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Data Management
Transport management

Tuesday, 03. March 2020

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How to get a grip on data quality in your scheduling

The automation of scheduling promises a number of advantages. I already reported on these in detail in my last blog post. However, reaping all these benefits is not a matter of course. On the one hand, it must be ensured beforehand that a number of important prerequisites are met, and on the other hand, the introduction of automated scheduling – like every process improvement and every software introduction – requires a considered and structured approach. Otherwise, disappointed expectations are inevitable. The design of the introduction process will be the subject of one of my next articles.

In today’s article, however, I would like to focus on what I consider to be the most important prerequisite for automating scheduling and shed light on the question: “Do you have all the data necessary for automated scheduling under control?” If you have to answer this question in the negative, it will be difficult to impossible to actually reap the benefits of automation. An old IT adage says: “Garbage in, garbage out”. To ensure that you are well prepared for the introduction of automated route planning, I will make a plea for data quality today.

Does your data reflect operational reality?

Automation always means that a machine supports humans in processes and decisions. However, the machine can only use the digitally available data. If this data does not reflect the operational reality or is of poor quality, even an automated result will be unsatisfactory. Automation then brings no advantage. On the contrary: if automatically calculated results or plans are followed, even though they are based on an unclean database, this can lead to significantly worse results.

Why is data quality an issue at all; after all, the data is already needed within the manual processes? The answer is as simple as it is obvious: manual processes always make use of a great deal of implicit knowledge on the part of the actors. A procedure along the lines of “What do you mean, the truck is 20 cm too short? That’s nonsense, the trucks are all longer than the data say; it has always worked so far”, a digital process cannot reproduce this. Nor should it. The widespread combination of incorrect data and manual correction by “gut feeling” should generally be replaced by correct data. For this reason, it is very important to give high priority to the quality of one’s own data. Unfortunately, failure to do so often leads to frustration among users and a rejection of new software tools.

The focus is on the following data in particular:

Order and article data

  • Do the specified time windows actually correspond to the customer’s preferences or is there implicit or empirical knowledge or are the real preferences possibly different or flexible? (Example: The appointment is in the afternoon, but the customer prefers to be delivered early in the morning).
  • Is the article data (capacity requirements, special properties, etc.) correctly available?

Location data

  • Are the opening hours known, up-to-date and correct?
  • Do we have correct and accurate address or localisation data?

Vehicle data

  • Are the vehicle master data and the capacities cleanly recorded?
  • Are relevant vehicle characteristics and aids (e.g. transportable forklift) recorded centrally?

It helps to realise that this data is actually needed anyway. With manual decisions, only each decision-maker uses his or her own version of the data – a combination of explicit data and gut feeling. With automated decisions, however, this data must all be available digitally in ONE version. An additional advantage of clear, digitally available data, which should not be underestimated, is that it makes all decisions clearly traceable. This makes subsequent evaluation much clearer. The effort to bring the data to a good quality level is therefore worthwhile anyway.

Bild: iStock (subtik)

What criteria do you actually use to make decisions in scheduling?

Closely related to data quality is the awareness of your own actual decision-making criteria and your own operational objectives. Of course you want to meet promised deadlines as much as possible, use your drivers efficiently, treat them fairly when distributing tours and, of course, keep the total distance to be driven as low as possible. But can you clearly determine which of these goals is most important to you? Can you also put these and possibly other goals in relation to each other? For example, how much delay is acceptable to you in order to save 100 km? In other words, can you define clear rules on the basis of which an automated decision can be made?

Is it transparent for me according to which criteria plans are drafted and decisions are (supposed to be) made in the scheduling?

Bild: iStock (gorodenkoff)

Don’t worry! If you cannot do this ad hoc, you are in good company. Explicitly defining decision criteria and setting different competing goals in relation to each other is quite a challenge. Very few users are able to do this off the cuff. Nevertheless, it is unfortunately necessary – after all, you want to receive automatically generated plans that precisely reflect your preferences.

Get clarity in advance about the decision rules followed by you or your dispatchers! Since route plans are created every day, they do exist. But they are partly hidden in the heads of your dispatchers. It is possible that different dispatchers apply different criteria – a good opportunity to explicitly define these together as far as possible and, above all, to standardise them.

Alternatively, you can still do this “exercise” during the introduction phase of a software system for automatic scheduling together with your software partner or consultant. In doing so, suitable decision criteria or a weighting of different target criteria can be worked out step by step, for example. Draw up an initial plan and evaluate it. Too many delays? Then give more weight to meeting deadlines! Of course, it is important that the chosen route planning software supports such an individual weighting of different objectives. By far not all of them do!

In the end, you gain transparency in this process – similar to data quality – about your actual decision criteria or your actual preferences regarding the objective. This is then also uniform and no longer differs from one dispatcher to the next.

The positive thing about this: once you have the transparency, you can also explicitly change your preference! If, for example, your customers are dissatisfied due to frequent schedule violations, you can now give a higher weighting to the adherence to schedules centrally, uniformly and explicitly, and thus better support your higher-level corporate goals in a direct way.

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Conclusion

Changing business processes and introducing software systems is almost always complex and challenging. This is especially true when complex decision-making processes are automated – as is the case with scheduling. But this does not mean that you should shy away from it. The advantages of automating scheduling are simply too significant for that. It does mean, however, that you should lay the necessary groundwork for automation and take it seriously. Get a grip on your data! Then the first big hurdle is overcome and you can start to reap all the benefits of scheduling automation.