A whole new range of AI-based supply chain technology is currently sweeping across retail, 1PL and 3PL warehouse operations.

And it’s working — for those who get it right.

A recent Gartner study revealed companies using AI and machine learning systems perform better across KPIs like demand forecasting, order management and supply planning.

However, any business looking to replicate this should ask themselves: Do we trust the data fed into these models, and is it providing the whole picture?

Because AI with insufficient data is ineffective and unintelligent.

We’ll show why data accuracy is the secret sauce to keep the supply chain running. We’ll identify where you’re losing it and an easy way to obtain it.

What is needed? Supply chain accuracy

Data accuracy is crucial to warehouse operations in 3PL, manufacturing, or retail. We recently conducted a straw poll on Reddit among supply chain professionals, which confirmed this finding*.

Inaccuracy and accuracy both come at a cost.

Accuracy is the only one that will pay you back.

But accuracy requires investment and checks to compare system data with reality. A good effort-accuracy balance is integral to success in warehousing and supply chain logistics.

Bad data leads to bad decision-making. Data focused on specific areas leads to overfitting, meaning the model struggles to adapt to new scenarios.

*Other options included space optimization, storage issues, and adopting new tech.

Growth of AI technology in the supply chain

Gartner’s survey of supply chain organizations found that the top-performing organizations have invested in AI and machine learning to optimize processes.

They prioritized their digital assets for productivity rather than seeking cost-saving efficiencies. According to Gartner, capturing and leveraging organizational data is a crucial strategy for high performers.

In other words – these systems are working because the data is reliable.

Capturing, protecting and then leveraging an organization’s data through the use of AI and Machine Learning is an example of how organizations are increasingly turning towards intangible assets to extract new sources of value.

Ken Chadwick, VP Analyst Supply Chain Practice, Gartner.

Gartner said these businesses are starting to use AI for improved decision-making processes that unlock new sources of value.

Bridging warehouse and distribution operations - where things go wrong

A consignment goes through a complex process before it is loaded onto a truck. This includes boxing, organizing into carts, loading onto the right trailers and finally, the trucks.

TLDR: the right products must be on the road quickly and accurately.

But mistakes happen…

These include:

  • Misplaced Boxes: Boxes are not scanned during the packaging process, leading them to be misplaced on the wrong cart.
  • Inventory Problems: Discrepancies between actual stock and inventory records must be prevented.
  • Over-Ordering: Sales over-ordering products, leading to excess inventory, which sits around the warehouse going stale.
  • Misdeliveries and Losses: Products may be loaded onto the wrong trucks without precise tracking and accountability.

High accuracy in the warehouse process chain is the core business for supply chain and 3PLs especially. Here, we include receiving, put-away, picking, cross-docking, staging, packing/loading/shipping and inventory checking.

It also highlights three fundamental challenges in retail supply chain management: inadequate inventory visibility, difficulties in forecasting and excessive time spent locating products. Overall, it emphasizes a need for enhanced tracking, effective data analysis and streamlined processes.

And all of this must be done without increasing employee workloads with more data capture and manual scanning.

Operational inefficiency - why are things going wrong?

Put simply, track and log more products throughout the process. The machine learning models then have the whole picture and can be better optimized in the future.

So where does it go wrong when it comes to warehouse accuracy?

There are a number of culprits:

  • Slow, tedious, and non-intuitive processes: A real problem in an environment with a high staff turnover.
  • Error-prone data capture: This could be single-scanning devices or relying on pen and paper, which is prone to human error.
  • Legacy systems: These make it difficult for IT departments to implement new technology.

Deploying new technology into warehouses can be problematic. While data capture can be done on individual devices, they must feed into a central system. It has to be introduced slowly; otherwise, the entire business can halt if it goes wrong.

PepsiCo is an excellent example of a company bringing new technology into the warehouse supply chain. Its technology venturing arm, PepsiCo Labs, subsidiary exists to identify and embed test new startups into the company technology. It is currently working with MatrixScan Count in its warehouses.

A possible solution - smarter data capture

Providing warehouse employees with effective scanning tools is vital to increasing warehouse data capture.

There is a way to speed up data capture by 10X with MatrixScan Count.

Warehouse supply chain businesses already use Scandit’s MatrixScan Count technology. As the video below demonstrates, it allows employees to scan multiple barcodes simultaneously and receive real-time guidance via augmented reality.

Moreover, it comes with a pre-built UI, requiring minimal implementation. IT departments also save development time as it can be installed on any device with a camera with just a few lines of code.

Alternatively, MatrixScan Count is available as part of our Scandit Express app. So testing, integration and deployment can be sidestepped.

Scanning multiple items simultaneously saves workers time and provides a greater flow of data for machine learning models.

Moving confidently towards AI-driven warehouse technology

Retailers, manufacturers and 3PL businesses will inevitably move to AI-powered supply chain systems.

Just don’t get caught up in ‘shiny object syndrome.’

10 out of 10 AI models would be correct if their data were right too. Data accuracy throughout the distribution stages is vital to getting the most out of AI.

To do so effectively, they must confront operational inefficiency challenges and adopt advanced smart data capture solutions.

Find out more about how our technology can bring more accuracy to your warehouse.