Abstract
Tactical S&OP process can be visualized like in Fig. 3.18. In this big chapter, we bring to you a lot use cases which can help in your maturity assessment, design, and implementation.
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Notes
- 1.
The square root of MSE is an approximation for standard deviation. This is based on the assumption that forecast errors are normally distributed with the mean of zero. The probability determines the z value. For example, z takes on the value ~1.64 for 95% probability. Therefore, the 95% prediction interval would be calculated by multiplying 1.64 and √MSE (calculated using the test or holdout dataset).
- 2.
The graph was created in Excel using a two-step process. Step 1: using functions RAND and NORM.INV to simulate forecast values for various random probabilities. Step 2: using PERCENTILE function, calculate the lower and upper range for a given prediction interval (say, range between 2.5th percentile and 97.5th percentile gives the lower and upper bounds for 95% prediction interval).
- 3.
For the benefit of the heuristic method, and given that in-house production is the preferred sourcing option, quotas were maintained in master data in such a way that 100% was allocated to in-house production.
- 4.
Please note that remaining steady does not mean low variability. It just means the behavior of these factors do not change significantly over time.
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Kepczynski, R., Ghita, A., Jandhyala, R., Sankaran, G., Boyle, A. (2019). Ways to Improve Tactical S&OP. In: Implementing Integrated Business Planning. Management for Professionals. Springer, Cham. https://doi.org/10.1007/978-3-319-90095-7_5
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