Why usage of big data is essential for warehouses to stay competitive

What precisely do you keep at your warehouse?

That seems like a simple question, but it actually is not. It turns out that many warehouses are unsure about the particular goods they are storing, and this ignorance has a significant impact on the entire supply chain.

We're talking about core inventory data, which is comprehensive, in-depth information on a product's size, weight, and photo, as well as information about its inventory levels. This data serves as the foundation for all warehouse processes.

The advantages of mastering data usage

Many businesses believe that (master) data acquisition is time-consuming, costly, and should be avoided. Or they are unsure on how to move forward and how to effortlessly include the master data collection into their processes.

However, the benefits of gathering relevant data are huge. Logistics operations can be enhanced, errors can be eliminated, and savings can be made with properly recorded and evaluated data.

Logistics operations can be enhanced, errors can be eliminated, and savings can be made with properly recorded and evaluated data.

Master data, for instance, can support to determine where goods should be stored and makes it easier to retrieve inventory from the warehouse. Therefore, inventory information on stock levels and storage capacity aids in streamlining throughput times, ensuring timely replenishment, and preventing item bottlenecks. Even the calculation of stacking patterns to determine the ideal placement of the corresponding packages on the load carrier when the order is initiated is made possible by comprehensive master data. this enables the best possible use of load carriers and transport vehicles to cut down on errors and picking times.

The features of particular goods frequently affect how they are handled along the supply chain, therefore thorough information helps guarantee effective warehousing. Additionally, it is possible to accommodate unique storage needs, such as those imposed by legislation or due to security concerns. Another benefit is that staff in logistics and production have an easier time doing their jobs thanks to well-maintained master data. The client ultimately gains from this as well.

An example of a company mastering data usage: Walmart

Walmart has mastered the conventional supply chain. It also invests heavily in technology to take advantage of the rise in e-commerce and other trends. It takes more than chance to complete all of these tasks perfectly.

Walmart used a number of strategies to create one of the most effective supply chains ever. Through a combination of vendor managed inventory (VMI), improved supplier collaboration, and connecting the various entities through technology for significant efficiency gains, the company was ultimately able to maintain the low prices on which it had built its brand in an economy where costs would otherwise be rising everywhere else.

Although Big Data analytics did not only contribute to Walmart's success, data was crucial in enabling predictions, projections, and creative decision-making to fully exploit the opportunities present across the supply chain. Of course, not every business seeks to be the next Walmart, but any business can reach its full potential if data is wisely used to spur innovation and improve the supply chain.

Why is (master) data often inaccurate or difficult to gather?

Master data is quite simple to obtain if you work for a manufacturer who ships their own goods. After that, it becomes more difficult. This is because:

  • Transfers between supply chain partners. Every time goods change hands, whether it be for the procurement of raw materials, subcomponent assembly, finished items, or distribution, there is a chance that data will be lost, altered, or not transferred.
  • Incompatible or non-communicating systems between supply chain parties. It's not always possible to convert data from one format to another, especially when it's done manually. The same data may be stored differently by various ERPs, TMSs, and WMSs.
  • Product alterations. Albeit, the quantity and price of the products may not have changed, other characteristics could have. Revised information regarding these changes might not get to the shipper or the warehouses down the line.

You could start to wonder how any data ever gets communicated accurately anyplace if you compound these obstacles by the large amount of phases in the supply chain (from manufacturing to shipping to storing etc.).

Why data should be accurate

Your business depends heavily on the quality of the data in your WMS or ERP. While both bad data and no data can result in poor decisions, low-quality data is worse than having no data. That’s because low-quality data still costs money to gather and maintain and can give decision-makers a false sense of confidence.

For instance, faulty inventory counts for a consumer good in the months leading up to the holiday season could generate a shortfall of inventory, order fulfillment delays, order cancellations, and dissatisfied customers. The miscalculation might have further led to broken promises to customers and substantial harm to the firm’s reputation in circumstances when these issues were brought on by poor data that management thought was great.

Why today's warehouses should prioritize data-driven decision making

"You will not find it difficult to prove that battles, campaigns, and even wars have been won or lost primarily because of logistics." - President Eisenhouwer

"You will not find it difficult to argue that battles, campaigns, and even wars have been won or lost primarily because of logistics", former US President Eisenhower declared. This just serves to emphasize how crucial efficient logistics management is.

Today's warehouses must be adept at navigating change, as consumer demand and technological improvements force them to keep up or risk falling behind. The use of Big Data to guide decision-making is becoming more and more significant in this equation. To use Big Data to improve the supply chain, however, one needs identify the appropriate data, ensure a consistent flow of correct data, and one must convert this data into valuable insights. As a result, it's crucial for businesses like warehouses to embrace big data as well as become experts in managing and analysing it for decision-making.

Within the field of inventory management, Powerhouse AI supports warehouses in gathering (master) data while using our automatic inventory counting and checking application. One can think of data regarding dimensions, pieces per carton, layer configurations or specific product codes for example. This way, warehouses that are constraint in labor can gradually build up their quality data foundation and profit from the many automation features that Powerhouse AI has to offer along the way. One example of a feature levering master data like dimensions is counting and checking all boxes on a pallet based on just a single picture. Do you want to know how your warehouse could benefit or would you like to try the application? Please, feel free to get in touch.

Get a demo today