Waste is a core concept in many management philosophies within manufacturing. Taiichi Ohno, an industrial engineer at Toyota, introduced this term as part of the Toyota Production System, which later inspired the Lean Manufacturing movement in Europe and North America.
Ohno identified 8 distinct wastes in industrial production, summarized by the acronym DOWNTIME in English, representing:
- Defects — errors and failures
- Overproduction — producing more than can be sold
- Waiting — time spent waiting on machines or instructions
- Non-Utilized Talent — underutilized employee skills
- Transportation — distribution and logistics inefficiencies
- Inventory — the cost of unsold products sitting in storage
- Motion — unnecessary movement within the assembly process
- Extra Processing — over-engineering that adds no value
Let’s dive deeper into the waste of overproduction by examining common myths and their debunking, followed by lean tools that can help redirect processes toward improvement.

Myth #1: With current supply chain issues, I have to keep more inventory in stock!
Truth: Even non-perishable items can lose value over time. They might expire legally, become outdated, or suffer material degradation. Hoarding raw materials and finished goods can increase transportation and inventory costs, two other key wastes.
Next Steps: Use Bottleneck Analysis, a lean tool to visualize workflows and identify production bottlenecks. While you can’t control the supply chain, you can streamline your processes to better adapt to supply chain challenges.
Myth #2: I'm using state-of-the-art Smart Factory forecasting technology, so I know exactly what future demand will be.
Truth: Relying solely on technology for demand forecasting can lead to overconfidence. While Smart Factory tools can provide useful insights, they should augment human decision-making rather than replace it.
Next Steps: Ensure your Smart Tech supports a Just-in-Time (JIT) production environment, allowing demand to drive production rather than overproduction.

Myth #3: We’re a seasonal business, so overproduction isn’t our biggest issue — leftover stock adds to next year’s inventory.
Truth: Leftover stock can become outdated, leading to additional overprocessing. Documenting, storing, and maintaining this excess stock adds time and costs that often outweigh potential profits.
Next Steps: Rethink your business strategy using tools like Root Cause Analysis or Value Stream Mapping to streamline production cycles and reduce overprocessing.
Read More: VKS: The SOP Maker
Myth #4: We have to overproduce to account for defects during production.
Truth: Accepting defects as inevitable is a flawed practice. Instead, focus on eliminating errors with tools like Poka-Yoke, which directs attention to prevent human errors and improve overall quality.

Myth #5: Stopping the production line is more costly than overproducing a bit more product.
Truth: Continuing production despite defects results in both the cost of the defective product and the opportunity cost of not creating defect-free items. This waste accumulates over time.
Next Steps: Use tools like Takt Time and Overall Equipment Effectiveness (OEE) to assess and improve production efficiency.

Myth #6: I have to wait for new hires to settle in before applying their skills.
Truth: New employees bring untapped skills. Not utilizing them immediately can lead to wasted potential. They may also adopt inefficient habits if not given clear, optimized processes.
Next Steps: Use a platform like VKS to standardize work instructions and ensure employees adopt best practices from the start. Tools like Kanban can also help foster teamwork and responsibility.

Myth #7: Our shop floor is perfectly lean, so we don’t have to worry about overproduction!
Truth: Overproduction can happen at administrative levels too. Unnecessary reports, emails, or meetings are forms of waste that add no value to operations.
Next Steps: Embrace Kaizen — the principle of continuous improvement. Use the 5 Whys tool to identify and solve hidden inefficiencies in management processes.
