The Spreadsheet Was Supposed to Be Temporary

Consider an illustrative example. Four employees each spend 30 minutes a day assembling and checking operational information. Across 250 working days, that adds up to 500 hours a year.

Those hours represent capacity the business is already paying for. Recovering them could create more time for planning, supplier follow up, or resolving exceptions. Financial savings depend on how the business uses that capacity.

We also examine what happens while the information is being assembled.

A material shortage may become visible after a schedule has been issued. A supervisor may assign labor against an outdated workload. An inventory planner may place an order before discovering stock at another location.

A useful business case accounts for both the effort required to maintain the workaround and the consequences of making decisions through it.

AI is changing what we can economically build

Traditional ERP and warehouse management system implementations have often required substantial investment. Licenses are one part of that cost. Configuration, data migration, integrations, training, and ongoing support all contribute to the commitment.[Source]

Custom software historically carried its own development burden. Even a focused application required people to define requirements, write code, connect systems, test behavior, and maintain the result.

For a growing business with a specific operational gap, the expected benefit could struggle to justify that investment.

AI is changing that calculation.

We combine operational understanding with AI assisted development to build focused tools around the decisions a business needs to improve. AI can help with coding, test creation, documentation, and early versions that teams can review.

Used effectively, this approach can reduce development effort and make custom software considerably more affordable. [Source]