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May 14, 2025 · The Bloomfield Team

Five Things Manufacturers Should Steal from the Software Industry

In 2001, a group of software developers published the Agile Manifesto and changed how an entire industry builds products. Within a decade, companies that shipped software once a year started shipping daily. Cycle times collapsed. Error rates dropped. Customer feedback loops tightened from months to hours. The underlying ideas were not new. The discipline of applying them consistently was.

Manufacturing has its own history of operational revolution. Ford, Toyota, Deming. The instinct to systematize and improve is native to this industry. But somewhere in the last 20 years, software pulled ahead on a few specific practices that manufacturing can and should adopt. Not because software people are smarter. Because they faced the same problems, information scattered across systems and people, and solved them in ways that translate directly to the shop floor.

For a deeper look at how these principles connect to AI adoption, see our complete guide to AI in manufacturing.

1. Ship Small, Ship Often

Software companies learned the hard way that big releases are dangerous. A year of accumulated changes shipped at once means a year of accumulated bugs, integration failures, and misaligned assumptions. The fix was to ship small changes frequently. Daily deployments. Weekly releases. Continuous delivery.

Manufacturing shops can apply the same principle to process improvement. Instead of launching a massive ERP overhaul that takes 18 months and disrupts the entire operation, make one change per week. Fix the quoting spreadsheet format this week. Add a setup checklist to one machine next week. Connect one data source to the scheduling board the week after. Small changes that stick compound faster than large changes that stall.

2. Measure Everything, Trust Nothing

Every serious software company instruments its product. Page load times, click paths, error rates, conversion funnels. They measure continuously and make decisions from data, not from the opinion of the highest-paid person in the room.

Most manufacturing operations measure output and quality. Fewer measure the process between input and output. How long does a job actually wait between operations? What percentage of setup time is productive versus searching for tooling or information? What is the real win rate by part type and customer segment? A production dashboard that tracks these process metrics reveals bottlenecks that output metrics hide entirely. The shops that measure the process, not just the result, improve faster because they can see where the time goes.

3. Version Control for Processes

Software developers track every change to their codebase. If something breaks, they can see exactly what changed, who changed it, and when. They can revert to the previous version in minutes.

Manufacturing processes change constantly. A setup procedure gets modified. A tool path strategy shifts. An inspection step gets added or removed. In most shops, these changes are undocumented. The current process exists only in the collective memory of the people running it. When something goes wrong, there is no way to identify what changed. Version-controlling your critical processes, capturing the current method and logging every modification with a date and a reason, creates a trail that makes troubleshooting faster and prevents the slow drift that erodes quality over time.

4. Retrospectives After Every Project

In Agile software development, every sprint ends with a retrospective. What went well? What went wrong? What do we change next time? The format is simple. The discipline of doing it consistently is what produces improvement.

Most job shops run 30 to 50 jobs per month and never conduct a structured review of any of them. Jobs that ran over budget get a conversation between the estimator and the shop foreman, if that. Jobs that shipped on time and on budget get no analysis at all. A 15-minute retrospective on every completed job, reviewing quoted hours versus actual hours, material cost accuracy, and any quality issues, feeds directly into better data for the next quote. One shop we spoke with started doing this in Q3 2024 and reduced quoting variance from 22% to 11% within six months.

5. Treat Data as a Product

Software companies build dedicated teams to manage their data infrastructure. They clean it, structure it, document it, and make it accessible. Data is treated as an asset that requires investment and maintenance.

In manufacturing, data is treated as exhaust. The ERP generates job records. The machines generate cycle data. The quality department generates inspection reports. All of this data accumulates in separate systems with inconsistent formats, missing fields, and no one responsible for its quality. When someone needs to analyze quoting accuracy or trace a quality issue to a process change, the data is technically available and practically unusable.

Treating your operational data as a product means assigning ownership, establishing quality standards for data entry, and building the connections between systems that make the data useful. The manufacturer that has five years of clean, structured job data has an operational advantage that compounds every month. Turning shop floor data into a usable asset is the foundation for every AI application, every analytics project, and every process improvement initiative that depends on evidence instead of memory.

The Transfer Is Already Happening

The manufacturers adopting these practices are not doing it because they read about Agile or attended a tech conference. They are doing it because the problems are the same. Scattered information slows decisions. Lack of measurement hides waste. Undocumented processes create fragility. The software industry solved these problems because software is information. Manufacturing is information plus physical transformation. The physical side has been optimized for a century. The information side is where the next 30% of operational improvement lives.