How Big Data is Improving Demand Forecasting Among SMBs

Small and medium-sized businesses (SMBs) are always falling victim to market pressures, but big data processing is transforming the way these companies deal with these pressures. Predicting demand is never easy, especially when it’s done in the traditional manner. However, a few recent breakthroughs in the field of statistics are helping to dramatically alter the way that smaller firms figure out how much demand there is for a particular product.

It’s also helping business leaders identify new markets. Companies that develop and stabilize their supply chains are the ones that stay the most competitive in these markets, so adoption of this new technology remains quite high.

Finding Out What Customers Want Before they Even Know

Patents on machine learning algorithms held by online retailers have made it difficult for SMBs to take advantage of this kind of technology. Larger firms, however, have traditionally licensed technology tools that crunch numbers collected from consumer buying patterns to theoretically predict what individuals want before they place an order.

In one of the most extreme examples, an online retailer tracked long-term users of their service and announced that they were capable of shipping products that had not yet been ordered. Logistics optimization is difficult, because statisticians have no way of knowing when a customer might place an order. Tracking demand patterns by sorting binary search trees allowed the retailer to figure out when consumers would order something and take advantage of the best possible shipping conditions.

A combination of expiring patents and releases of open-source tools are putting this kind of demand forecasting technology in the hands of smaller IT departments. While this particular example is rather extreme, it does serve to illustrate how these solutions can be applied to a problem.

Technologists are now pointing them at the field of business analytics.

Using Big Data to Measure Inventory Turnover

No other metric has received as much attention in recent months as inventory turnover. It measures how fast a particular product supply gets sold off before there’s a need to replenish it. The faster inventory gets turned over, the more inventory that’s needed per fiscal quarter.

Most commonly, Total Annual Sales ÷ Inventory is the formula used by computer scientists who code scripts designed to calculate inventory turnover. While this might seem like an exercise in simple business mathematics, information collected from retailers on a large scale is starting to challenge assumptions about this metric.

Statistical data illustrates that a high inventory turnover rate isn’t necessarily a sign of good business performance. If an item was turned over consistently, then it often means lost business as a result of waiting for new stock to come in. Several major firms realized this trend as a result of analyzing big data, and they’re now implementing new IoT-based solutions to prevent these problems in the future.

Demand Forecasting in a Connected World

A warehouse filled with products isn’t the easiest thing to keep an eye on. Strap a transponder on every package and things get much simpler. Inventory that can track itself takes all the guesswork out of maintaining this kind of physical plant.

Real-time product tracking can keep an eye on goods through the manufacturing, distribution, retail, consumption and even disposal or recycling phases. Smarter supply chains are usually associated with QA alerts, but they’re also an excellent way to collect data that can be fed into a demand forecasting program.

Once a firm has sufficient information about when consumers order their goods and how they use them, they can start to paint a better picture about how demands can shift in the near future.

Supply Chains in the Goldilocks Zone

Planners would love to be able to get goods to the end destination at the exact moment of demand. Since supply chains can’t teleport goods to consumers who orders them, statisticians have come up with the concept of the Goldilocks Zone.

Knowing exactly where to send just enough of a good to meet future demand in time is referred to by this unusual label, because the conditions have to be just right like the temperature of porridge in an old fairy tale. Managing supply chain through a combination of IoT fixes and inventory turnover algorithms will help move companies into this zone by helping them to better forecast consumer demands in the near future.