How to increase inventory management effectiveness with Datrics?
For the steady and smooth functioning of the retail and e-commerce businesses, you should have a planned approach to managing stock replenishment, ensuring regular supplies, analyzing the market and trends, forecasting sales.
The lack or overabundance of items at the storage is usually faced by E-commerce Executive, Product Owner, Data Analyst, Inventory/Supply Chain manager or Replenishment manager. Big companies can have analytical departments to handle mentioned situations: collect, compare, analyze the data and predict an accurate number of goods will be needed.
But smaller usually don’t have enough resources. Data science achievements can be helpful in the optimization and automation of analytical routines in retail and e-commerce.
Two main fears in retail and E-commerce
There are lots of different challenges and issues in the daily operations of e-commerce executives: online ordering, order fulfillment and much more. Surely, it depends on the business type and size. We have defined the most common and significant obstacles that can arise in retail and e-commerce:
1. Understocking
Understocking is the situation when your supply of a particular product fails to meet consumer demand. It happens when you don’t want to overpay for goods in order to avoid product spoilage and idle storage. Understocking can cause not only financial problems but also can undermine the credibility of your company.
2. Overstocking
Overstocking is the exact opposite, where your supply exceeds consumer demand. Almost every store or retailer had such an experience. It usually happens when you have a high demand for any item and decide to buy more in order to meet the market.
It’s extremely important to meet the expectation of clients, but both situations (overstocking and understocking) are not good for the customer and shop assistant. If you had the wrong data or didn’t take into account trends and seasonality, you can be in one of these situations. In both instances, you should rethink the strategy for your different categories.
Potential product spoilage: where it comes from
The potential spoilage is mostly related to food and flower production. If you are working in one of these fields your schedules must be accurate, as a retail store management mistake can cost you a lot. Online shopping is very challenging for food retailers because of the low value and high cost of handling of grocery products. Currently, food suppliers cannot ignore the advent of online sales. But
only a few large producers are able to make money on e-grocery.
Other fields of retail can also face this problem, In general, spoilage units are those that do not meet customers’ expectations, and either discarded or sold at reduced prices like defective clothes, shoes or plastic items which can be remelted to produce other plastic products.
Company owners should determine normal and abnormal spoilage in accordance with business specifics. On the one hand, there is a normal spoilage index that will be different for the supplier of fruits and vegetables and for cereal producers. Usually, this percentage is included in the goods’ cost. On the other hand, there is an abnormal spoilage that spreads far beyond the acceptable limit. Such spoilage is charged as incurred costs or a separate cost without the compensation.
Retail inventory management in spoilage optimization
We have determined the most common issues in retail as overstocking and understocking. One of the most effective approaches to solve them is inventory management.
Inventory management includes aspects such as controlling and overseeing purchases, maintaining the storage, order fulfillment, using a company’s inventory. These include the control of not only raw materials or parts but also finished products, as well as warehousing and processing such items.
An inventory is one of the most important assets almost in every company. The company’s inputs and finished products are the core of its business in retail, food production, and other inventory-intensive sectors. A lack of registered items when and where it’s needed can cause unpredictable outcomes. Due to the mentioned reasons, retail inventory management is crucial for businesses of any size. Understanding when to restock certain items, what number of items to buy or produce, what price to pay can simply become difficult decisions. Small companies will usually keep an eye on stock manually and manage the reorder operations using Excel formulas. Larger businesses will use specific enterprise resource planning (ERP) software. The largest corporations use deeply customized software as a service (SaaS) applications.
Demand prediction can be used for more accurate analytics in inventory management. This is a field of predictive analytics that strives to learn and predict customer demand to optimize supply decisions by corporate supply chain and business management.
Historical data is the only thing that is required for a demand prediction. You can have manageable statistics and an understanding of the future flow based on the data you have.
If you sell only one simple item, you probably can do analysis on your own. For larger companies, it is completely important to have an analytical tool that will combine all the aspects such as seasonality and trends. Algorithms usually work well with complex data and provide accurate quarterly and yearly predictions.
How to make your analysis simple
As we mentioned the most important point for analysis is data. Usually, historical data is used for a thorough analysis. Past-periods data used usually as a basis for predicting future data or trends. Historical data includes most data created either manually or automatically within an enterprise: log files, financial reports, product documentation, statistics.
For demand forecasting, you will need at least a few months of historical data in CSV format. It’s challenging to get accurate analytics with such amount of data, but you can get a preliminary prediction. Few months of historical data will work better for fast-moving goods.
You will need a year of available historical data for more accurate analytics. More data is always better, as software algorithms deal best with various records and easily catch complex patterns.
Data Science platform for retail and e-commerce businesses
Currently, Data science has a huge influence on different industries, especially in retail and e-commerce.
Data analytics helps companies to focus on the most relevant goods which are actually the core of the companies’ income. Inventory management could be easily organized and optimized with SaaS solutions.
Data science software can assist with the goods’ optimization. In some instances, the speed of the goods turnover in warehouses influences companies’ income. Analytical inventory management tools can help with reorganizing the replenishment process and, as a result, optimizing income.
SaaS platforms resolve the obstacle of manual work and analysis which usually takes a lot of time. They do not require additional work, coding or manual efforts. The software provides you with numbers based on your historical data analytics and the only thing you will need to do is to make a decision.
Datrics platform makes inventory management easier
As we already noticed, Data science can help with automation and optimization. There are a plethora of different software options both for individuals and businesses. Comparing to other solutions Datrics provides retail companies with a way to leverage their own and public data without writing a single line of code. The platform helps with resolving the issue of overstocking and understocking, demand forecasting, pricing analytics.
Advantages:
Datrics is a fully automated platform. It builds forecast models automatically, manages pre-processing, actualization, maintenance and doesn’t require hours of coding or manual effort for model training. You can concentrate on decision-making and business strategies.
Efficient solution: there are no installation or settings processes. You can make an analysis just in a few clicks. All analytical models are already implemented into the platform and ready to be used. For demand prediction, you will need to upload your pipelines and make a decision based on the proposed numbers here.
You can test our platform on https://platform.app.datrics.ai/signup.
