So here is a much-delayed part two to our Gloves and Goggles piece back in April.  

In this piece we’ll be diving into the second significant trend in the Industry 4.0 space in New Zealand, but first a brief recap of part one. In summary we broke the trends down into three categories: 

Part One: Shop floor intelligence and closing the data gap. 

Part Two: Getting data to where it’s useful. 

Part Three: Integrating with your supply chain. 

If you haven’t read part one, here’s a link:  

In part one, we were keen to show the importance of linking your businesses key success drivers to the shopfloor. We recommended using Shopfloor Intelligence to gather more accurate real time data relating to productivity, quality, safety and so on. We now need to talk about how to present and interpret the data so that it is useful and helps with the decision making process. 


Data, Data everywhere but not an insight in sight.  

Data rich but information poor is a phrase you may be familiar with. It is an easy trap for businesses to fall into if they don’t keep the objective in mind. It essentially refers to gathering a wealth of data, from the shopfloor for example, but no way of turning this into insights to help us make better decisions, faster.  

The most advanced manufacturers globally are looking to automate the decision-making processes on the back of insights they are obtaining and analysing continually. However, what we’re seeing in most manufacturers is a failure to turn the data they’ve currently got into easily digestible insights for their teams to make better decisions faster. The ultimate goal may be to automate decision making, but the first step is to empower teams with the data you currently have.  

This is where it’s important to highlight that Industry 4.0 and Lean Manufacturing (and Continuous Improvement, Six Sigma, DMAIC etc.) aren’t two different things but part of the same continuum. Where lean looks for us to measure in order to improve, Industry 4.0 technologies enable companies to measure what they are doing and make decisions about how to improve much faster, cheaper and easier than they have ever done before.  

Where shop floor visual management acts to provide the necessary people with up to date information on job progress, job lists, on-going issues and actions, Industry 4.0 and the associated technology aims to deliver the same outcomes but with less latency and reduced administration.  


An INSIGHTful example 

So back to getting data to where it is needed. In Part One we presented a simple exercise to evaluate your current shopfloor KPI maturity, by looking at what data was being gathered. Taking this a step further, we now need to understand how we transition this data to insights for the right teams, in the right format.  

Take quality monitoring for example. Previously, quality assurance (QA) team members would manually check parameters; record faults; update spreadsheets and visual management boards. Now,  technology can track quality information live (vision systems, check weighers, dimensional analysis sensors) reducing the QA time to physically carry out the inspection.  

It is not the automation aspect we wish to focus on here, but the improvement in the knock-on decision-making and improvement processes. The data gathering at the shopfloor level by this technology could simply be kept locked within the PLC of the equipment itself, doing the job of rejecting the necessary items. If we were to extract this information and transfer it to a suitable database/enterprise system whereby we can show the trends over time of faults and display this live to operators and quality assurance staff on the floor then their time can be better spent conducting problem solving to identify root causes and resolve them (with almost instant feedback).  

As with lean, it is not simply the act of measuring that will improve performance but the associated actions we take as a team to identify causes and resolve them. Greater value can be delivered to the business by the improved quality and speed of the problem solving/feedback loop than simply through the automation of the quality processes themselves.  

It’s worth remembering that businesses often automate this type of task in an effort to reduce labour costs in the inspection process, rather than focussing on the financial benefits of being able to identify and solve root cause issues faster. The value of elevating the role of the team to problem solvers rather than problem identifiers may far outweigh the reduced inspection time.  

The above diagram shows a simplified view of the suggested staged approach to improving your use of data. 

One Step at a Time 

We alluded to a tiered approach earlier in this blog, whereby if we can improve the insights we obtain, our teams can make better decisions. The next evolution of this is to automate parts of the decision making itself through direct feedback of measurement technology to the machines and processes that influence the parameters they are measuring. An example here could be a check weighing machine feeding back live information to the packaging machines to constantly adjust and optimise their operating parameters to reduce giveaway and rejects simultaneously.  

The reality of taking this next step is that it is often a requirement to simply automate the data collection and deliver the insight to teams in order to build confidence in the data being acquired. This helps establish the relationships between the data and the relevant process changes that will deliver the desired outcome. In which case, in terms of change management and cost it often makes sense to stagger the journey, reaching the insights step first before trying to automate any process response.  

Although not a universal rule, from our experience it seems to be the one that gains greater buy in from operators and team leaders, so when the second step of process adaptation is automated, we don’t get so many situations where people are ‘overriding’ the system, circumventing the very solution you have just invested in. 



Part One of this series was about identifying what data you should be gathering on your shop floor, with the interactive exercise a simple way of understanding what gaps you might currently have. Part two has focussed on what to do with that data once we have it. Keeping it locked away in the technology itself will limit the value the investment in that technology can add to your business whilst hamstringing the development of team members who could be using live data to optimise the process continually.  

Whilst acknowledging that technology allows us to automate data feeds and change process parameters automatically (and optimise these interactions through machine learning) we believe that taking a staged approach works best. Delivering insights to teams can assist in change management and team upskilling whilst reducing technology investment costs by acting to inform the iterative development of future automated process changes.