Most enterprise-level businesses have their own analytics solution to better leverage the glut of supply chain-related data available in the modern marketplace. Many smaller businesses, however, just can’t justify either the expense of setting up in-house analytics capabilities or the overhead of maintaining them. Unfortunately, as a result, many of these companies choose to forego analytics entirely, making vital decisions strictly from raw data without the benefit of the insights available to their larger competitors.
Companies can be missing out on important information. Not only are full analytics capable of exceptional value when it comes to company operations, but with managed services it is also now within the reach of companies of almost any size.
Why Are Analytics So Useful?
The number of factors that affect forecasting of any type is dizzying. Proper, well-tested analytics algorithms are the only effective way to wade through all of those factors and come out the other side with something even remotely accurate. Analytics can combine internal factors and external factors, including the actions of competitors, and weigh them appropriately for the desired type of forecast.
Anticipate Commodity Price Fluctuations
For anyone involved in manufacturing, the normal fluctuations in the prices of commodities can make things tricky. It’s necessary to either make most decisions on a worst-case scenario basis or to gamble on favorable prices and dive in headfirst. Through macroeconomic modeling, however, those price fluctuations become much more predictable, which opens up a third alternative: buying with confidence.
Use Dynamic Pricing
Most manufacturers experience annual price changes on many of their components. The price differential on any single component is seldom large, but collectively the difference can be significant. Unfortunately, there is hardly any uniformity to how the prices change. The modeling capabilities provided by today’s analytics can analyze past changes and predict future ones, which in turns allows for more dynamic pricing of finished goods. This means product pricing that takes component prices into account.
Use the Full Power of Analytical Modeling
All of the above features are powerful tools in the hands of any business, but the real power of analytics is its ability to accurately model complex systems quickly. In addition to running forecasting and pricing models focused on predicting specific future conditions, it is also possible to play “what if” and look at the likely effects of any change, large or small, on a business. This includes everything from the start of the supply chain, to rolling product out the door, to marketing and sales, to maintenance and customer retention.
Breaking Down Walls With Managed Services
The barrier to entry for full-fledged, in-house analytics is pretty high. So high, in fact, that it is generally only an option for the largest of companies.
By opting for analytics as a managed service, though, smaller companies can get access to the specialized skills necessary to pull accurate insights from large amounts of data without having to redesign the corporate structure or break the bank. Supply chain analytics as a managed service is a great way to get enterprise-level answers without an enterprise-level budget.