Big data is here, and it is making waves in every aspect of work and life. From predicting customer demand to highlighting gaps in productivity, through to algorithms controlling brand awareness – big data is a key player in warehousing and manufacturing operations. 

The good news is that warehousing and manufacturing companies are using big data and data analytics to automate, power, and streamline operations. However, many companies are drowning in data.

Data is coming at companies from all angles – RFID sensors, IoT devices, the machinery powering operations, autonomous mobile robots (AMRs), logistics data, ecommerce and marketing, etc. The challenge is understanding how to quickly process this data to benefit operations. Your approach to data analytics must be unique to your business goals and customer expectations. 

Just as there is not one approach to automation or how to use continuous improvement strategies in your operations – the same holds true for data analytics. Decisions on automation, market demands, and using continuous improvement strategies to support your operations must be based on data.  

In this blog, we take a deep dive on data analytics and manufacturing to help you understand how to use your data to optimize your operations:

  • The link between big data and lean manufacturing
  • A refresher on continuous improvement in operations
  • How data helps you think critically about your operations
  • How big data optimizes continuous improvement
  • Strategies for using data in your operations

Data is your ally in streamlining operations, improving warehouse management, making informed decisions about market demands, predicting gaps in the supply chain, understanding what your customers want, and supporting change across all facets of your operations. 

Big Data, Data Analytics and Lean Manufacturing

In 2001, Gartner analyst Doug Laney, defined big data as:

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. 

Big data is unique due to its volume, variety, and velocity:

  • Volume: data is collected at all stages of design, production, manufacturing, distribution, and warehousing. 
  • Velocity: the rate at which data is generated and collected to be processed, analyzed and understood, as a means to positively affect the business is constant – 24/7. 
  • Variety: this includes all data collected from machines, IoT devices, RFID sensors and tags, AMR feedback, logistics, shipping, marketing, and more. The data ranges from structured numerical data to unstructured data in a range of formats such as messages, emails, text documents, etc. 

The challenge for companies is knowing how to use this data – how to identify data that can benefit your operations, understanding what to do with it, and then capitalizing on it.  

Within lean manufacturing, data analytics is the best way to proactively respond to the 8 key wastes of manufacturing: 

  1. Defects – are there process changes you can make to minimize product waste and damage, limit production downtime, reduce injuries, or address gaps in product inventory?
  2. Overproduction – how can you ensure you do not have product sitting on warehouse shelves? Are there process changes, automation, or communication adjustments that can improve just-in-time (JIT) management?
  3. Waiting – where is your idle time in your manufacturing and warehousing processes? What is causing this downtime – labor shortages, inefficient training, lack of automation, materials shortage, etc.? How much does this waiting time cost your operations?
  4. Non-utilized talent – do you have employees who could be doing more high-value skilled work? Is there a way to use automation and AMRs to reallocate employees to high-value roles?
  5. Transportation – are you moving goods efficiently through design, production, packaging, and distribution? What is slowing your shipping and distribution process? How well is your supply chain operating?
  6. Inventory – do you have too much or the wrong inventory sitting on shelves? Are customers left waiting for products they want? Where are the gaps in your JIT management?
  7. Motion – are people spending too much time walking, bending, lifting, and doing complicated repetitive procedures? How can automation and AMRs minimize extraneous motion and streamline efficiencies?
  8. Extra-processing – do you have redundant steps in your design, production, manufacturing, and distribution process? What is slowing decision-making, interrupting the design process or causing confusion?

The insights derived from big data give companies the ability and opportunity to unlock potential, improve quality, lower costs, streamline production, and strengthen all aspects of their operation. 

Ultimately, companies who act on their data become agile, responsive, resilient, and better equipped to introduce change under pressure. 

What is Continuous Improvement in Manufacturing and Warehousing?

Continuous improvement in manufacturing and warehousing uses small constant measurable change that results in an efficient production process free from waste, interruptions, delays, and unnecessary steps. 

As we wrote in Sustaining a Lean Supply Chain in 2021 and Beyond, incorporating continuous improvement strategies throughout your operations is key to lean manufacturing and JIT success:

  1. Always be looking for opportunity: Continuous improvement means you’re constantly looking for the barriers to success, ways to speed time-to-market, increasing flexibility and agility, sticking to a zero-errors culture, standardizing processes to ensure safety and quality, and building a supportive employee environment.
  2. Take advantage of Industry 4.0: Technologies such as IoT, 5G and 6G, automation, artificial intelligence, additive manufacturing, and 24/7 connectivity give companies the tools and insight to be responsive and agile.
  3. Streamline warehousing and distribution: Co-locating warehousing and distribution, using automation such as robots to move goods safely and securely, and redefining JIT inventory level benchmarks are just some of the ways companies can fix one of the largest bottlenecks in modern supply chains.
  4. Eliminate waste: Core to lean manufacturing is reducing waste within the supply chain. Look for ways to eliminate defects, under-utilized employees, transportation slowdowns, excess inventory, inefficient processes in moving goods, and delays.
  5. Focus on the customer: Your goal is to provide value to the customer, and the best way to do this is by optimizing quality and reducing cost. 

Taking continuous improvement to the next level demands using the data you have to understand where change needs to happen, what the change can produce, and how to measure the effectiveness of this change. 

For example, using big data to understand and predict customer patterns, or using automation and machine learning to improve product quality, eliminate waste, and speed time-to-market. 

Continuous improvement in your processes is supported by capitalizing on the volume, variety, and velocity of AI, automation, IoT, and robotics data and information to drive informed decision-making, process improvements, and product development. 

Big data is your ally in making small measurable process changes that add up to noticeable improvements while eliminating the risk that comes with change. 

5-Step Roadmap for Powering Continuous Improvement with Data Analytics

Alone, big data and data analysis have zero value. The value comes when companies accept that they must be proactive with their data. This demands taking action, even action that can be difficult or introduce change people are resistant to. 

This 5-step roadmap for powering continuous improvement with data analytics can be customized to your unique needs and challenges:

  • Know Why You’re Using Data Analytics

Data is powerful when you know the problems or challenges you need to solve. What do you need to improve? Where are your wastes? What are your customers telling you? What are your business goals? 

  • Create A Plan for Continuous Improvement

Use your data to inform how and where you introduce change. For example, does your data highlight gaps in productivity due to a labor shortage or non-utilized talent? Can you introduce AMRs and automation to fill these gaps and move high-value employees to other tasks? What is your rate of product and production waste and how much does this waste cost?

  • Be Ready to Communicate

Change is hard. It’s critical you communicate the why, how, what, when, and where of change. Do not do this in a top-down manner. Get the people affected by this change involved in deciding how best to roll out the new process or procedure. Do not overlook the firsthand data and information your employees can give you about current processes and how they can be improved. 

  • Define Measurable Outcomes

Measurement is critical in creating lasting change. This measurement must start from day one, you need to know where you are, where you want to go, and understand your success rate. 

For example, you want to improve how products move from manufacturing to packaging because the current process is too slow and results in delivery delays. To create effective change in this process, you need to measure the current process and continue to monitor, adjust, and measure any changes you make. For example, is the process faster and less prone to risk when you use AMR pallet moversand tuggers – what does your data tell you about this change? What other improvements do you see because of this change?

  • Trust the Data

In the early days of GPS, many people resisted trusting the GPS to get them from point A to point B as efficiently as possible. The same holds true for big data and data analytics – remember, the numbers do not lie and hold no bias. The data is not always going to tell you that everything is perfect – in fact, the value comes when your data highlights opportunities for improvement. 

Ultimately, this circles back to building and supporting a culture of continuous improvement. Big data and data analytics are critical in helping you make the right changes that will benefit your processes, people, and products. 

Finding the Real Value in Big Data

In lean manufacturing, the value in big data and data analytics is in enabling continuous improvement and ultimately eliminating waste from your operations. 

However, it’s critical companies do not expect overnight success and introduce too many changes at once. Starting small, and continuously measuring and adjusting makes it easier to create lasting change that leads to further improvements.

Remember your data – do not ignore the data analysis that says your change is not working or is creating more waste and risk in your operations. The value in big data is in listening to it, understanding it, and using it to support your culture of continuous improvement. 

We’re here to help you build a culture of continuous improvement using our people, processes, and technology to support change in your operations. Contact us to discuss how our AMRs can help you eliminate waste and enable measurable change.