Any technology is only human’s gift to humans. It connects us to people countries apart, or sends humans to the moon or simply makes life much easier for us.
Let’s talk about technology trends that have become indispensable in business: Big data and IoT (The Internet of Things). Capable of providing real-time data and its in-depth analysis, they have empowered businesses to make informed decisions. Quite statistically, big data in inventory management is responsible for revolutionizing the supply chain and inventory management process.
As Eisenhower puts it, “You will not find it difficult to prove that battles, campaigns, and even wars have been won or lost primarily because of logistics.”
Inventory and supply chain are the backbones of any business be it automobile, retail, manufacturing, pharma, etc. All types of industries have to hold some form of inventory or the other. Big data allow them to determine when, what and how much inventory they need in order to maintain the right level of stock. But how? In this write-up, we will talk about Big Data in inventory management.
Inventory Management in the Era Of Big Data
As the name suggests, Big Data refers to the huge amount of data generated by various sources like social media, mobile phones, transactions and many more. The Internet of Things, on the other hand, turns ’ordinary’ everyday things into ‘smart’ objects. These objects when connected to the internet collect and transmit data. This information, when combined with the sources above, becomes big data. Just like both these concepts are closely linked, similarly, they also share an intimate bond with regard to their impact on inventory management.
Before the advent of cloud solutions and the availability of big data, collecting analytical information about inventory required more manpower and resources. Not to mention the high degree of errors that arose due to manual entry into excel sheets. With the coming of big data in inventory management, its associated operations have also become more streamlined than ever. However, too much data without the proper technology, infrastructure, and qualified personnel to handle it, will not serve its purpose.
How does Big Data work in Inventory Management?
Inventory management needs to go beyond traditional methods such as analyzing historical data on sales and stock outs. Algorithms can explore patterns and relationships between various data elements, and business decisions. this will give retailers unparalleled insights into consumer behavior, supplier relations, product performance, offline and online store performance, replenishment planning and more.
Big data in inventory management has innumerable applications. We’ve highlighted a few that we think would be more important in order to fine-tune your inventory and supply chain management process.
Improved Demand forecasting
Big data can help retailers and warehouse managers to predict the demand for products. It gives them an insight into which products are bestsellers and which aren’t performing that well.
Supply chain visibility in real-time
Big data provides increased visibility of the entire supply chain, that too in real-time. You can monitor your supply chain, movement of every product with hawk-eyed focus. You don’t have to guess where your items are or whether they will arrive on time. Big data tracks everything, from each item, where it is and the status of each shipment.
Inventory Planning and Development
With the help of big data, managers can forecast product demand, plan and optimize their inventory to the maximum. This prevents them from wasting valuable resources and money on inventory they don’t need and warehouse space as well.
Wondering and worrying whether your order will arrive at the right time or in the right quantity? Big data takes away your worries. You can optimize your ordering process with big data and analytics making sure you get what you have ordered when you want it. This can greatly improve your on-time order stats and reduce the cost of bringing items to you.
Knowing exactly how much inventory is required to meet customer demand without overstocking is probably every warehouse manager’s dream. Big data enables retailers and suppliers to keep track of inventory levels, how much is needed and where. Since earlier collating information to predict demand, monitor stock levels was a time-consuming activity, big data speeds up the process and encourages timely decision-making.
Big data analytics can help businesses to determine the final price of their product. How? The information about the available supplies, their cost, the pricing of the closest competitor and value of the product or the customer are essential factors that help to decide a fair price for a product that would be reasonable for customers and fulfil the businesses’ revenue expectations. Big data analytics help businesses to decide an average price by evaluating these factors.
The supplier relationship is at the heart of the supply chain. The ultimate goal is to create a ‘win-win’ situation for both the business and supplier. Data has the potential to drive a great deal of innovation in supplier relationship management. The amount of data gathered from electronic transactions and payments can be leveraged to forge strategic vendor partnerships to increase the probability of those ‘wins’ both parties are looking for. Since big data provides real-time info about orders, shipments, delays etc, they also give an insight into vendor performance and profitability. Since vendor partnership is directly connected to achieving the financial and operational goals of a business, keeping both parties happy will consequently lead to better customer service and more satisfied customers. And that the ultimate goal right?
Big Data and IoT make a super force
While we’ve explored Big Data in inventory management; combined with IoT, the emphasis is greater. The applications for IoT in inventory management are limitless. From ‘smart’ warehouses to RFID labelling for real-time inventory, IoT can transform every aspect of the supply chain and inventory management process.