Reducing Supply Chain Fines Through Customer Profiling and Allocation Optimization
Motivation
In today’s world, as we optimize supply chain throughout the network, one thing that stands out is the large return logistics that cost companies several million $$ every year. This returns logistics has a enormous cost to any company and it would make a substantial compelling case to reduce the returns in the first place (first option) and then try to optimize the reverse logistics. This will not only help you reduce returns but also help in minimizing the credits that need to be provided. For one large customer, we saw about $ 50 Million in returns as they worked through their retailer. The reasons were several and are listed below
• Few SKU’s getting returned consistently.
• Particular order/lot sizes for customers
• High priority orders have high return rate
• Orders with tight SLA’s
• Damages wrt carriers, distribution center equipment/labor etc
• Problems at distribution center’s where orders are not correctly fulfilled (shorts, excess etc)
In one particular case we saw that Customer merchandizers were not planning well and pushing the supplier to rush the order which not only increased costs due to order expediting but also increased reverse logistics costs. In this particular case the supplier was able to negotiate better with the customer due to insights derived from the IntelliEngine
Solution
To help companies reduce the returns, DeepAutomate IntelliEngine analyses the causes of returns, visually lays out the various causes of returns per customer/geography/SKU/Order size etc hence helping your customer operations teams to deep dive into data and gain insights into issues. This can be taken one step further (optional) by plugging the IntelliEngine into your Order Management System (OMS) which lays out a risk factor for orders as they come in real time and need to be allocated which will help you predict if an order is going to be problematic in advance.
The IntelliEngine plugs into your ERP or OMS/Returns database and builds models on order returns and their likely causes. It analyses the returns on various parameters based on SKU, Customer, Geography, Distribution center, Carriers, Order size etc and builds order profiles. The data can be sliced and diced into various views ( SKU vs Customer for example) helping your teams to be on issues that really matter.
Value Realization:
Avoiding financial losses:
When companies fail to deliver orders on time and in full, they face fines and charges that can lead to significant financial losses. By reducing the on-time in full (OTIF) fines and charges, companies can avoid these financial losses and improve their bottom line. We noticed one CPG company with about $20 Billion revenue paying fines in excess of $50 Million per year to a top tir.
Improving customer satisfaction and reputation:
Getting your orders First Time Right (FTR) will compel your customers to have you as a supplier of choice, especially as they optimize their own supply chains and want to keep supplier returns at a minimum to reduce handling costs at their end
Staying competitive:
With far lesser returns to handle, your supply chain gets it right the first time and you can deploy the additional savings into further optimizing your supply chain which will fuel the growth engine for your company
Improving supply chain efficiency:
With a reduction in returns, both your and your customer supply chain’s efficiencies go up as there are warehouse and transportation productivity increases.