During the pandemic, the rebalancing of the significance of each demand channel (e-commerce, brick-and-mortar, etc.) stressed supply chains all over the globe and companies learned they need a viable crisis-resilient inventory optimization plan to adjust cash and execute plans quickly for on-time delivery.
Setting effective management of inventory can mean the difference between profit and loss.
On one hand, lack of inventory leads to out-of-stocks, which in the short-term result in lost margins, and on the long term jeopardize customer’s loyalty.
On the other hand, excessive inventory increases holding, space, and spoilage costs, and intensifies the complexity of storage and picking operations. Moreover, there is an increasing pressure towards inventory policies, to cut costs in order to increase competitivity.
In several companies, the process is still highly manual, focused on Excel files and supported solely by empirical knowledge, lacking advanced forecasting methods and inventory management policies that capture the nuances of the value chain, from the supply side (e.g., lead time, supplier service level, ordering costs) to the demand side (e.g., sales volatility, target service level to end customer).
But how do you meet customer demand without keeping excess stock? In short, by developing a data-driven planning process that combines advanced analytical models with managerial insights.
To successfully deploy such a transformational change in the inventory management process, four structural activities are recommended:
The first step is to assess the current situation and analyze the KPIs. By mapping the operational flows, you’ll be able to identify opportunities for improvements in the core methodologies, and, at the same time, quantify the benefits.
Don’t forget that, to ensure that strategic targets are met, all departments should use the same data sources and analytical models. For instance, if strategy planning and inventory managers use different data sources, have their own way of doing forecasts, or make incoherent assumptions of consumer behavior, planning operational decisions to meet strategic targets will eventually (and most certainly) fail.
After the diagnose, it’s time to build a solid analytic model to support demand forecasting, replenishment, purchasing, and lateral transshipment decisions. This model will allow you to test new inventory policies in a risk-free environment.
With the simulation of a newly optimized inventory management policy, you will be aware of the potential savings and decide which is the best flow for each product. Different products can have different optimal configurations, depending on a variety of attributes, such as supplier’s reliability, promotional activity, and sales speed and stability.
As several case studies showed us, it is possible to decrease operational, inventory, and commercial costs by optimizing stock at the SKU level.
Once you define the suitable strategy for each product, you will need an integrated inventory planning tool to provide granular forecasts and optimized recommendations for inventory needs.
Decision support tools to manage forecasts, replenishment and procurement significantly improve inventory management results.
There are several ways supply chain teams can incorporate state-of-the-art methodologies within their processes/systems:
Defining what KPIs to measure is a crucial step on the referred aspect, as it must cover the following angles:
As nowadays supply chains are constantly evolving, you must be prepared to keep your inventory policies constantly optimized.
A monitoring culture will ensure stock replenishment accuracy and automatically highlight key areas for improvement.
It is also crucial to integrate portfolio revision and profitability/trend analysis with inventory management processes, as procurement/inventory costs place a relevant part on the total cost of a given product. Thus, you will be able to reduce time to market for disruptive inventory management changes, accelerating positive impacts on supply chain operations
By: José Queiroga
