Great information in the news this week – I am sure everyone reads the news with a specific focus on how to improve forecasting and S&OP, right? Anyway – to the point.
Yields on 10 year treasuries rose 11 basis points. Claims for jobless benefits fell to a 5 year low. US home sales for the month of Dec. fell 7.3%, with a 1.3% increase in average sales price, but for year over year sales actually increased 8.8%, with a 14% rise in average sales price.
From an S&OP and demand planning/forecasting point of view, this information raises some interesting questions about causal impact and associated lead times.
What does all of this information imply for demand for your products 3 months from now, 6 months from now? For future pricing of your raw materials? Do the implications change per geography? Per season? Per product line or per customer class?
Does the fact that banks are accelerating repayment of bailout funds mean anything regarding interest rates? And do interest rates impact your procurement policies or demand for your products?
And what is the lead time associated with all of these potentially causal and leading indicators?
Most importantly, does your current process and technology platform allow you to study the impacts, understand the causality, plan for the impact with correct lead time? Are you having these conversations?
Our clients are talking about the implications of increased housing values on consumer spending and in what time frame. They are creating scenarios around different inflation trajectories, different employment rate scenarios and doing this within the context of different weather projections by geographic region. Are you equipped to have similar conversations on a weekly, monthly quarterly basis?
CPG and B2B manufacturing companies use leading indicators / causal factors like weather (temp. and precip. indexes), unemployment, birth rates, housing starts, and Nymex commodity spot prices to improve their forecasting and do so for cascading time frames. They are included in as additional inputs into forecasting process and used concurrently with the traditional historical inputs.
We recently had a good conversation with Amber Sally from Gartner. Among the topics discussed were her views on competitive differentiators in demand planning and forecasting – one was the ability to employ causal factors into your process.
Are you going to figure out how to corral the power of this competitive differentiator, or are you going to passively let your competition outflank you just as you are passively allowing external forces to impact your business without understanding the details of the how, why, where, and how much?