Simplifying Inventory Management Using Data Analytics
Tracking is a critical competent of inventory management for retailers. It is dependent on a number of factors — from changes in weather to changing client demands. The evolution of technology has enabled businesses to adopt to a more sophisticated method of inventory tracking. New technologies like big data and predictive analytics make tracking and updating records seamless and simpler at the same time. Businesses can make real-time decisions related to orders, supply, customer feedback and damage. Let me put down some of the major ways in which analytics can help simplify inventory management in the retail space.
Forecasting demand for businesses promotes cost cutting.
According to me, one of the biggest advantages of using predictive analytics is the need to be able to forecast demand. Analyzing past trends and future variables allow retailers to arrive at a dependable median. This lets businesses stock up sufficiently to meet customer demands. It also prevents wastage. Not only in terms of excess, but also in terms of blocking precious capital that could have been used productively.
Viewed from the other end of the spectrum, the demand forecasting exercise draws attention to the need for clean, accurate data sources. Trust me, the more reliable the data source the better would be the forecasting.
Improved forecasting has several benefits for retailers.
· Lower spending because of accurate availability of stocks.
· Infrequent clearance sales.
· Better understanding of market trends, which means better strategies for businesses.
· Efficient warehouse operations.
Reaping the benefits of reducing inventory costs.
Cost of housing excess inventory is one of the major concerns for retailers. Stocks which haven’t been sold or shipped inflate inventory costs considerably. This can happen despite having inventory control in place. Three major heads contribute to rising inventory costs.
· Capital costs can lead to wastage of businesses using the capital to fund productive activities like innovation and research.
· Holding costs are costs like rent, utilities, storage space and taxes.
· Handling costs are usually the costs related to labor organizing, moving and packing goods.
Predictive analytics helps with reducing costs, improve frequency and accuracy of ordering cycles. This increases economy in inventory management even if the costs cannot be eliminated.
Analytics supports reduction of inventory shrinkage.
Shrinkage is a real problem faced by retailers globally. All goods stocked do not get sold because of various reasons like damage, reaching expiry dates or even shoplifting. When shrinkage data is tracked and analyzed, manufacturers & retailers can analyze the potential reasons with causes. Statistics from various sources show that the biggest cause of damage is faulty packaging. Retailers can use the intel obtained from analytics to ensure sourcing from reliable suppliers and reduce inventory shrinkage to a great extent.
Better inventory management increases customer satisfaction index.
While customer satisfaction might not be one of the leading factors affecting inventory management for retailers, it is still important. Access to predictive analytics allows retailers to stock inventories at ideal levels. Hence customer demand patterns translate to lower costs in the long run.
Trends identified by a good analytics tool will also allow retailers to be aware of spikes or dips in demand levels as per the seasonality & interest. This allows them to reduce the wastage of excess stock. At the same time retailers can prevent in-demand items from going out of stock. Stock-out items are a direct loss of revenue and a challenge in the supply chain as well.
Retailers should use a personalized strategy.
The lack of an organized inventory process will reflect directly on the bottom line, productivity losses and unhappy customers. Big data and predictive analytics can be used effectively to monitor both market and customer trends, enabling retailers to reap harvests of an efficient inventory management system.
However, each business has its own priorities and focus areas. Hence, the best approach to using analytics for inventory management should be customized per business needs. What do you think? Let me know your thoughts in the comments.
About Author:
As the CTO of Rishabh Software, Srinivasa Challa (a.k.a CS) defines and drives the technology solution roadmap for global customers. CS is responsible for the seamless execution of the company’s technology strategy, development & cross-functional delivery. With 25+ years of experience, he is instrumental in creating CoEs across domains like Healthcare & Fintech. CS is a constant learner who loves to stay updated on technology, healthcare, digital transformation and customer experience design and more.
