This article covers the fundamental aspects of data-driven inventory optimization. Businesses, manufacturers, retailers and other personnel involved in supply chain management, can utilize this article to learn more about what data-driven inventory optimization entails.
This article covers:
- What Is Meant by Data Driven Inventory Optimization?
- The Key Benefits of Data-Driven Inventory Optimization
- The Industries That Are Using It
- What Does It Take to Implement?
The Main Objectives of Inventory Management
Purchasing stock requires a substantial financial investment, therefore predicting the demand for a certain product or products is a key issue involved in inventory management. Factory owners, retailers and personnel have to manage inventory levels and determine the right balance to ensure there is no surplus or undersupply of a product. The ultimate aims of inventory management and optimization are to ensure that:
- Oversupply and its associated costs (storage, distribution, wastage and transportation) are significantly reduced.
- Customer demand for a product is met.
- Items in the inventory are sold quickly.
- Profit is maximized due to increased efficiency and reduction in overall costs.
There are a number of factors which can affect the demand for a certain product or products. New trends and buying preferences can either increase or decrease the demand for an existing product, markets frequently become saturated, and seasonal changes can affect product demand. Additionally, external factors such as natural disasters can have an impact.
Companies often stockpile inventory in order to meet potential future demand, however, this can result in additional holding costs.
Many companies use spreadsheet-based forecasting models in order to try and predict future demand and manage their inventory. However, these forecasting models tend to be elementary and do not take into account all the variables and factors involved. The main issue comes down to being able to differentiate between anticipated demand and actual demand. Predicting actual demand involves gathering enough data in order to make informed decisions about stock purchasing and replenishment.
The Definition of Data-Driven Inventory Optimization
Data-driven inventory optimization refers to the collection and use of big data and algorithms in real-time, to manage and optimize inventory levels.
This contributes to the development of a pull-based system, where products are only produced/ordered when there is a demand for them, instead of a push-based system which is based on expected demand. The implementation of an IoT platform is critical for accurate data-driven inventory management and optimization.
For instance, by utilizing IoT technology, data can be collected about products that have been RFID tagged, in order to automate the tracking and reporting of stock. RFID tags are used to encode digital data about a specific product, such as its location and model number etc. This data can be delivered to the cloud by the RFID reader. The cloud stores the location of the item and the model and can deliver this information to any PC or smartphone. This enables the accurate tracking and monitoring of stock by end-users. End-users can see the existing quantities of stock and the location. Furthermore, the IoT system can be configured to provide outputs such as alerts about when a certain product falls below safety levels in real-time, as well as if an inventory item has been lost. This is just one example of a data-driven inventory management solution.
It is often difficult for companies to determine their best-selling products and stock their inventory accordingly. However using an IoT solution, data about customer patterns, real-time customer reviews about a certain product as well as data from factory ERP and MES systems, can be integrated so that the amount of this product in the inventory is kept at accurate levels at all times. The factory at hand can increase the inventory levels of a certain product based on real-time data. This data can also be transmitted to machines on the factory floor, in order to adjust production levels as required.
There is a need for a robust industrial cloud solution that can collect data about all the variables mentioned above such as customer buying preferences, the tracking of stock and seasonal fluctuations etc. and deliver the analytics needed for inventory optimization.
Data-driven inventory optimization is tightly aligned with Industry 4.0 objectives since it promotes the digitization and automation of stock tracking and management, the integration into digital systems of multiple variables affecting product demand, as well as the use of predictive analytics garnered from big data.
The Key Benefits of Data-Driven Inventory Optimization
Improvement in customer service
One of the main benefits of data-driven inventory optimization is improved customer service levels. The analysis of real-time customer data and patterns allow businesses to match inventory acquisition and replenishment to real-time demand. So, customers are less likely to be left in a situation where the product they intend to order is out of stock.
Categorization of stock in the inventory
Stock can be categorized based on real-time purchases, customer orders, customer reviews and turnover by analyzing the data. Consequently, companies will have an accurate reflection of their best-selling products at any point in time.
Accurate Prediction of demand
By using data-driven inventory optimization, multiple data sources are integrated that traditional forecasting methods do not accommodate. As a result, stock levels in the inventory can be adjusted according to real-time demand variability.
Improved monitoring and tracking of individual stock in the inventory
If data about product location and models are collected continuously by the IoT platform, then information about the existing product quantities in the inventory can be delivered in real-time.
The Industries That Are Using It
Data-driven inventory optimization is being utilized in a number of sectors such as retail, manufacturing, e-commerce, food and beverage, and automotive industries.
For example, Ocado a British online supermarket that does not have any chain stores, but does home deliveries directly, uses data from their factory in order to manage inventory. Amazon also provides an option where they tell merchants where to send a product, using data based on sales.
What does it take to implement?
It is recommended that companies who do not have in-house resources, use a technology provider. This technology provider should be well-versed in the development of IoT solutions that facilitate the flow of information, needed for data-driven inventory optimization.
The company should determine the kind of data that they want to collect and store about their products, such as the location on site, customer patterns and behaviours associated with their products, seasonal demand and other unique product-related data. This information should be given to the technology provider in order for them to develop the IoT system needed to gather and process the data.
Conclusion
IoT based data driven inventory optimization is set to be adopted increasingly since it accommodates the multiple variables needed for the accurate prediction of product demand. End-users are advised to look at different IoT platforms that can assist them with data-driven inventory optimization.
