With globalization transforming the modern supply chain into a more complex process, the need for optimized inventory management has become irrefutable. However, many organizations are still unable to enhance their inventory planning leading to one of the major scenarios — excess inventory causing wastage or out-of-stock events causing opportunity loss. Perishable items such as vegetables and medicines are particularly susceptible to spoilage. Together, these situations contribute to losses of up to $1.1 trillion every year.
Besides these numbers, nearly 81% of customers worldwide have experienced out-of-stock situations at least once. Such frequent out-of-stock situations adversely affect the shopping experience of customers and may even deter them from making future purchases in that store. With optimized inventory management processes in place, organizations can avert these scenarios and boost their profit margins while keeping expenditures low. One of the ways to achieve this is by leveraging analytics in their inventory planning.
How analytics can help optimize inventory
With billions of transactions taking place every day around the world, there is an ocean of data being generated. But only 16% of retailers use this data to derive actionable insights. 24% of the retailers find themselves at the beginner’s level while 60% are getting to the point of harnessing insights from their historical data. Applying analytics on relevant transactional data can help companies to draw crucial insights on customer behavior, popular products, and even items purchased together. Such insights allow brands and retailers to make more profitable inventory decisions.
Here are some of the ways analytics-driven inventory planning is helping companies globally.
Forecasting accurate inventory levels
Manual methods, involving the use of Excel spreadsheets, have traditionally been used for inventory planning. However, these can be tremendously inefficient in meeting customer requirements when demand fluctuated, causing occasional backorders which would raise expenses. With the use of analytics, companies are able to foresee demand fluctuations and operate more efficiently than before. As a result, they have been able to reduce the frequency of backorders and cut down expenses.
Promote suggestive selling
To ensure inventory stock remains ideal, organizations often classify their inventory based on its demand and price. Activity-based costing (ABC) is one such method of discerning inventory where it is bifurcated into three categories based on its value and popularity. This approach is incredibly efficient if retailers are aware of which products are most popular.
Using a market basket analysis approach, the most popular products can be identified and suggestive selling can be propagated. Based on past data, the most popular products bought together can be analyzed and associations between frequently-purchased products can be established. This helps maintain a healthy stock-in-hand.
The reasons for shrinkage varies for different retailers. From theft by customers to spillage during shipment, shrinkage is a predominant issue that affects inventory count. By assessing patterns based on past data, companies can create predictive models to alleviate this problem.
Often, cargo that arrives can be damaged and may be unsuitable for sale. 65% of damaged cargo is the result of improper packaging. By analyzing data such as type of product packaging, mode of transport and transit time, companies can create alerts to maintain extra caution during transportation. Further, analytics can be used to flag stocks that display greater anomaly for investigation and also retain that knowledge for the future.
Obsolescence is a harsh reality of inventory planning and, to address it proactively, it is imperative to know when to anticipate obsolescence and what contingency plans are available to mitigate its outcomes. This can be achieved by drawing out an obsolescence risk assessment plan which will manage the probability of items becoming obsolete and alert the management about the items most vulnerable to it. Analytics can help executives in identifying items that are already obsolete, items that can be purchased swiftly and automate the entire process.
One of our clients, a leading supermarket chain, was able to save $1.8 annually by applying analytics to optimize the replenishment cycles of highly perishable items. By applying a combination of time and price elasticity-based models, it was able to ensure the demand estimate was as precise as possible. Click here to read the full case study.