First and foremost thanks for the positive response that my last blog received. I really appreciated the feedback; one of them was not to write in Danish.
In my last blog I wrote about which department/division could benefit from a good statistical forecast and why it is important to have a holistic approach instead of a horizontal approach to forecasting. One of the departments was logistics or SCM, here I only see forecasting as one of the steps when the goal is to right size the inventory and thereby reduce the bullwhip effect.
Of course having a great process and clear identification of sales drivers like trade promotions or retailer campaigns helps to reduce some of the Houlihan Effect. However the true benefits come when you combine your great hierarchical Point of Sales (POS) forecasting (or as close to POS as you can get), with great inventory models that not only derive the best safety stock for one location at a time, but one that can take a holistic view of your mapped supply chain.
By holistic I am not only thinking of the physical structure of the supply chain split on different echelons in the network, with the ability to risk pooling. I am also taking into consideration an inventory model that takes a holistic view of the things that affect the performance of the supply chain. It can be simple things like:
- Taking into account different lot sizes in different echelons in the supply chain in order to reduce the Burbidge effect.
- Using the uncertainty of the expected demand and lead times between locations etc.
- Minimum order quantities or other capacity constraints.
- Aiming for an individual SKU service level, with a service type like fill rate.
While minimizing the total cost of the supply chain (ordering cost, holding cost and penalty cost.
These calculations are hard enough to carry out and take a reasonable amount of time if you are to do them manually for one small group of SKUs. In real life however, this has to be done for large groups and continuously updated whenever it is possible to place an order or master data is changed. So with all these steps and issues to take into consideration, it is no wonder many used rules of thumbs in the past. With today’s technologies and rapid research within this area, this process should be data driven. During the last 8 years where I have worked on optimizing supply chains by the use of analytics, I have also observed high levels in the ROI for these projects, due to:·
- Reduced inventory ► reduction in working capital·
- Reduction in out-of-stock situations and unsaleable products ► increased revenue
- Freeing resources from doing manual, heavy and recurrent work ► efficiency
- Reduced Bullwhip effect ► stable supply chain ► less handling costs
That’s why I think it spot on when Lora Cerere, states that ”Does Better Forecasting Improve Inventory? Why I Don't Think So Anymore.”
Therefore, when you look at your own supply chain you can ask yourself the following questions:
In which steps are we using analytics to raise the bar?
Which steps are data driven?
And finally, which steps can be further automated?
But I can not help to wonder if there are other processes that need to be taken into account? Any suggestions?