help of real-time updates from each level of inventory. The deep interconnection between different stages of the supply chain with the help of modern technology has helped in improving the relationships of OEMs and their partners.
Optimizing Efficiency with Smart Inventory Management
The future of spare parts planning is in the integration of competitive technologies that offer OEMs the ability to transition smoothly to sustainable production methods. By exploring different options in predictive analytics, OEMs can easily master just-in-time manufacturing, avoiding supply chain disruptions and minimizing waste.
With the help of real-time decision-making and personalized services, OEMs can stay relevant and competitive in the dynamic market. As the aftermarket industry is ever changing and evolving, OEMs that openly accept the changes and challengers that come along while managing the increasing demand will surely achieve efficiency and sustainability.
The future of spare parts planning has great potential and will see rapid growth in the coming years. To understand parts management and demand forecasting better, book a free demo.
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