Use improved demand forecasting to drive business impact
Data Analytics can provide logistics companies - actionable insights into various value drivers and the distribution complexities, resulting in const optimization and reduction in the system inefficiencies. NeenOpal's Supply Chain solutions help organizations address different challenges/logistic drivers like freight mix, logistics spend, time to market, etc., across their value chain.
Heuristic based algorithms to optimize your company’s supply chain & logistics network along with building capabilities to optimize routes in real time. Our analytical solutions also optimize other touch points in the distribution network such as warehouses and reducing demurrage.
Customer Loyalty Management
Optimize internal resources focused on preventing customer churn by taking into account Customer revenue, profitability and the likelihood to churn. The churn probability incorporates latest dealings with the customer by implementing a machine learning based predictive model.
Operational Capacity Planning
Improve asset and manpower utilization through our tools to accurately forecast long term demands and incorporate them in fleet sizing, etc. Estimate seasonality, growth trend and cyclic effects separately in each one of the different industries to improve forecast of overall demand.
Warehouse Management Solutions
Robust WMS system help capture relevant data increasing supply chain visibility and improve information accuracy. We provide a single point solution for multiple needs such as barcoding, processing orders, handling returns, etc. including performance ranking of different WH/DCs as well as fleets/drivers.
Risk Management Analytics
Our risk framework quantifies the 4-dimensional uncertainty - multiple vendors, products, countries and modes of transportation - in current global supply chain networks to help companies understand and develop a mitigation plan to hedge against the uncertainty.
Service Level Agreement Analytics
Our algorithms are able to predict, by simulating millions of possible theoretical combinations, the amount of stock to be kept at each level of the network to meet the required SLA. The cost of not meeting the service level is taken into account while recommending the optimum stocks and replenishment strategies.
The forecasting model is designed to handle exceptional events due to missed sales, promotions, etc. as well as incorporate latest sales data through machine learning based forecasting tool. A wide range of contextual parameters which can affect demand are tested which help to improve accuracy of demand forecasts to range of 90-95%
Inventory levels are defined at all levels of supply chain – warehouse, distribution centres and stores, to strike at the core of the supply chain trinity – 1) reduced logistics costs, 2) less stock outs leading to improved sales and 3) lower inventory holding costs.
Large Organizations as well as Small & Medium Enterprises across Asia and Europe