At some point, nearly every software infrastructure experiences the law of diminishing returns, as it strives to meet customer-service levels, achieve on-time and in-full delivery, and strengthen both inventory and overall cost-performance objectives.

The lack of visibility and control is thwarting organizations’ ability to make effective decisions and improve target metrics. Companies are looking to overcome this deficit with the help of new techniques and advanced technologies such as artificial intelligence.

Before a business can make a shift in either process or technology, however, it must first must understand the limitations of its current infrastructure. Failure to make the proper determination can result in a significant investment of time and cost, yet still leave the company incapable of delivering the expected productivity or outcomes.

An easy way to get started is to determine the opportunity costs of any of these common variability issues:

• How much are in-store and in-stock issues eroding revenue?
• Is inventory planned at higher levels than needed, due to issues around mix, timing, and volumes?
• Are warehouse and DC capacity being consumed by the wrong inventory mix?
• What percentage of carrier appointments and slots are consumed by late shipments consisting of inventory based on stale demand data?
• Have we been forced to cut cases due to demand variability, long lead times needed to react to order variance, and the inability to schedule appointments for addressing variance in shipment demand?

Optimizing inventory and achieving customer-service levels at a minimum total cost from an end-to-end supply-chain perspective will maximize the value of each participant within the network. Yet research indicates that very few companies have been able to adequately resolve variability and inventory issues within their extended supply chains.

Inventory costs are largely influenced by customer demand and the lead times required to move material between each stage of a supply-chain network. Uncertainty is generally characterized by demand and lead-time variability, both of which result in increased inventory expenditures.

As variability increases, there is a need to increase the amount of safety stock in order to achieve desired customer-service levels. Safety stock acts a buffer for multiple sources of uncertainty in a supply chain. After-all, variability adds cost to the network due to discrepancies in demand, lead time, transportation, order processing, and purchasing.

Of particular note is informational lead time, the period required for information to move between network participants. It’s critical for planning, meaning that the longer it takes to communicate between supply-chain participants, the more inventory is needed. Informational lead-time variability further pushes the need for more inventory, so any reduction in that metric has the potential to improve profit margins with no negative impact on customer-service levels.

To tackle informational lead times, many organizations are deploying a control tower on top of the network platform, to drive a real-time demand signal through all trading partners. In many cases, this approach drops the informational lead time to its theoretical minimum, or near-real time. The network solution also solves the typical lack of trust between trading partners that can result in excessive delays and variability.

Done properly, a platform play can generate a step-function gain in performance, and move companies away from the law of diminishing returns. A platform investment at the network level almost always generates a reduction in the inventory required to meet service-level objectives, along with associated improvements in waste, logistics, distribution, and warehousing.

As enterprises continue to roll out network platforms across multiple phases, performance metrics continue to improve. Moreover, companies can keep on evolving the business model as the market continues to shift.

Additional benefits come in the form of greater visibility and the ability to execute decisions jointly on a real-time basis. With the establishment of a collaborative environment, companies can move on to other value-added areas of the business.

When investigating advanced technologies such as artificial intelligence and automation, make sure to understand the sensitivities of the chosen model in terms of activity versus productivity. With a near-endless number of available options at hand, it’s best to understand which variables will have the greatest impact on your current situation, as well as how changing one might affect others, both positively and negatively.

Put simply, before embarking on the great adventure offered by today’s technologies, be sure to understand the underlying business goals before making any big budget commitments. In doing so, you’ll save yourself much pain and suffering, while gaining the ability to structure an agile rollout focused on business value, and establish targeted metrics based on business outcomes.

Joe Bellini is chief operating officer at One Network Enterprises, provider of an AI-enabled business network platform.