- Solutions Overview
- Inventory Management
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Big Data has been a dominant topic across industries for a few years now, and with good reason. Big Data can be a powerful tool; but many organizations still struggle with the right way to extract useful insights from the ocean of data they are collecting (or planning to collect).
This is important. Many corporate leaders are unsure about how to put Big Data to work, and part of the confusion stems from the fact that decisions are based not on raw data, but on digestible reports constructed from that data. Which means that reports must be understandable, accurate, intuitive, and timely—in a word, accessible.
Logistics provide a great example of the power of the accessible report. The flow of items through an organization can generate mountains of data. Much of this data might seem mundane at first: A new order of widgets has arrived, three boxes of whats-its are shipping to Kansas, and so on. But hidden within the aggregate are important pieces of knowledge that can provide a window into the business as a whole. For example:
Shipping records, including destination, quantities, and purchasing patterns, can provide extensive amounts of marketing intelligence. For example, one can answer questions such as:
These answers can in turn inform business decisions and help substantiate other forms of marketing research.
There are more choices when it comes to carriers these days, and negotiating contracts with them is a vital exercise. Generating reports on various performance indicators, such as frequency of invoicing errors and number of delayed deliveries, can help companies gauge carriers’ reliability and speed, which in turn can help inform rates and contract awards.
Many things can influence a product’s velocity, the rate at which the item moves through a company: Seasonality, availability of good labor, recent marketing and advertising promotions, and even the weather. When enough data are collected, patterns begin to emerge, predictions can be made, and inventory can be adjusted accordingly to meet demand.
A great example: Walmart stocks extra Strawberry Pop-Tarts in local stores when a hurricane is reported in the news. Sales of the breakfast item spiked before hurricanes (the pattern) as people stocked up on supplies, Pop-Tarts being a food that requires no refrigeration or heating and that kids are willing to eat.
Such adjustments must be made as close to real time as possible in order to gain any advantage. If reports are coming at a frequency of every week, the data is too aggregated and the pattern gets lost.
On the other hand, data might not be aggregated enough to capture a relevant pattern. For example, the sales trend of a particular product might be heading steadily downward, signalling that an item is losing popularity. Noticing this, purchasing agents might decide not to restock that item.
Better yet, they can research why the item is becoming unpopular: Perhaps there is a newer model out. Or a slick new competitor. Or maybe the company has not been marketing in the regions where its products are most needed. Nobody will know to do this research, however, unless the overall trend is recognized.
Accessible reports can also streamline warehouse operations themselves. A good example is measuring order velocity to ascertain which items could benefit from forward staging. Forward staging refers to having a set amount of stock in a forward location in your warehouse, close to where packing and shipping will occur. This helps minimize trips to more remote locations in the warehouse, as well as the distance pickers have to travel to get an item.
Forward staging makes sense for popular items with high velocity and no unusual storage requirements. Having the right reports, then, can help optimize picking and packing operations, which in turn saves costs, reduces errors, and increases customer satisfaction.
Then again, the areas where time and money can be saved are not so obvious. Again, the patterns need to be extracted from the data in a timely manner to make the appropriate adjustments. Some examples include:
Finally, there are always “curveball” questions that come up in the course of business. Not knowing the answer is less of a big deal on the warehouse floor, or at your desk. Not knowing is a much bigger deal in the boardroom, especially when you are looked upon as the person who supposedly has the answers at your fingertips.
For examples, suppose you are presenting a typical logistics and warehouse report. Suddenly, you are asked one of the following. Would you be able to bring up the appropriate report to find out the answer?
Naturally, being able to do these things requires a mix of standardized and customized reports. The best software solutions have a number of building blocks that allow users to build their own reports if needed, or use well-established templates.
While older ERP systems could take days to extract, transfer, load, and process all of this data, newer systems can query the data in real time, even as transactions are taking place. This has ushered in a new era of Big Data business decisions, especially where logistics are concerned. And everyone wants answers at their fingertips.
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