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How To Use Your Supply Chain Data

Clemence Chee, International Director of BI at Hello Fresh, explains

7Nov

All too often, I see various people erroneously use the terms 'reporting' and 'analysis’ as interchangeable terms or almost synonyms. This leads to a lot of cycles of spinning through data. Purpose, tasks, outputs, delivery and value are completely different. To prevent the belief that we 'took action from the data' just because we converted a spreadsheet into a chart, I would call out the clear distinction of the differences in terms of the purpose, tasks, outputs, delivery, and value.

Reporting is 'the process of organizing data into informational summaries in order to monitor how different areas of a business are performing.' Measuring core metrics and presenting them - whether in an email, a slidedeck, or online dashboard (tableau) - falls under this category.

Analytics is 'the process of exploring data and reports in order to extract meaningful insights, which can be used to better understand and improve business performance.'

I would like to break down three different ways on how to use data:

Operational Reporting

Within our operations and supply chain, we report data at a high frequency and, often, at a very granular level with a discretely defined role in a given process. A report of all items that have been ordered by a purchasing officer is one example (it’s only a good example if the process includes reviewing the purchasing order report each day and follow up with any issues). Another example is a customer care/call center report that breaks down wait times by different variables e.g. used for adjusting staffing throughout the week. Operational reports are also produced within the warehouse. It’s pretty simple.

Trouble arises when someone starts to repurpose such a report: 'I get a daily report of item spent and costs for delivery, so I’m just going to combine all of those into one view to see what my spent per region within the quarters are.' Same complication, but, '… I want to analyze margin/spent in per regions'. This winds up to be much more complicated and can create all sorts of issues with data interpretation and application. I’ll come back to that later.

Metrics Reporting

Metrics reporting has aggregated data: total numbers, total spent, sales conversion rate, etc. Key Performance Indicators (KPIs) are always metrics, but all metrics aren’t necessarily KPIs. There is a huge difference between metrics and operational reports - often a wrong applied usage of this understanding leads to a lot of unsolved business questions and wasted time/energy by:

  • Confusing metrics with analysis.
  • Starting with the data when determining metrics instead of starting with objectives.

The 'easy' way to get data quickly is to start out by asking what data is 'easily' available, and then choosing the metrics from that list. Yes, we are fast-moving, output-oriented and need results quick - but this is just wrong, wrong, WRONG! I can completely understand that it is very tempting to do and it is very easy to fall into that trap.

The correct way to start when determining metrics is with what we try to accomplish in business terms: 'We’re trying to grow', 'We’re trying to make a company more profitable' etc. This could be difficult to separate from data-term soundings. For instance, 'growing' the company might be the same as 'increase revenue', or maybe not. As a business and especially as an Analyst, we should be sure about our definitions, objectives and what we are really trying to accomplish.

Analysis

Besides architectural and technological work, Business Intelligence is a lot about Analysis. The major differences between metrics reporting is the purpose. While metrics reporting is measuring the performance of a department, a process, a project or a company - and knowing what actions to take if there is an alert or performance issue (e.g. in costs or errors) - analysis is digging into the main and root challenges and trying to figure out what’s going on with something with a hypothesis.

Without a clear hypothesis in analytics, results of findings can be even counterproductive and damaging.

  1. Have a clear hypothesis
  2. Have a clear action plan
  3. If you do not have any clear different actions if hypothesis is proven or not disproven, you are wasting time.
  4. Dumping all data into some fancy tool and see what if spits back out something useful, does not work.
  5. MVP even a hypothesis disproval model
  6. Get that data for the MVP - only that data module
  7. Run Analysis

Don’t get into scope-creep with more data, just because it’s there. It is a lot easier to sequence together a series then trying to waterfall. Stay agile!!

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