With vast amounts of data being constantly pulled from multiple data sources it can be hard to accurately predict and optimize operations without Business Intelligence.

Data-First Approach to Inventory Management

Having the right inventory, the right volume, and at the right location is crucial for inventory analytics. With online retail sales showing no signs of slowing, the opportunity for business growth is monumental.

The challenge, however, is that without successful inventory management, it becomes difficult to evolve with the ecommerce industry and customer’s expectations around a multitude of things — not least of all product availability. By failing to have the stock to fulfill demand, you are at risk of missed sales. This can further lead to a damaged reputation and loss of future customers.

By achieving speed and accuracy, inventory management capabilities go beyond ensuring accurate inventory and automating key business processes, which was once considered to be a revolutionary development in ecommerce.

Today’s inventory control systems now also hold the key to powering business insights that can improve your ability to make data-driven decisions for increased productivity and profitability. SME’s inventory management solutions and capabilities provides competitive efficiency.

While balancing product availability against anticipated market demand and the ability to forecast future demand has historically been a difficult task, advanced inventory management systems leverage historical data and apply data analytics to process vast quantities of your past sales data and factoring in lead times and seasonality.

In this era of big data, an inventory system can also provide you with unparalleled insights into customer behavior, product performance, and channel performance, made possible even for large retailers with huge datasets. It’s time to put your big data to work in a big way.

Here are two case studies where inventory management saved SME's customer time and money. 

 

Solution Overview

Inventory Management for Supply Chain & Logistics

The primary challenge was the lack of consistent views across multiple data sources and lack of business user access to inventory data. The product managers had very little idea about product turnover rates and there was a large need for better production planning and performance metrics. The reports were also generated manually, and ad-hoc reports were created on-the-fly with minimal consistency or governance around queries/calculations. 

Modernizing the customer's BI efforts made inventory data available in real-time to all business users. Product managers now have a pulse on how much inventory is on-hand vs. in-transit, as well as backordered by supplier.

This effort resulted in:

24% reduction in holding costs

97% increase in data availability

5.4% faster Inventory Turns

Key Performance Indicators (KPIs):

  • Inventory Turnover
  • Average Inventory Volume of On hand, In Transit and On Order quantities
  • On-Time Deliveries
  • Freight & Holding Costs
  • Surplus/Shortage %

Datasets include information on product availability, sales demand, and vender reliability. 

Business questions addressed include:
  • How much inventory is required to meet demand while keeping stock levels to a minimum?
  • How to optimize the management of stock?
  • How to reduce the impact of product recalls?
  • How do we prevent stock-outs or surplus of inventory?

Inventory management solutions are now equipped to apply a level of AI to power insights such as those listed above.


Keep reading about this case study. 

 

Solution Overview

Inventory Management for Energy & Utility

The business users were having issues reporting and analyzing their meter inventory data out of their current SAP system. The managers did not have very good insight on current inventory levels. In addition, the SAP table and field structures were not intuitive or business friendly. 

The solution automated 100% of the inventory reporting allowing users to focus on data analytics instead of data gathering and data entry. We greatly reduced the number of queries to their SAP system by giving all of the shop managers across the company the same version of the data.

This effort resulted in a $4 million reduction from the customer’s balance sheets by rebalancing their inventory across storage locations and distribution centers.

Key Performance Indicators (KPIs):

  • Inventory Level by Operating Company
  • Turnover Ratios
  • Burn Rates
  • Purchase Order Forecasts

There was a lot of agile development with the end-users in order to define the complex business rules around storage locations, operating companies, and distribution centers.

 

Keep reading about this case study.