Object properties determine performance in material flow

Object properties such as identification, dimension,
weight, and condition – are fundamental building blocks for the smooth and
efficient automation of material flow. Yet
not all attributes are static and constant; many vary throughout the intralogistical chain between goods receipt,
warehouse storage, and goods issue. High-performance logistics automation must
be able to generate this data throughout development. System solutions are
available for just this purpose – but their journey into the intralogistics process is only just beginning.

The variability of object properties is just one of
the challenges we face. Ambiguous master data and the detection, storage,
retrieval, and real-time provision of what are normally large data volumes
throughout the process also prove to be both costly and burdensome without the
appropriate technology. But at the same time, experience has shown us that
deviations from the expected condition may have huge negative ramifications for
logistical and automated processes.

What
happens when object properties change?

The intralogistics
lifecycle of goods normally begins at goods receipt and ends at goods issue. In
a very small number of cases, the object properties, and therefore the data
relevant to logistics, remain unaltered. Object data at goods receipt can be
either not known at all or known only at a basic level because of the preceding
electronic data interchange (EDI). Therefore, it either has to be entered as
new data or matched up and updated – and this process can exclude the part
number, which is necessary from a logistics point of view. The part number
usually remains the same; however, the
properties relevant to logistics and handling may undergo considerable changes.
As a result, storage sizes and containers cannot be used as planned, incorrect storage space suggestions can trigger
stock transfers, and shipping units can vary regarding
size and weight. It can also become critical when a company uses the handling
units of the supplier (e.g., shipping cartons) to
store goods in their warehouse. The condition of
the handling unit in terms of its
suitability for the conveyor system, storage capabilities in the automated
warehouse, stackability, and ease of handling,
are of fundamental importance. These
object attributes do not at first constitute the core master data of the
product in question, but they do add properties relevant to logistics and are
key to smooth processing regarding
storage and the conveyor system.

New
business models increase the intralogistics
complexity

E-commerce, multi-channel sales, cross-selling, and
far-reaching supply chains also increase the complexity of processes. This not only increases requirements regarding logistical performance: faster,
punctual, and more cost-efficient all at once. This trend also means that a
sharp increase in returned goods (e.g., from e-commerce) must be taken into
consideration when planning logistical systems. Moreover, influencing factors
such as batch size 1, the volatility of markets, flexible business models, demographic
change, and, increasingly, Industry 4.0 mean that logistics must transform
itself from a conventional field of business to one of the drivers of
innovation. This, in turn, requires even
more extensive, timely, and machine-readable a priori knowledge about object properties – particularly in intralogistics process stages in which master
data or object properties have previously been collected and updated in a
highly randomised way, meaning they are
not always up to date as a whole. Only constant and continual analysis of the
relevant master data can prevent logistical errors occurring in
high-performance material flow systems. Over the coming years, the degree of
automation will steadily increase as a result. Only then will the balance
between flexibility, throughput, and quality within a given budget guideline be
possible and become the benchmark for successful approaches to logistics. The
knowledge required for this to happen is incredibly difficult to obtain or to
generate in processes, but there are already suitable system solutions
available today that can tackle this challenge.

System
solutions for high-performance material flow

SICK offers a variety of system solutions – dubbed track-and-trace
systems – which enable the automated collection, plausibility evaluation, and
storage of product master data and object properties. This involves both static and dynamic solutions, e.g., for manual
and automated goods receipt processes. The systems also draw on state-of-the-art
technologies such as barcode scanners,
RFID, vision sensors, and multidimensional laser measurement technology for
identifying objects and determining their geometries, contours, overruns, and
weights. All systems are designed for ‘plug
& play’, easy operation, high reliability and availability, as well as
simple maintenance. Among other features, the solutions differ in terms of the minimum and maximum size and
weight of objects they can detect. In addition,
they may differ in terms of technology
depending on the surface condition of the objects. Some systems are also able to provide additional 2D-image or 3D-object
information in order to carry out analyses of data that are relevant to
logistics and handling. For example, regarding optimal gripping points for
robots, the geometric centres of containers, their suitability for the conveyor
system, and the occurrence of bulges, as well as container counting and optical
character recognition. With this information, the key logistical master
data can be updated in the ERP, MES, and
warehouse management systems. The logistics chain benefits from improved
processes such as those for storage location determination, packaging
suggestions, or forecasting shipping prices.

With the track-and-trace systems from SICK,
appropriate solutions ensuring consistent availability of master data are
available on the market today as state-of-the-art solutions. In the future, collection of object information in large data
centres along with modern capabilities for processing these large data volumes
virtually in real time will create entirely new concepts which will strike a
balance between efficient, high-performance material flow systems on the one
hand and maximum flexibility on the other, at an affordable cost.

Written
by Volker Glšckle and Bernd von Rosenberger, Automation Logistics Industry
Management, SICK AG, Waldkirch, Germany

Interview: Intralogistics – Perfect object data for
greater performance

Q: What intralogistical
processes will necessitate greater knowledge of the properties of objects in
the future?

Volker Glšckle:
Modern automated warehouses will have to evolve more and more into handling
centres in order to remain competitive
and flexible. In order to establish
reliable processes such as palletising unsorted pallets or “bin picking” as
added value in terms of logistics, it is
essential to produce information such as gripping points, stackability, or load
capability to a reliable standard of quality. This
will make further object attributes relevant, alongside the conventional master
data. In particular, when independent agents initiate machining, transport, and
further processing for automation
concepts within the context of Industry 4.0, this information becomes
significant.

Q: To what extent is it useful or necessary to use
such comprehensive object properties both internally and within a supply chain?

Bernd von
Rosenberger: Master data plays an important role in logistics handling at many
stages along a supply chain. In this respect, it is obviously desirable to have
consistent standards for storing and archiving object data even across company
boundaries so that this data can always be
accessed by each link in the supply chain, e.g., via the cloud. But in order to do this, it will be necessary to
establish rules regarding the origin, real-time provision, and alteration of
data, as well as standardization and
security of master data archives, among other things. Industry 4.0 can make an
important contribution in this regard,
since its processes are particularly reliant on data that is consistent,
secure, and quickly available.

Q: How will entering new object properties affect the
formation of intralogistical information
systems?

Volker Glšckle: In
order to manage the data of objects, it is normally allocated to them
using their part number, e.g., EAN. A query sent to the host system with this
identifier returns the desired data back
into the process. Particularly when part numbers are not unique, additional
identification technologies will be required in the future such as fingerprint
or matching methods, in addition to the barcode,
RFID, and vision sensors. The data storage of 2D and 3D information relating to
objects in large data centres and the possibility of processing these large
data volumes in real time will create entirely new approaches to logistics and
automation.

Q: Is this still a pipe dream, or has the future
already begun?

Bernd von
Rosenberger: Today, the awareness of object properties already enables smooth
automation and therefore higher quality of logistics, while optimizing process costs. This is based
on intelligent sensors and system solutions – already available today – which
collect the master data at the required standard of quality. So in one sense the future has already begun, but it
still offers a lot of potential for further automation of intralogistical tasks if we are able to develop technologies further and
make large data volumes manageable and usable in a secure way.

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