Category Archives: data

How to use data to select the best improvement projects

Possibly one of the hardest things to do is make a selection of all the possible improvement projects that are possible for the company. Talks with quality and safety managers showed that this topic is pretty high on their list. A lot of you struggle with managing internal projects/initiatives and how to prioritize them. Prioritizing projects and knowing where to assign resources is more of an art than a science and the road to the result might not be linear. It is important to balance short-term gains with long-term impact.

Start with Data

The number one input to make a good call is having the data. It should be possible to find out where things go wrong and how much the financial implications are. It would be even better if there is data available on different root causes that caused the issues. All the data can be analyzed and ordered to be helpful in making the right decision on which improvement projects utilizes the resources the best.

Selecting Projects

When selecting projects, it is always good to look at perceived value but also to look critically at the time required implementing the improvement project. Some great criteria to look for are:

  • Time
    required.
  • Impact
    on the business.
  • Impact
    for customers.

These can be applied in any particular order and should be in line with company goals. When there are already projects going on, a new high-impact project might not be the best choice.

Short-Term Gains

The time needed to implement and verify the results is
an important aspect when selecting the projects to work on. Of course, projects
with a high financial benefit and a short time period are the gems but also
most of the times are already finished.

Short-term improvement plans with a positive impact for
the customers are even better. These will help boost customer satisfaction or
customer loyalty, both of which are very important for the continuity of the
company.

Internal process improvements are more important to streamline the internal process, which could result in a positive effect for the customers. They are essentially implemented to improve the operations. These projects can be hard to find resources for because not all the losses of time in the process are always crystal clear. Here, data is very important because the data will help in proving the importance of the changes. Also, these projects require some willingness to change by employees, which will lead to resistance.

Long Term ROI

When we look for more long-term improvements with a longer ROI it can be hard to find the required resources. These plans require a completely different approach. Due to the long-term impact on the company, it is good to align them with the company’s vision and make them strategic for the organization. This can be done regardless of the size of the company. These projects require significant buy-in from the company hence top management involvement is key in this.

Long-Term Impact on Product

Then there are the projects that have a long-term impact
on the product or service the company produces. These projects will have major
benefits over the long haul for customers and business. Most of the time the
R&D department does these projects but they can also be initiated by
quality when it comes to production improvement. However, a close collaboration
between engineers and quality is very important. The drawback is the long
development time which requires serious resources. A clear ROI is important in
this case.

Conclusion

In the end, it all comes down to internal resources and
how to deploy them. As long as decisions are based on data that has been
collected by the company, they are backed with some sort of evidence. Of course
you shouldn’t stare blind on the data because there could be opportunities that
haven’t been part of the data collection until now.

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Simple Guide to Process Improvement

As discussed in one of our previous blog posts on process mapping, the handover from one process to another is a critical point where many difficulties arise. When both processes are done by the same person, the difficulties might be limited, but when performed by more than one person, it becomes exponentially harder. On top of that, when information needs to flow from one system to another, significant difficulties can arise even when performed by the same person.

Different People

In the first case when the handover is between people, communication is key. Every person needs to have access to all the critical information. A good example is when the customer switches from sales to project execution, or from project execution to support, or in a different scenario from sales straight to support. Having the right information is crucial for the next person in line to support the customer effectively. All the information needs to be known to the next person. This includes all the things that went badly, as well as all the things that went well. This allows the next employee to be prepared for any potential difficulties. The handover between people can practically be done by having a physical or digital handover meeting. During the meeting, the team will have an open discussion. Simply start by creating a structured calendar for every handover meeting to give some guidance to the people.

Different Systems

Another potential pitfall is the switch between systems when one process flows into the next. For example, when a deal has been closed, it may have to go from your CRM to ERP for order handling. A seamless integration prevents people from keying in the information again and introducing errors. Hence, a good integration is money well spent. Also check which information is crucial for an effective handover. You don’t have to hand over every email and calendar invite, but make sure you give as much relevant information as possible. As pointed out in the previous paragraph, make sure you hand over what is important.

Outcome Is Key

Regardless if the process goes from one person to another or switches systems, the end result is what matters during the handover. It happens all too often that information cannot be found or just disappears. This seriously harms the outcome of the process and the start of the next. Make sure you have some sort of guidance in place for the handing over process.

Audits

Audits are great methods to check how the handover is performed in practice, not just on paper. Companies come up with the best playbooks, but it doesn’t mean it always works. With an audit, you can simply check the handover and see what went wrong. You can even perform a brown paper audit to get a good idea of where the handovers actually are and which information is crucial during the handover.

Conclusion

Handovers can costs the company some serious money if they aren’t done properly. This means that a lot of money can be saved by coming up with a great way of handling this. Check out what information is crucial and make sure it is transferred properly.

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Workshop Qooling: How to use data as input to improve my business.

Last week we hosted a great workshop by Tessa Lange. She is an expert in continuous improvement and shows the participants how to use data as an input to improve business. Some key take-aways will be pointed out in this post.

Why Collect Data?

Lots of companies collect a ton of data without any valid reasons. Data is often described as the new gold and therefore companies just collect tons of it, sometimes without reason. The downside of this is that somebody needs to create that data. In a company setting, this means that the employees need to key in the data into some sort of system. Creating this data costs time and effort by the employees. Hence, collecting the data without any purpose is a huge waste of valuable time and effort. Also, when people need to fill in a ton of forms and they don’t see the benefits of this, they will either get very frustrated or simply stop filling in the data. With this knowledge, it is even more important to think twice about why you collect certain data. Of course, there can be very good reasons for collecting the data like laws, certain standards or maybe to improve the company. Just think about the reason for every point of data that you are collecting.

How Do I Rate the Data?

When the data is collected, it is important to rate the data and check its reliability and validity.
  • Reliability = If you measured it again, would the outcome be the same?
  • Validity = Do you really measure what you want to measure?
To be on the same page with your employees, it is important set certain norms on what data is right and what data is wrong. Data is often used as a yardstick to measure performance and in order to do this sensibly, you need to know if the data is usable.

Interpreted the Data

Interpreting data isn’t as easy as it may sound. To draw conclusions out of data you need to understand the context. Context can be the unit of the data point or the relation of the data to data in a previous period or even a benchmark you work against. Simply concluding if the company is doing a good or bad job based on the data doesn’t make sense. Make sure you have context. When the context is clear and you are measuring the same data over and over again, you can check for anomalies in the data. Look for spikes or drops in the data, or points that break the pattern.

Start Improvement

When all the data is collected and you start to see trends that aren’t according to the plan, you can dive into these trends to see what goes wrong. When you really start to see a negative trend, you can start an improvement process to try to break the trend. This can be relatively easy, just for example, by talking with suppliers when they make too many mistakes. On the other hand, these plans can be pretty elaborate like complete safety awareness programs. The results of these improvements should be measured on an acceptable timeline. You cannot expect to see safety changes in a couple of weeks. While improvements from a supplier should be seen relatively quickly.

Conclusion

The most important lesson is “context”. Always have a context before you analyze the data. Before you know it, you are improving the wrong processes. Published by: