Challenge:

Forecast variability is one of the major concerns due to change in consumer pattern, O2O platform and challenges in the complex processes or the ways of working of an organization. So inventory replenishment is one of the key challenges to meet the demand.

Objective:

To optimize overall inventory and production across entire primary supply chain distribution network and reduce inventory-holding and distribution costs.

Findings:

My supply chain planning experience has taught me that companies are carrying far more inventory than they actually need to meet their service level targets. This is because supply chain planners are battling inventory on one or more fronts:

• They face a plenty of SKUs and optimizing inventory for each SKU is challenging
• They face a complex distribution network and optimizing inventory for each location is challenging
• They face significant demand variability and optimizing inventory for the right time is challenging

Analysis: Supply Chain Solutions as follows fall short of inventory optimization.

• They provide software and services that have nothing to do with inventory optimization, e.g. ERPs and warehouse management systems (WMS)
• They propose textbook formulas for Safety Stock setting or Economic Order Quantity calculations, with no optimization
• They suggest “best practices” for inventory planning such as simple ABC inventory segmentation.These traditional methods have massively different behavior due to many other important parameters that you didn’t consider in your classification.

Solutions:

How to achieve true inventory optimization?

True magic of inventory optimization lies in making finer and finer slices of data when segmenting. Aggregate planning just doesn’t cut it if you want to reduce overall inventory while maintaining service levels. Simplistic models can’t solve complex problems. To do that, you have to be able to plan at the SKU-Location- Service level. This level of granularity allows you to make the right balance of trade- offs between stock and service where it matters most rather than making sweeping aggregate assumptions. Thus getting the right inventory in the right place at the right time is key to meeting your service level targets and business goals.The more complex your business is, the more money you are leaving on the table when you rely on simple ABC inventory analysis. Optimizing inventory at the SKU- Location-Service level is critical for companies with large demand variability, large SKU portfolios or great distribution complexity.

Multi-echelon inventory optimization (MEIO) can navigate the data challenges supply chain planner face and automate the SKU-Location-Service tradeoffs to meet business goals.

MEIO optimization allows to strategically optimizing inventory by leveraging advanced analytics to balance inventory across different echelons (locations, bill-of- material levels, SLA’s, etc.). The ability to optimize against these fine slices of data is vital in minimizing your costs while maximizing service levels in the most optimal way.

Based on my experience I’ve categorized Inventory optimization per below 3 models1)

1. Simple Distribution network –Standard Location Netting (SAP)
2. Moderate Distribution Network-Aggregated planning
3. Complex distribution Network-Optimizer or Multi-Echelon Inventory Optimization.