00:31 How is pricing currently approached from a Supply Chain perspective?
01:25 How well does the pricing work as a mechanism?
02:47 Is that the reason why pricing isn’t really taken into account?
04:11 Where do you start from for a pricing optimization initiative?
05:34 Are there any companies that are actually doing pricing optimization successfully now?
07:14 What is the basis of the dynamic pricing policy?
09:06 How can you build a forecast that works well with pricing?
11:11 Is this where a probabilistic approach fits in?
14:09 What are the technical requirements that a company needs in order to optimize their prices?
16:09 How does this differ from a more traditional S&OP approach?
17:34 How important is it to keep track of the competitors’ pricing?
19:21 Can you envisage a time when a company can set a pricing point knowing basically what profits they will get in return?
Pricing optimization is typically not considered as part of the Supply Chain Management (SCM) practice. Yet, pricing is a factor that strongly influences customer demand. Thus both production capacities and stock levels are highly dependent on prices, and must be jointly optimized.
One of the first things we’re taught in basic economics is that price is inherently linked to demand. There is no demand without price. However despite pricing being such an integral part of a demand forecast, until now the vast amount of variabilities meant that modelling the link between the two has been intrinsically difficult. Pricing has become the elephant in the room.
To compensate, many companies use micro optimizations of a moving average or even more simplistic excel models to set prices. Classic supply chain optimisation tools focus mainly on demand, with pricing being pretty much inexistant, creating an enormous blind spot in this vital area.
In this episode of LokadTV, we explore the concept of pricing optimization and learn how instead of pricing being just something you need to forecast it can, and should, be something you engineer in to your supply chain solutions.
Up until now one of the main challenges has been understanding the vast number of exterior factors that can affect sales. We discuss how well existing techniques work and understand how much trust can actually be placed in these models. In addition, we learn how the latest advances in machine learning mean that a company can keep track of the shear number of variables and why machines are so well equipped for this task, particularly when there is lots of noise.
Finally, we discuss the technical requirements needed to implement pricing optimization - what does a company actually need to put this in place? We also investigate how the latest advances in machine learning and big data mean that pricing optimization is now no longer an aspiration of the future, but a very real possibility for businesses today.