The Spreadsheet Was Supposed to Be Temporary
AI is changing the economics of custom software for the operational problems businesses have learned to work around.
By Streamliners Studio
Picture a production planner opening a spreadsheet before the first shift.
Orders come from the ERP. Material availability comes from another report. Machine capacity needs a conversation with the floor supervisor. A customer has changed a delivery date, and someone has emailed a revised priority list.
The planner brings everything together and decides what the plant should run next.
The spreadsheet began as a practical solution to a specific gap. Over time, more decisions moved into the file. More people became dependent on the person maintaining it.
Eventually, a temporary workaround became part of the operating model.
When we evaluate a workflow like this, we want to understand why the workaround is still necessary and whether the economics of replacing it have changed.
AI is giving us more ways to address that question.
The business is already paying for the workaround
We begin by examining the work required to keep the process running.
Who collects the information? How often does someone check it? What happens when an order changes? Which decisions depend on one person knowing something the system does not capture?
A spreadsheet can be an excellent place to explore a problem. Teams can change assumptions, test calculations, and learn which information matters.
The economics become different when the same manual work repeats every day.
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 A supervisor may assign labor against an outdated workload.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]
The amount saved depends on the application, the integrations, and the requirements. We assess those costs alongside security, training, maintenance, and support.
A focused planning tool can therefore have a much lower entry point than a broad ERP or WMS implementation. Its scope addresses the workflow that needs attention, while existing systems can continue serving the functions they already handle well.
That creates room to revisit useful tools that previously seemed too expensive to build.
Our work with By the Way Bakery
Our work with By the Way Bakery offers a concrete example.
As reported in The Wall Street Journal, we built a custom planning application using the bakery's spreadsheet data. For an order of 1,200 cakes, the system could calculate the eggs and powdered sugar required, while supporting ingredient, labor, and production planning.
The case illustrates the value of connecting an incoming order to the resources needed to fulfill it, a focused operational problem that growing businesses can recognize.
Read the original Wall Street Journal feature.
We start with the operating rules
Lower development costs make more projects possible. Operational understanding determines what we should build.
Return to the production planner.
Combining orders and inventory data would remove some manual work. Producing a useful schedule requires us to understand the plant's constraints.
Which machines can run each product? How do changeovers affect available capacity? Does material count as available if it is awaiting a quality release? Who can approve a priority change?
We work through those questions with the people responsible for the process.
The existing spreadsheet can provide valuable clues. A color code may indicate an exception. A hidden tab may hold a capacity assumption. A manual adjustment may compensate for a constraint the ERP does not represent.
We examine which rules reflect sound operating judgment and which habits the business should reconsider before carrying them into a new application.
Faster development makes that judgment more consequential. We still need to define the right process and test how the software behaves when conditions change.
We choose the first project around a decision
At Streamliners Studio, our starting point is a recurring decision with a clear user and a measurable consequence.
“Improve visibility” is too broad to guide a useful first release.
“Help planners identify material shortages before releasing tomorrow's schedule” gives us something specific to work with.
We can establish the information required, the systems involved, and the action the planner needs to take. We can also agree on how to measure progress.
That might mean less time preparing the schedule, fewer changes caused by unavailable materials, or faster resolution of planning exceptions.
We assess the available options before deciding to build. An existing feature, a configuration change, or an established product may provide the best answer.
Where custom development makes sense, we keep the initial scope clear and involve operations and IT throughout. We also establish who will maintain the operating rules, manage access, and support the application after launch. [Source]
That gives the business a practical way to evaluate the result and decide what should come next.
Revisit the assumptions behind the spreadsheet
We build software for manufacturing, distribution, supply chain, and logistics by connecting operational knowledge with AI assisted development.
The spreadsheet is a useful place to begin because it shows where people have already invested effort in making the operation work.
Some workarounds will remain sensible. Others may now have a stronger business case for a dedicated tool.
Our recommendation is to revisit the assumptions behind that decision.
How much work does the process require today? What does the business lose when information arrives late? What would a focused tool cost to build and operate now?
The spreadsheet may have been the right answer when it was created.
The economics of the next answer deserve a fresh look.
Learn more about how we work at streamliners.studio.
Picture a production planner opening a spreadsheet before the first shift.
Orders come from the ERP. Material availability comes from another report. Machine capacity needs a conversation with the floor supervisor. A customer has changed a delivery date, and someone has emailed a revised priority list.
The planner brings everything together and decides what the plant should run next.
The spreadsheet began as a practical solution to a specific gap. Over time, more decisions moved into the file. More people became dependent on the person maintaining it.
Eventually, a temporary workaround became part of the operating model.
When we evaluate a workflow like this, we want to understand why the workaround is still necessary and whether the economics of replacing it have changed.
AI is giving us more ways to address that question.
The business is already paying for the workaround
We begin by examining the work required to keep the process running.
Who collects the information? How often does someone check it? What happens when an order changes? Which decisions depend on one person knowing something the system does not capture?
A spreadsheet can be an excellent place to explore a problem. Teams can change assumptions, test calculations, and learn which information matters.
The economics become different when the same manual work repeats every day.
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]
The amount saved depends on the application, the integrations, and the requirements. We assess those costs alongside security, training, maintenance, and support.
A focused planning tool can therefore have a much lower entry point than a broad ERP or WMS implementation. Its scope addresses the workflow that needs attention, while existing systems can continue serving the functions they already handle well.
That creates room to revisit useful tools that previously seemed too expensive to build.
Our work with By the Way Bakery
Our work with By the Way Bakery offers a concrete example.
As reported in The Wall Street Journal, we built a custom planning application using the bakery's spreadsheet data. For an order of 1,200 cakes, the system could calculate the eggs and powdered sugar required, while supporting ingredient, labor, and production planning.
The case illustrates the value of connecting an incoming order to the resources needed to fulfill it, a focused operational problem that growing businesses can recognize.
Read the original Wall Street Journal feature.
We start with the operating rules
Lower development costs make more projects possible. Operational understanding determines what we should build.
Return to the production planner.
Combining orders and inventory data would remove some manual work. Producing a useful schedule requires us to understand the plant's constraints.
Which machines can run each product? How do changeovers affect available capacity? Does material count as available if it is awaiting a quality release? Who can approve a priority change?
We work through those questions with the people responsible for the process.
The existing spreadsheet can provide valuable clues. A color code may indicate an exception. A hidden tab may hold a capacity assumption. A manual adjustment may compensate for a constraint the ERP does not represent.
We examine which rules reflect sound operating judgment and which habits the business should reconsider before carrying them into a new application.
Faster development makes that judgment more consequential. We still need to define the right process and test how the software behaves when conditions change.
We choose the first project around a decision
At Streamliners Studio, our starting point is a recurring decision with a clear user and a measurable consequence.
“Improve visibility” is too broad to guide a useful first release.
“Help planners identify material shortages before releasing tomorrow's schedule” gives us something specific to work with.
We can establish the information required, the systems involved, and the action the planner needs to take. We can also agree on how to measure progress.
That might mean less time preparing the schedule, fewer changes caused by unavailable materials, or faster resolution of planning exceptions.
We assess the available options before deciding to build. An existing feature, a configuration change, or an established product may provide the best answer.
Where custom development makes sense, we keep the initial scope clear and involve operations and IT throughout. We also establish who will maintain the operating rules, manage access, and support the application after launch. [Source]
That gives the business a practical way to evaluate the result and decide what should come next.
Revisit the assumptions behind the spreadsheet
We build software for manufacturing, distribution, supply chain, and logistics by connecting operational knowledge with AI assisted development.
The spreadsheet is a useful place to begin because it shows where people have already invested effort in making the operation work.
Some workarounds will remain sensible. Others may now have a stronger business case for a dedicated tool.
Our recommendation is to revisit the assumptions behind that decision.
How much work does the process require today? What does the business lose when information arrives late? What would a focused tool cost to build and operate now?
The spreadsheet may have been the right answer when it was created.
The economics of the next answer deserve a fresh look.
Learn more about how we work at streamliners.studio.
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