Just in Time - Making Research More Efficient?

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    In the Insights Industry, we are constantly on the lookout for ways to become more efficient. We have found ways to automate extraneous processes, design more impactful research experiences (for researchers, stakeholders, and participants), and are exploring new ways to report valuable insights to help stakeholders activate insights to their fullest potential. But we still have a way to go until we are able to call ourselves a truly efficient working industry.

    In our quest for efficiency, it is increasingly evident that we have become too used to waste; wasted insights, data, resources, budget, time, and more due to many reasons, but in particular a lack of practical innovation in this area until recent years. As we look to other industries for proactive, transferrable methodologies, we should consider efficiency measures from industries that already have answers – namely, the ‘Just in Time’ method in manufacturing. How would the principles of this method work in the insights industry to help bring efficiency about in a quicker and cleaner way?

    'Just in Time' Principles

    Just in Time’ (JIT) manufacturing refers to the process of building something just before it’s needed to reduce the amount of waste that would otherwise have occurred. This waste was originally in the form of storage costs, time, and material resources, but now has a broader purview in the form of the management philosophy the phrase has come to represent.

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    How would the principles of 'Just in Time' manufacturing work in the insights industry to help bring efficiency about in a quicker and cleaner way?

    Developed within the Japanese car manufacturer, Toyota, the JIT method was created to increase their ability to meet consumer demand in that very moment. This had many benefits, such as being able to receive materials only as they need them, minimising the amount of time between material storage, production and supply, and maximising the impact of each product sold.

    There are a number of elements required for the JIT method, including:

    • Continuous improvement to remove defects/features that add no value
    • Eliminating waste and mistakes
    • Good organisation and discipline
    • A kanban board for agile project management
    • Set-up time reduction (increases flexibility and promotes handling smaller batches)
    • Multi-skilled workforce (greater productivity, flexibility, etc.)
    • Mixed and levelled production to streamline the flow of products throughout the factory
    • The automation of extraneous processes
    • Problem signalling to highlight issues as soon as they happen
    • Preventative maintenance for flawless running
    • Greater individual involvement within the production process

    All of these elements creates a culture of control and increases productivity within all production processes, unifying everything and everyone together under one common goal – to eliminate waste.

    Many of these elements of ‘Just in Time’ are directly transferable to market research, but only a few are required for application in insight generation and activation: continuous improvement, to improve on processes that may contribute to wasted resources; good organisation and discipline to generate and deliver insights to the right people at the right time; make use of a multi-skilled workforce to create better, richer insights; automate extraneous processes so insight professionals have more time to analyse data and report it to stakeholders; problem signalling to highlight any issues in the production process; and preventative maintenance to help manage participants in ways that encourage them to provide better quality data, halting quality issues before they arise.

    The Benefits and Risks

    Where could the JIT method be a boon and a hindrance to making market research processes more efficient? In market research, the JIT method could lend itself to developing quicker, more impactful insights – but it could also be just another fad stalling the insight generation and activation processes if not implemented properly.

    The concept of gathering and delivering insights to stakeholders in the moment they need them is extremely attractive, and something we have been working towards as an industry for a while now.

    Using the JIT principles has great potential to maximise the impact of those insights on the decisions being made by stakeholders. It also has the potential to make research more efficient through reducing issues typically encountered on in research processes, such as: shorter production times so we can move quickly from one project to the next, reducing the costs of long-term insight generation, and more efficient spending on the areas we need most. Efficiency surrounding time and budget are the most common discussed areas to where insight professionals think innovation is needed most, and the JIT method is waiting to provide.

    However, the human element is what makes or breaks the JIT methodology. When everyone comes together and works towards a common goal, we know we can achieve it; but this method also relies a lot on consistency and ability to meet deadlines dead on, which we might not be able to do every single time. The phrase ‘to err is human’ is incredibly applicable here, and this method is very unforgiving when it comes to mistakes. So, while the JIT method might help efficiency in most cases, there is bound to be a few instances where mistakes cannot be eliminated entirely, thus disrupting the entire production process.

    If there are disruptions in the supply chain then the whole system falls apart; in terms of insights, this means if, for example, the data isn’t up to scratch during the first data collection stage, then the insight team will need to spend more time getting the right data to deliver the best quality insights possible, and the rest of the insight production process will be delayed until the relevant data has been collected.

    Even with all elements implemented properly, there are external factors that are out of our control, like the participants providing subpar data. While Toyota refined this method, they also serve as a warning for just how badly the JIT method could go wrong due to external uncontrollable factors: in 1997, a fire at one of Toyota’s supplier (the effects of which rippled out to other suppliers) meant the car manufacturer’s production process was completely destroyed, and they lost 160 billion yen in revenue. If the external factors affect the insight generation process, then we will be late to deliver those insights, and the decisions stakeholders make will be uninformed as a result.

    Building a JIT-Based Methodology

    So, are the risks bad enough to entirely put us off implementing the JIT method in the insights industry? Not quite.

    If this is to work, then we need to find a way we can alter the JIT method to minimise the potency of those risks and maximise the benefits. To do this, we would need to marry the benefits of real-time insight generation and activation with a more forgiving strategy to account for the human factor in the process, and this will create a more beneficial JIT-based method.

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    While the 'Just in Time' method might be a bit harsh, there are certainly many of its principles that we can incorporate into the research process to help realise a greater impact with insights.

    The development of the JIT-based method will need to be iterative in order to have the cleanest transition and largest impact. To start, we need to consider our overall priority: to deliver the right insights to the right people at the right time, and make sure that any incorporation of JIT principles works to aid this outcome rather than work against it.

    Next, think about our own research processes, and apply the most beneficial JIT principles first: continual improvement and eliminating waste. This will work to get insight teams used to the new research techniques that will help them make the most of their time, resources, and skills when generating valuable insights.

    If at any point the quality of insights declines, then it will reveal an issue that needs work (continual improvement); this will require a certain agility to make it right and maintain productivity in other parts of the process, but will be worth it when we can generate high-quality insights more efficiently, that will realise a bigger impact on stakeholder decisions.

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