How to approach data capture

Data capture is the powerhouse of any organisation - the problem is the quality of data being captured.

You cannot gather data about the performance of anything without a base line to measure against, whether you are?running a race or a production line. Dr Russell Ackoff talked about 5 levels of content of the mind:

1. Data - symbols and numbers we collect, but are useless without being processe
2. Information - Processing data allows us to answer questions like Who, What, Where & When.
3. Knowledge?- Application of information that helps answer questions like “How”
4. Understanding?- Appreciation of the “Why”
5. Wisdom?- Takes you beyond understanding is the essence of philosophical probing. Wisdom is the process by which we know right from wrong, good from bad.

The first problem in the majority of scenarios is the weight of content is heavily stacked at the data end of the scale, with very little information, less knowledge, and almost no understanding or wisdom (you can’t impart what you don’t have).

It’s this very same illogical thinking that kills most business attempts to improve. They will collect masses of data, quite often reported on spreadsheets and dashboards as KPIs or some other metric and are killing themselves with the numbers and poor quality of these so called KPIs (which are not KPIs as they are just data).

Let's take for example the single biggest KPI error ever made regarding Service Level and it goes something like this - “We want to fulfil our customer orders to a 99.5% service level, because that will keep the customers happy.”

Here is why this statement is flawed..

The customer isn’t paying for 99.5% service level, and the actual SLA in place either hasn’t been read or has been mis-interpreted.

Secondly, when forecast accuracy is lower that 99.5% you cannot deliver that service level without incurring costs in one or more of the following areas:

• Stock holding (if this is indeed possible and at a cost to you)
• Labour inefficiency (at odds with your labour productivity KPI)
• Stress on the inbound & outbound supply chain (at odds with your COGS KPIs)
• The further away from 99.5% the forecast is, the more expensive it will be to service the demand.
• They measure service level differently, and there are very few organisations I have seen where their service level matches that of the customer they are serving.

What then happens is you try to support the flawed Service Level KPI with a few others.?For example Service Level will be followed by:

• ‘forecast accuracy’,
• ‘stock holding’,
• ‘labour efficiency’,
• ‘material COGS’,
• ‘Special delivery costs’,
• ‘OTIF site despatch’
• …..and the list goes on.

So you see how everyone gets bogged down with more and more data points to support the original one that was built wrong.

A better way to approach data capture:

Data capture needs to be against a known fixed point, a relevant target that is accurate and is something we can assign meaning to. ??You need to process data into information, that you can use to measure performance and drive improvement.

Here are the 4 basic stages of data capture:

1. The Plan

The first stage is always the plan (schedule). That is the fixed datum that we know to be the requirement of the site, without that what are you measuring? ?

2. Execution of the plan

Next we capture the key data involved in the execution of the plan:

• did we start on time,
• how many did we make,
• how many people,
• how fast,
• how much downtime,
• why,
• how long to changeover,
• yield,
• giveaway,
• waste,
• rejections,
• over makes etc etc..

3. Context

Remember it is all ?just data until you give it some context such as:

• Efficiency v Target
• ?Waste v Standard
• Downtime v Norm
• Speed v Target

?4. ?Conversion

You need to convert the data into information for decision making:

• Why does line 1 always start late
• Why does A shift perform better than B
• Why are Mondays worse than Fridays
• Why is waste always high on Product C

Congratulations, you now have information you can use and you can ?start to look at how to make things better.

The Improvement Phase

The improvement phase gets more complicated as it includes the application of some tools and competencies that people will generally not have in their toolkit. A major part of your improvement phase is knowing these gaps. It is vital you pass the knowledge to the people so they have ?the competence and confidence to deliver the improvement (I write a lot about the importance of the competency framework https://www.dhirubhai.net/pulse/competencies-starting-point-martyn-king/)

As you create a system and process for improvement you start to see how at every level there are new areas of understanding needed and questions you can ask:

• What KPIs do we need and how to we measure them?
• How do we use the KPI data to make decisions?
• How do we improve KPIs using improvement tools?
• How do we change the process to eliminate the issues?
• How do we train our people in the new processes to remove waste?
• Are our people capable and competent to do the new job we want?

This is where the close relationship comes into a project that balances changing a process and giving people the skills and competency to do the new job.

Most managers have risen to the level because they were good supervisors, who were good operatives. They are skilled at fighting fires and getting product out of the door. They are not skilled in problem solving, managing people, understanding KPIs, challenge, change management etc..

Hence we need a competency framework to assess people against.

We are a world away from Wisdom in most organisations we work with but the important thing is to get each organisation we work with on that journey.??

Want to know more? DM me here on LinkedIn, email me at [email protected] or call me on 07860 925712.

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