Some time ago I was on a call with IBF board members discussing situations where social media data signals rapid changes in demand for a product. These data might include some favorable online reviews of products or a celebrity mentioning a certain product, leading to a rise in demand. These are great “predictive” downstream demand signals, but since they cause very rapid spikes in demand, current forecasting processes could not incorporate the information quickly enough to act upon it. I blurted out “We need to start thinking about Quick Response Forecasting techniques”, making up the term on the spot.
Since then, Quick Response Forecasting (QRF) has gained a lot of traction among forward-thinking Demand Planners and Forecasters, and I recently wrote a detailed piece on it in the Journal Of Business Forecasting. Now we are at the point where demand planning organziations can identify very real applications for QRF in their own companies, and develop a framework to turn advance knowledge of changes into meeting spikes in demand with sufficient supply.
QRF leverages predictive analytics, social media information, and other Big Data.
What Is Quick Response Forecasting?
Quick Response Forecasting is updating forecasts in line with ‘real’ and rapid changes in demand, both during and between planning cycles. Data sources can be POS data or unstructured data like social media comments.
Quick Response Forecasting Solves The Availability Issue In Case of Spikes In Demand
A contact of mine works for a company that sells nail polish. Lady Gaga, at one of her concerts, wore a unique color of nail polish that his company sells. Shortly thereafter, social media lit up and sales of this item went through the roof. The company and its supply chain partners ran out of the product, as well as a key ingredient that went into making it. The company was not prepared to take full advantage of the rapid change of demand for this nail polish and missed out on a significant revenue opportunity. Had QRF been employed by the company, they would have been able to react quickly enough to ensure enough inventory was available.
Quick Response Forecasting Makes Sense Of Big Data
QRF leverages predictive analytics, social media information, and other Big Data. This relates to the explosion in digital data and the enormous amount of information available about customers and product users on the World Wide Web. As data get bigger, companies are looking for techniques and methods to both find and incorporate a few key demand signals among Big Data’s noisy information deluge.
QRF is a way to maximize revenue from rapidly emerging opportunities that are happening right now, and opportunities that are likely to develop in the very near future.
Quick Response Forecasting Supports Short-Cycle Planning
Like the nail-polish demand spike mentioned above, supply chains often cannot act quickly enough to take full advantage of the opportunity that the demand signal offers. More frequent forecasting has the potential to increase forecast accuracy in terms of identifying rapid changes in demand but supply chain responsiveness may be too sluggish to take full advantage of it. For example, manufacturing managers might complain about getting whipsawed by demand forecasts that change rapidly, despite their increased accuracy.
One quote I often use with regard to planning responsiveness came from a manager who ran the S&OP process at a high-tech company. Generally, these types of companies operate ‘responsive’ rather than “efficient” supply chains. Responsive supply chains handle high-margin, high-value products.
Their major goals are less about minimizing operating costs and inventories, and more about maximizing inventory availability at the point-of sale/consumption in order to capture potential upside revenue. One thing QFR is not, is supply chain optimization.
How QFR Can Work In Practice
A typical S&OP process is a routine planning process that would be too disrupted by having to incorporate QRF, so it is not a good candidate process. QRF is needed to support teams that are specially put in place, on an ad hoc basis, to manage significant event-based and substantial demand changes like natural disasters, celebrity endorsements etc. These teams should be cross-functional and be supplemental to the S&OP process. The teams need to be quickly assembled once an on-going QRF forecasting organization detects that a demand spike or significant demand change has occurred, or is likely to occur.
Once the team is put in place, QRF forecasts for the event need to be continually provided to the quick response supply team. If forecasters ask managers whether they need QRF today, they will likely say no. They don’t want their operations to be whipsawed by frequently changing forecasts. This is why a separate and special quick-response supply process will be needed to handle each event.
If you are on the lookout for an organization to partner with to develop QRF and supply response teams, your sales organization is the best bet. The teams are focused on going after significant revenue opportunities, for which current processes are too slow to take advantage of. If you can identify highly lucrative revenue opportunities, Sales will jump at the chance to exploit it. A supply response team will be tasked with taking full advantage of an opportunity, in terms of squeezing as much revenue from it as possible.
Bottom Line: We All Need QRF To Maximize Big Data Opportunities
In short, QRF is a way to maximize revenue from rapidly emerging opportunities that are happening right now, and opportunities that are likely to develop in the very near future. It incorporates the ability to forecast unstructured data like Facebook comments or online reviews, and requires ta specialist supply team that sites apart from the standard S&OP process to respond quickly, switching supply up or down as required. In the age of Big Data where technology promises to deliver greater insight and greater revenue opportunities, QRF is exactly what we need to make it a practical reality.