Analyzing developer capacity: Making vs. buying pallet detection

A new approach to ROI calculations

While an AGV is still prohibitively expensive even for most mid-size companies, hardware price differences account for a few thousand dollars on vehicles with a total cost over $100,000.

That makes improving performance the most significant opportunity for a robotics company to make a fleet more affordable and accessible to more potential customers. 

Users can achieve their automation goals with smaller, more efficient fleets when an individual vehicle completes more missions in the same shift. 

This approach may reduce the revenue of each sale. However, more affordable pricing ultimately increases the prospective customer base and overall market demand. 

Enhancing AGV value

Pallet detection makes an automated guided vehicle more efficient by increasing its speed, accuracy, and reliability. Each AGV completes enough daily missions to justify fleet investment. 

However, developing a system that consistently delivers these improvements is challenging.

Considerations

• Developers: Maintaining performance requires building and maintaining a living system that will always need to evolve.
• Engineering managers: Every new facility deployment brings a learning curve. What worked in nine facilities may struggle in the tenth.
• Business leaders: Development requires long-term resource allocation, operational reliability, and initial development costs.

Creating a pallet detection system: The resource investment

Organizations must carefully evaluate and align developer capacity with their long-term business objectives.

A robust pallet detection system must accommodate seemingly countless pallet types, load configurations, and environments.  This scope demands significant upfront investment and ongoing development resources.

If pallet detection is a robotics company's core competency, shifting resources away from it can result in a lackluster product. However, development needs for iniital design and ongoing maintenance can quickly exceed estimates. Then, pallet detection delays more valuable improvements elsewhere.

Considerations

• Developers: Building and maintaining a robust pallet detection system while meeting other development timelines.
• Engineering managers: Managing the complexities of many facilities, pallet types, and environmental variations at scale.
• Business leaders: Whether a reliable, scaled, proprietary solution is key to achieving core business goals.

Buying a pallet detection system: Maximizing benefits

Companies produce more robust and differentiated autonomous mobile robots when they concentrate on developing their distinctive solution. A third-party system offers compelling benefits when pallet detection isn't central to their IP.

Third-party providers specializing in pallet detection often invest far more resources than a company developing it as one component of a larger system. This focused expertise typically results in more sophisticated solutions at lower costs with dedicated support. 

This approach eliminates substantial upfront investment in data collection and performance optimization. It also removes the ongoing burden of algorithm maintenance.

Considerations

• Developers: More focus on core innovation with full visibility into a third-party algorithm. 
• Engineering managers: Reliable scaling without dedicating permanent resources to pallet detection. 
• Business leaders: Proven technology that delivers immediate value and reduces operational risk. 

Evaluating third-party solutions

When selecting a third-party pallet detection system, a vendor’s development approach and real-world experience are essential indicators of system reliability. Prioritize a solution that has proven it can:

• Maintain continuous operations
• Reduce costly mistakes
• Enable faster vehicle movements
• Support diverse facility needs
• Future-proof automation investments

Each new pallet type and facility configuration introduces complexity that grows exponentially at scale. The most effective solutions embrace this variability. 

ifm spent nearly a decade developing the industry-leading Pick & Drop System using data from tens of millions of picks across facilities worldwide. The development team quickly learned what others are now discovering: The real world rarely matches test conditions, and each new pallet type requires an adaptation to the model. 

Even as AI machine learning gained popularity, ifm continued investing in a heuristic method using only real-world data. This approach differs from the uncertainty of an AI learning model by offering clear visibility into its decision-making process. Companies can have confidence that it will function as expected.

This transparency and proven reliability across diverse environments ensure the solution can evolve alongside changing operational demands.

Making the right choice

The build-versus-buy decision rests on both immediate impact and long-term strategic objectives. In-house development offers more control. But ongoing resource demands significantly impact core innovation capacity. 

Selecting a trusted third-party solution combines proven reliability with continuous development commitment. AGV manufacturers can focus on their distinctive capabilities. 

Ultimately, success lies in delivering enhanced value to end users through improved fleet efficiency and reliability while focusing on strategic differentiation.

Keep reading: Increase mission throughput with Pick capabilties

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