Just before the holiday break, dropped the very interesting results of a Gartner survey on the challenges to sales and operations planning in Consumer Goods. The top challenge was surprisingly down-to-earth. It bodes well in that it reveals a maturing of S&OP in the industry and an opportunity to make even more progress.
What are we talking about? The number one challenge facing Consumer Goods companies in their S&OP processes, according to the report, is connecting S&OP decisions to production scheduling and operational execution. In other words we are doing a better job at S&OP planning but we still struggle to translate the results into real life scheduling and execution!
Why is that? The trend over the last few years has been to deploy S&OP support tools and processes that aggregate and summarize the demand and supply matching process. This is done to help make the tradeoff decision making and negotiation manageable in the S&OP process. The theory is that aggregated data is “good enough”. The reality is that aggregated data may be good enough to make the decision on the tradeoff but not good enough to enable translation back down into operations.
Why do we care? Because if you can’t translate the S&OP decisions back into operational execution you end up creating a mess of reaction to the plan in the short run. Worse you create a culture of reacting to the changes in the short term and potentially miss out on the whole point of an S&OP process: Creating a predictable plan!
One the one hand you don’t want constrained data that is so detailed it is unwieldy and useless for making S&OP decisions. On the other you don’t want aggregated, unconstrained data that makes the results of S&OP decisions useless for planning and execution on the back end.
What’s the answer? The answer is to have a data hierarchy that allows you to translate or span planning inputs and outputs. In this way you can have input at one level in the hierarchy, S&OP decisions at another, and the outputs for execution at another while the system keeps everything in sync.
For example: You may collect collaborative demand forecasts from the channels at the product category level. You may make S&OP decisions at the product family, or channel level. You then want the output translated to SKU/source level to make sure the decision scenarios are feasible. Working at these different levels for different purposes is not impossible if you construct the planning hierarchy correctly from demand through supply. That’s what good supply chain planning systems do.
The S&OP topic is certainly ripe with opportunity in the New Year.
What do you think? Is this the year for S&OP in your organization? Email me at blog@logility.com.
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Logility Staff
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