Introduction

The aim of TIC 4.0 is to represent any reality in digital format. The way to represent an instantaneous reality (a frozen image of reality) is through the “status” without time dimension (position, speed, status) in a specific timestamp.
(e.g. at 06-10-2023 09:00 Straddle Carrier 01 is working bay 01A 02 01 and driving with speed of 10 km/h)

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TIC 4.0 aims to develop a proper KPI semantic data model that allows the industry to get to the most value.

The aim of this paper

This paper intends to create an understanding of the methodology of how any KPI (standard or exotic) may be calculated and represented using the TIC4.0 semantic. By using this semantic, the TIC4.0 user is empowered to create his/her own KPI which best suits the user’s requirements.
This paper does not intend to re-create existing KPIs such as “crane productivity” or “terminal throughput”.

However, it illustrates how to use TIC4.0 semantic to represent such common KPIs and provides examples with 5 simple and popular KPIs. This list of examples will be extended in future releases.

TIC 4.0 KPI Definition

KPI = Represents in a consolidated way a reality, during a period of time, filtered, grouped and split in such a way that allows the reader to understand it creating value.

TIC 4.0 KPI SEMANTIC

The KPI semantic defines the ontology to express a unique meaning of a KPI (consolidated reality) just in one “sentence” or long name. A KPI is always a Mathematical Formula that represents a dataset time series in a consolidated way. To get it, many attributes should be included in this sentence to consider all the dimensions of such consolidation. The most relevant are:

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KPI Elements: All the elements are used to define how the operator is represented. Starting by timeframe, filter, split, and bucket.

1º Example

Name: Average Moves (box per minute)

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Average Moves (box per hour) = 23.4

2º Example

Name: Average fuel consumption (liter per hour)

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Average fuel consumption (liter per hour) = 6.4

3º Example

without bucket, without filter per name

Name = CheckInOutPlanned

Datapoints: carriervisit@.cargovisit.checkin_or_checkout.counter.planned.box

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CheckInOutPlanned (box) = 3.167

with filter by name

Name = CheckInOutPlanned

Datapoints: carriervisit@.cargovisit.checkin_or_checkout.counter.planned.box

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CheckInOutPlanned (box) = 2.378

With bucket per name multiKPI

TABLE

Name = name | Datapoints: carriervisit@.carrier.name | Operator = none

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