A recent Vuealta webinar analysed the workings of contemporary supply chain intelligence and discussed practical lessons and supply chain strategies in the context of demand, inventory and performance management. The session was officially titled Analytics for Supply Chain: Demand, Inventory and Performance Management.
Speakers included Michael Detampel, Sr. Director of Supply Chain Solutions, Anaplan; Antony Lovell, VP Supply Chain Applications, Anaplan; and Professor Emel Aktas, Professor of Supply Chain Analytics, Cranfield University School of Management.
After covering a good deal of initial ground, which we have covered in this story here, the speakers moved on to discuss alternative safety stock methods e.g. DOS (Days of Sales) and how to get the best out of this approach.
As we understand it in the common parlance, Days of Sales is a different way of communicating core safety stock information. By using ‘Days Of Sales’ rather than ‘Quantity Stocked’ we can communicate across departments with a more holistic view to actual customer fulfillment.
Unity in units
This approach also helps compare working stocks with lead time, which is also most frequently expressed in days. Working in the same base of ‘units’ has obvious advantages and allows us to monitor the changes in demand (sales) and update the safety stock investment accordingly.
Inefficient capital allocation occurs comes when companies apply a DOS ‘rule of thumb’ to broad categories of products, rather than optimizing stocking levels for each item-location.
With agreement across all three speakers on this subject, the group moved on to look at after-sales/service part inventory optimization, where demand is typically highly intermittent (and rarely stable or normalized). So what should organizations think about in this area?
Citing specific use case instances, the group highlighted the fact that many customers are using Anaplan to plan service parts and/or manage intermittent demand. To do this, they use different analytics behind their models rather than standard inventory calculations.
Logical disaggregation advantage
Other key methods include forecasting at a higher level with respect to time, hierarchy or location (e.g. weeks or months, instead of days) and then using logical methods to disaggregate the forecast. Some customers also plan to create ‘baskets’ of service parts. As a real world example, car drivers typically purchase 4 replacement tires at one time, so stocking 1, 2 or 3 tires in a particular brand, size or style does not make sense, regardless of what the algorithm says.
This webinar also covered areas including Stock Keeping Unit (SKU) best practices and how to engineer and working business operation around those principles. The team also discussed working with Service Level Agreements (SLAs), looking at how to cope with the specific levels of availability and/or response times needed for every instance.
Forward planning fusion
Again looking to leverage core Anaplan functionalities and looking to showcase and examine the capabilities of the Vuealta Demand & Supply Chain Applications, this webinar concluded with some discussion of ideas relating to fusing statistical planning and sales opportunity planning?
The Anaplan platform, deployed and dovetailed with the highly-flexible Demand Planning Application from Vuealta provides a set of capabilities to integrate demand sensing, demand management, pricing and inventory planning models. It has interfaces that can do scenario planning of opportunities identified for the business. What all this means is that a good consensus planning process should consider input from the sales team – and this should be evaluated in parallel with the statistical forecast and used to justify overrides and adjustments.
As we continue to host these discussions and break down perception barriers that may have previously held some business decisions back, the use of platform-level planning and supply chain technologies to gain prime mover advantage will be key.