At this level the focus and concern is almost exclusively on the Financial aspects of the business, and Operations tends to invariably end up being the whipping boy because of Production or Inventory issues, and sometimes also because their numbers don’t always stack up against the voodoo that the Finance Dept does with their own numbers.

I think that the review at this level should be one lead by Finance, with representation from Operations, but that the actual S&OP process should be focused at a lower level to align Sales & Marketing expectations with the rest of the Supply Chain.

True, in order for this process to be meaningful you need to gather a lot of data pertaining to each and every SKU in your stable, but I would have presumed it to be a relatively simple task to extract this information from whatever ERP system you’re using.

I also presume that “massaging” data as you put it refers to nothing more than formatting it into a readable state? If that’s all you’re going to do to it, it seems like a terrible waste of a potential source of invaluable information that your organisation could use to its great benefit.

Essentially, the S&OP process should be the process by which you align your Operations with Sales, so that each knows what the other is up to and expecting from each other. It is the meeting place of your Production/Procurement/Inventory Management spheres with the Sales & Marketing domain. In a nutshell, it is where you measure the accuracy of the expectations Sales & Marketing had for your products vs what they actually achieved, and report on where your Supply Chain fell over and why and what’s been done to fix it.

I have worked for numerous organisations where I’ve prepared data for, and run, such meetings. I have found that the chief requirement is to work smarter rather than harder, and to manage by exception rather than trying to manage everything.

Drop me a line if I can be of assistance.

Regards

4) Better cash flow management. Building too much inventory can cause major issues with the company’s cash flow but by integrating financial data into S&OP the team was able to see the impact of the decision and adjust the inventory build to meet both demand and cash flow needs.

My read of that is that they are cutting into SS, therefore cutting service level / fill rate to meet cash flow needs. The real S&OP decision, I think, is to understand the drop in fill rate that will likely result from the reduced inventory, an agree that the increased cash flow needs warrant that.

On the other hand, I know of several major electronics companies who have implemented the CPFR principles: namely capture forecast (their own to suppliers or from their major customers), review changes from the previous cycle, firm up on a common view of the demand.

Then, if getting demand from customers, they test/simulate the capability of their supply chain to satisfy that demand, resulting in a committment back to their customers (which could result in an adjustment to the demand the customer plans to place on them).

When communicating with suppliers, the objective is to obtain a committed supply chain back from the supplier, which is then used to feed committments back up the chain.

The point is, not only CAN it be done, but that it IS being done with EXCELLENT results. However, the process has been simplified from formal CPFR into something that can be implemented and used.

Quantifying the maximum achievable forecast accuracy is a difficult problem, and I don’t think there is a perfect answer. We can concoct examples, such as forecasting Heads or Tails in the toss of a fair coin, in which we can determine the limit of forecast accuracy. We can do this because we understand what governs the behavior being forecast (called the data generation process (DGP)), and our accuracy is limited only by the randomness in the tossing of the fair coin. In real life situations we usually don’t know the DGP, the DGP may change over time, and we don’t know the amount of randomness in the behavior.

There are some good articles on the general topic of forecastability and forecast performance measurement. I discuss these periodically on my blog (http://blogs.sas.com/forecasting) and provide links to some specific articles that may be of use. There is also an article “Setting accuracy targets for short-term judgemental sales forecasting” by Bunn and Taylor in the International Journal of Forecasting 17 (2001) 159-169.

Since we really don’t know what is the best accuracy that is possible to achieve, I prefer to set performance targets with respect to what is the worst we should be able to achieve. Thus, I would set the goal “Forecast no worse than a naive model” (where a naive model is something cheap and easy to implement, such as a random walk or moving average).

Since we don’t know in advance what accuracy the naive model will achieve, we don’t set a specific numerical target. Instead, over time, we evaluate our forecasting process accuracy vs. the accuracy the naive model achieved. If our process is doing WORSE than the naive model, obviously something is going very wrong!