Additionally, did the store have enough inventory to satisfy the demand for this product? If there was more inventory, could the store have sold more, e.g., 120 units instead of the original 100? Was this product or any related product promoted and how did the promotion affect sales?
We tend to observe the final number—a sale of 100 units. Yet, actual demand is very different in many cases. The ultimate goal is to find actual consumer demand. This is what we need for forecasting and planning purposes.
But calculating the effect of any of these factors is a challenging problem. Most businesses are familiar with:
- Forecasting systems that calculate the effect of seasonality;
- Price optimization systems that calculate the price elasticity of demand;
- Systems that calculate the effect of weather-related changes in buying habits; or
- Promotional management systems.
In every case, it takes a sophisticated system to calculate the effect of one component of a consumer demand.
However, have you ever thought about whether these individual systems take each other’s results into account? Should they?
If your system manages prices without considering inventory management, logistics or events it does not optimize your prices. Similarly, if your system calculates demand forecasts without considering returns, promotions and other related factors, then it’s not much of a forecast, is it?
To calculate the combined effect of all influencing factors, requires a new approach called business-specific predictive analytics.
Understand the use of business-specific predictive analytics
Tailored specifically for the supply chain environment, business-specific predictive analytics can cover a wide variety of unplanned situations that may influence future consumer demand. As the result, it produces accurate forecasts with little involvement on the part of business analysts. With business-specific predictive analytics, the accuracy of future demand forecasts grows dramatically.
Predictive analytics proves to be increasingly useful across multiple industries including e-commerce; marketing and social media; banking; healthcare; and entertainment (see illustration on page 32). Some of the ways that predictive analytics is used include cases in which:
- Social marketing companies predict shopping habits and try to figure out the consumer’s next move;
- CFO’s use predictive analytics today to minimize risks and keep business agile in economically uncertain times;
- Online content providers and advertisers utilize predictive analytics to push relevant content to their users increasing conversion rate to record heights;
- The medical and healthcare industry run the data they’ve collected through predictive analytics engines to foresee healthcare epidemics; and
- Synoptic meteorologists collaborate with research centers and universities to build predictive analytics models to identify upcoming hurricanes, earthquakes and other natural disasters.
Supply chain improvements in forecast accuracy
There will always be some organizations that will wait a bit longer and see how the predictive analytics venture plays out. Sure, there are late adapters that prefer to wait for new technology to become industry standard. Just be aware, that while you wait, industry research conducted on almost 90 retail organizations across multiple industries shows stable improvement in forecast accuracy by average of 23 percent; 28 percent in inventory cost reduction; an increase in sales by 12 percent; and overall increase in GMROII of over eight percent. The future of predictive analytics is here now—do you really want to play the adoption waiting game?