Cognitive Model of a Semi-Mechanized Picking Operation

Abstract

Introducción: Este documento desarrolla un enfoque pionero sobre los factores humanos de las operaciones semimecanizadas de selección, las cuales se abordan desde una perspectiva cognitiva. Materiales y métodos: El modelo aportado se establece a través de una metodología cualitativa, la cual, a partir de varias teorías, es articulada y aplicada a la situación real de una operación de selección (picking semi) mecanizada de una empresa de alimentos secos. Resultados: Los resultados combinan la arquitectura cognitiva de la operación y sus relaciones con factores logísticos, para disminuir el error humano y, por lo tanto, aumentar el nivel de servicio.

Full text

Cognitive Model of a Semi-
Mechanized Picking Operation
Modelo cognitivo de una operación
de recolección semi-mecanizada
Modelo cognitivo de uma operação
de colheita semimecanizada
Martha Caro, MSc*1;
Leonardo Quintana, PhD1;
Juan A. Castillo M., PhD2;
Christian Zea, MSc1
Received: October 12, 2017 / Accepted: February 5, 2018
Doi: http://dx.doi.org/10.12804/revistas.urosario.edu.co/revsalud/a.6841
To cite this article: Caro M, Quintana L, Castillo JA, Zea Ch. Cognitive Model of a Semi-Mechanized Picking Operation. Rev Cienc Salud.
2018; 16(especial): p. 39-51. Doi: http://dx.doi.org/10.12804/revistas.urosario.edu.co/revsalud/a.6841
Abstract
Introduction: The current paper develops a pioneering approach to the human factors of picking
operations, which are addressed from a cognitive perspective. Materials and methods: The model
thus contributed is established through a qualitative methodology which, based on several theories,
is articulated and applied to the real situation of a dry foods company’s semi mechanized picking
operation. Results: The results combine the cognitive architecture of the operation and its relations with
logistic factors, in order to decrease human error and, therefore, increase service level. Conclusion: the
current model provides elements for quantitative modeling, which could include this type of factors in
order to optimize the picking operation of a supply chain.
Keywords: cognitive systems, human factors, human error, ergonomics, picking operation, qualitative
analysis.
Resumen
Introducción: este documento desarrolla un enfoque pionero sobre los factores humanos de las operacio-
nes semi mecanizadas de selección, las cuales se abordan desde una perspectiva cognitiva. Materiales y
métodos: el modelo aportado establece a través de una metodología cualitativa y a partir de varias teorías,
es articulado y aplicado a la situación real de una operación de selección (picking semi) mecanizada de una
empresa de alimentos secos. Resultados: los resultados combinan la arquitectura cognitiva de la operación
y sus relaciones con factores logísticos, para disminuir el error humano y, por lo tanto, aumentar el nivel
1 Industrial Engineering Department, Pontificia Universidad Javeriana, Bogotá, Colombia.
* Corresponding author: mpcaro@javeriana.edu.co
2 School of Medicine and Heatlh Sciences, Del Rosario University, Bogotá, Colombia.
E-mail: juan.castillom@urosario.edu
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ARTÍCULOS DE INVESTIGACIÓN CLÍNICA O EXPERIMENTAL
39

de servicio. Conclusión: el modelo actual proporciona elementos para el modelado cuantitativo, el cual
incluye este tipo de factores para optimizar la operación de picking de una cadena de suministro.
Palabras clave: sistemas cognitivos, factores humanos, error humano, ergonomía, operación de selección,
análisis cualitativo.
Resumo
Introdução: Este documento desenvolve um enfoque pioneiro sobre os fatores humanos das operações
semimecanizadas de seleção, as quais se abordam desde uma perspectiva cognitiva. Materiais e méto-
dos: O modelo aportado estabelece através de uma metodologia qualitativa e a partir de várias teorias,
é articulado e aplicado à situação real de uma operação de seleção (picking) semimecanizada de uma
empresa de alimentos secos. Resultados: Os resultados combinam a arquitetura cognitiva da operação
e suas relações com fatores logísticos, para diminuir o erro humano e, portanto, aumentar o nível de
serviço. Conclusão: o modelo atual fornece elementos para modelagem quantitativa, que inclui este tipo
de fatores para otimizar a operação de picking de uma cadeia de suprimentos.
Palavras-chave: sistemas cognitivos, fatores humanos, erro humano, ergonomia, operação de seleção,
análise qualitativa.
Introduction
Cognitive ergonomics in supply chains has been studied from different points of view. For
example, Resnick et al. identied training techniques such as task analysis —which is
intended for improving decision making in global chains— through sophisticated meta-
cognition (1). Specically regarding picking operations, a study by Goomas et al. analyzed
the cognitive aspects of a voice picking system, in order to improve workers’ welfare and
attain better quality through error reduction (2).
These studies concluded that human factors, apart from physical aspects, should be
kept in mind to achieve both welfare and better performance. According to Kanji et al. (3),
human and organizational aspects should be studied by measuring individual performance
in terms of percentage of errors, paying special attention to work quality. In addition, human
factors have been involved in supply chain studies through mathematical optimization
models (4), implying variables such as inspection or operation errors and quality learning
level, which have elevated cost impacts (5, 6). However, these mathematical models have
not been based on real operation conditions, but on simulations. From the perspective of
information systems design (7), an experimental study for the validation of business process
management (BPM) systems was undertook, so as to identify cognitive resources applied
by the users to understand the software.
Furthermore, the performance of human factors within supply chains has been measured
in terms of quality (i.e., error) (2, 3, 4); time (1) and even learning (4, 8).
Picking represents about 55 % of supply chain operational costs (9), since it is the most
laborious and time consuming activity at a distribution center (10, 11). Picking eciency is
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Cognitive Model of a Semi-Mechanized Picking Operation
40

measured in terms of speed (12), and accuracy (whether the collected items match the picking
order). In other words, picking is signicant not only for the labor cost it represents, but also
through the effect it has on the service level agreed with the costumer (10).
Accuracy is currently considered to be a fundamental aspect of organizational success.
In labor-intense environments, accuracy has a direct relationship with picking human
errors, which, in turn, are caused by storage and picking methods, among other factors (10).
The picking operation, which makes use of tools such as paper list, scanner, laser and voice
technology, can be carried out in different ways such as picking from multiple positions or
doing multiple picks at each stop. Gu et al. say there is a direct connection between service
level and picking, especially regarding picker travel speed and product search through the
warehouse (13).
In turn, Grosse et al. (11) made an exhaustive literature review to identify those studies
that incorporated human factors in the planning of picking operations, with the further
goal of identifying those that might shelter research perspectives, by means of an adequate
conceptual framework specically developed for such purpose. In turn, worker features are
considered to be determining in operation outputs (14, 15).
The conceptual framework provided by (Grosse, Glock et al. 2014) (11) identies three
macro analytical perspectives: (i) modeling and planning, which comprises layout outlining,
warehouse assignment, routing and work organization. Its optimization has been studied
through mathematical modeling (4, 16, 17, 18); (ii) system outputs, which cover performance,
quality and worker health; and, nally, (iii) human factors, namely mental, perceptual,
physical and psychosocial. For their part (Grosse, Glock et al. state that picking systems have
been studied with two main goals: the improvement of effectiveness and eciency, and the
development of better conditions for workers (11). The latter contend that effectiveness and
eciency improvement are directly related to error reduction and productivity enhancement.
These approaches to human factors in the picking operation allow focusing on its mental
and perceptual aspects, which, in turn, constitute a new research domain featured by the
encounter of cognitive elements and picking human errors.
The way the picker receives the information and makes it useful is considered to be a
key perceptual aspect of the operation (11, 19). One of these perceptual aspects is the picking
list, which has not been studied in distribution centers. Yet, it has been analyzed in other
manual operations requiring paper picking lists, with the purpose of reducing information
search time. Grosse et al. identied design, content and distribution as important aspects of
the information conveyed by a picking list (11). Design is related to font size, which inuences
the easiness of reading and memorizing. Other aspects are color and spatial position, which
have been found to affect search time (20).
Other picking system design perspectives allow for a larger inuence of technology. Such
is the case of radiofrequency equipment linked to warehouse management systems involving
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Cognitive Model of a Semi-Mechanized Picking Operation
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barcodes, handhelds and scanners. Likewise, it was found that a shorter item code inuenced
search time [20]. In turn, Weaver et al. and Brynzér et al. included graphic layout information
and highlighted product amounts in the picking order list, thus reducing search time (19,
21). According to a literature review by Grosse et al. (11) and Bishu et al. (22) demonstrated
that reading order in shelves works better from left to right and from top to bottom; and
Gudehus et al. suggested the use of radiofrequency to facilitate information perception (23).
Picking systems are classied according to labor demand (10). Therefore, there are
manual, semi mechanized and mechanized systems. About 75 % of all picking systems
operate manually (10). The intense labor requirements of these dominant picking systems
make human factors outstandingly important (11), all the more because they determine the
effectiveness and eciency of the process.
Making up a lower fraction (7 %), semi mechanized systems operate in three different
ways: horizontal carrousel, vertical carrousel and stock picker. Among them, variations
such as the “pick to pass” system have lately gained momentum as a response to electronic
commerce and the need to be time-competitive. Pan et al. developed a Markov chain analysis
of one of these systems to estimate picker traveling distance to the picking zone. Finally, the
scarcest system type is the totally mechanized one (2 %) (18).
As to the mental aspects of picking, Grosse et al. described a relationship between an
elevated proportion of repetitive tasks, cognitive picking abilities, and workload (11).
Based on the available literature on the topic, the present work has developed a cognitive
model of the manual picking operation. Two steps were analyzed: information identication
and the actual product picking decision. Information identication is carried out through
picking lists, wherein design, content (11, 20), and distribution (19, 21), are cognitive aspects
inuencing speed and accuracy, as shown above.
Product picking decision has been observed to comprise two sub-steps, namely global
scan and position location (24, 25). These authors studied the performance of novice and
expert operators as affected by aspects like position and color. In turn, Bishu et al. conducted
studies related to shelf label orientation (20). In sum, the different studies available on the
cognitive elements that inuence picking have been developed in different domains and,
although they have been used as a reference framework for the present research, they do
not allow establishing a formal cognitive model for this operation.
Nonetheless, among the most important goals of a picking system operation are its
effectiveness and eciency, which are measured by speed and accuracy, the latter being
specically related to the percentage of picking errors. Information changes and warehouse
positions have been found to inuence quality in terms of error percentage, likewise (19),
they have detected some picking error causes: (i) mixing of several components at a given
location; (ii) the operator is interrupted; and (iii) similarities in the appearance of several
elements which are located close to one another. In turn, Grosse et al. found that the number
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of orders was inversely correlated to that of errors (26); and de Koster et al. described that
a clear route pattern reduces both error and time search (12).
Finally, Brynzér et al. have described radiofrequency and barcodes as error reduction
strategies. However, they do not guarantee error elimination because of the persistence
of the human vs. system interaction (19). Exploratory studies on the human dimension of
picking by Caro et al. had already identied a series of error types and their main causes (27).
In sum, new research avenues on the cognitive perspective have emerged with the study
of human factors in picking environments such as the postal or automotive industries, re-
sulting in conceptual frameworks that can be applied to areas that have not been addressed
from such perspective. And this is precisely the case of semi mechanized picking operations
in distribution centers.
Therefore, the present work actually opens a new research line through the development
of a cognitive model for the real semi-mechanized picking operation of a dry foods
distribution center. The model intends to identify the elements that are likely to cause
human error and decrease service level.
Materials and methods
The methodology used to develop this study is based on the concepts of distributed
cognitive systems (28), cognitive analysis in dynamic situations (29), and process tracing
for the study of cognition outside the laboratory (30) (Woods, 1993), all of which were used
to design the information collection and analysis protocol (g. 2).
The protocol follows four phases. The rst one corresponds to eld observation of the
natural ow of the operation by means of ethnographic techniques. This step allows iden-
tifying the characteristics of the operation and the ideal conditions for recording the task
through systematic videos. At this phase, it was possible to determine the analysis unit,
which corresponds to each one of the picking zones. Ten containers per picking zone were
adequately labeled with information that allowed tracing the origin of the products the
pickers had put in them (30). In the second phase, some videos were recorded, for them to
be analyzed during the third phase through a codication system designed according to theo-
retical guidelines by Hoc et al. (29). This codication protocol allowed identifying cognitive
architectures, which are made up of different cognitive activities and elements. In the fourth
phase, a guided verbalization on the part of the operators is elicited and contrasted to video
watching, seeking to conrm in depth the mental processes underlying their performance.
Furthermore, at this stage it was possible to inquire into the likely error causes. In sum,
the current methodology provides two main results, namely the cognitive model and the
cognitive elements that might inuence picking errors.
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ANALYSIS AND
CODIFICATION
GUIDED
VERBALIZATION
NATURAL
OBSERVATION
(Woods, 2003) (Woods, 1993) (Woods, 2003)
(Hoc & Amalberti,
1999)
1 2 3 4
VIDEOS
Information Collection and Analysis Protocol
Field observation
Figure 1. Materials and methods
The protocol shown in gure 1 was implemented at the distribution center of one of the
biggest food companies in Colombia, which makes use of a horizontal carrousel that travels
around nine picking zones. Six of them store high rotation products distributed in several
product families, each of which covers seven to ten meter length shelf segments. The other
three picking zones contain low rotation product families occupying about ten to sixteen
meter length segments each. Each picking zone is served by a logistic operator. There is a
warehouse management system that uses radiofrequency to send the picking orders (i.e.,
information on products to be picked, amounts, codes, descriptions and availability) to the
operators.
The study was carried out on a sample of 60 containers lled with approximately 622
picked products. The corresponding orders were dispatched by eight operators during two
days. The error ratio observed in this sample was 3 %, which coincides with the error ratio
of the whole picking operation at the time.
Results
The resulting cognitive model allows visualizing the variables of the picking operation. It
is described in terms of the tasks involved in picking, as well as the information, activities
and cognitive elements observed and conrmed with the operators.
The rst task is reading (Table 1), which is highly focused on the identication, on the part
of the operator, of the features of the products required in the order, namely shape, size, color
and position. In this case, the cognitive activity is information identication (31) or detection
(29). The operator establishes their mental representation (32), keeping in the operative memory
the information about the requested product: name, amount and weight; and recovering from
the large term memory the information related to product position and features.
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The second task is searching, wherein the operator walks to where the product is placed
on the shelf. In this task, the cognitive activity consists in verifying that the requested product
corresponds to the one identied on the shelf. The cognitive elements involved are attention,
spatial orientation and the decision to choose a product.
The third task is to actually pick the product, for which the operator uses the information
stored in their operative memory (quantity and weight) and in their long-term memory
(product features, amount and weight). This task consists in taking the requested product
from the shelf, based on its amount, weight and size. According to the observation, it is at
this point when errors are produced.
Table 1. Simple Cognitive Model of Picking Operation
SIMPLE COGNITIVE MODEL OF PICKING OPERATION
TASK INFORMATION COGNITIVE
ACTIVITY COGNITIVE ELEMENT
READ PRODUCT
FEATURES
Color
Size
Shape
Position
Identication
(Rasmussen)
Detecting
information
(Amalberti)
ATENTION
MENTAL
REPRESENTATION
OPERATIVE MEMORY
(Name, Quantity, Weight)
LONG TERM MEMOERY
(Position, Color, Size, Shape)
SEARCH
MENTAL
REPRESENTATION
(Varies according
to experience level)
SHELVES
POSITION
PRODUCT
MATERIAL CODE
VERIFICATION
ATTENTION
SPATIAL
ORIENTATION
EVALUATION
(Rasmussen)
DECISION
(Amalberti)
Information CHECK against
reality Shelf orientation:
Left to Right or Right to
left COMPARISON Mental
Representation against real
product DECISION on a
product and its amount
TAKE
THE
PRODUCT
OPERATIVE
MEMORY
Quantity and weight
EXECUTION
(Rasmussen)
ACTION
(Amalberti)
TRANSFER
TECHNIQUE
according to
product
QUANTITY
Color according to
the product SHAPE
Volume according
to weight
VOLUME AND WEIGHT
LONG TERM
MEMORY
Shape according
to product
This simple cognitive model resulted from the recording, analysis and codication of the
videos of the picking operation. A guided verbalization was contrasted to the analysis of 18
videos in which the errors were detected. Based on the comparison between ordered and
picked amounts, gure 2 illustrates error frequency. Sixty-one percent of the errors were
related to unsent products, while 39 % were associated to products sent in amounts that
exceeded those specied by the corresponding orders.
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Figure 2. Picking Errors
The guided verbalization was conducted through semi structured interviews in which, at
rst, the analyst conrmed with the operator the task described in the model. Then, they
watched the videos while the analyst asked in depth about the use of information in each
task. This phase allowed identifying the error causes with the collaboration of the operators
(table 2). Fifty ve percent of the causes were found to be directly related to memory.
Table 2. Error causes.
Cognitive Elements
Error sources
Confussion
Supercial
count
Interruption
Forgetfulness
Forgetting
while walking
to the place
Pending
Wrong
replenishment
The container
goes by
The operator
goes by the
product
Similar
locations
Attention 1 1
Attention, Wrong instruction to the system 1
Attention, lack of verication 1
Attention, lack of verication 1 1
Attention, spatial orientation 1
Counting numerous products 1
Lack of verication 1
Information, interpretation, attention 1
Information, memory 1
Memory while walking 1
Memory while walking; counting 1
Remembering two products while walking 1 2
Memory, lack of verication 2
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Interestingly, product search strategy differences were observed between novice and expert
operators, since the latter resort to their experience and knowledge of the picking area, while
novice operators use location coordinates. Hence, expert operators only need the name of
the product, since the rest of the information is stored in their long-term memory.
This is evident from the answers of the operators when asked about the order in which
they read and used the information during the picking operation. In effect, 60 % of the
operators (actually the most experienced ones) declared using the product’s description as
the informational key to start the process; while 40 % (the novices) conrmed using product
location for the same purpose.
The detailed description of the use of information by the operators, which is provided
by the simple cognitive model, allows observing that in the reading task both novices and
experts use the same data: product description, location and code. However, they resort to
different memory types, since the novice needs to read the radio frequency, while the expert
already keeps these features stored in their long-term memory.
Finally, when it comes to the task of actually picking the product, there are no differences
between novices and experts, since they are already in front of the shelf to take the product.
For such purpose, they only use their operative memory to remember the amount and weight
of the product in question. It was precisely at this point where most errors were committed,
since they were related to selecting wrong amounts (more or less than requested).
Although this simple cognitive model reveals some perceptual and mental aspects of the
picking operators’ performance, as well as differences between novices and experts, eld
observation also allowed observing relationships between them and their logistic context.
The variables that account for this relation are picking zone type (high rotation, low diver-
sity vs. low rotation, high diversity) and the combination of products to be picked, which is
featured by amount, weight and size.
Picking zone types, which are related to the product search task, are featured by dif-
ferent product storage modes according to demand behavior. The high rotation zone is
an approximately 10 meter long section where there are large amounts of a few products.
This implies both that the operator does not have to walk long distances to search and
pick a product, and that there are not many references to search through. In turn, the low
rotation zone, which is approximately 10 to 16 meter long, stores low amounts of many
different products.
Finally, the variable “combination of products to be picked” inuences the actual product
picking task. This is certainly a determining factor, since it is at this particular moment when
the operator selects a product and decides on its amount, thus actually making a right or
wrong choice. This variable is affected by three factors, namely product amount, weight and
volume. The hypothesis underlying this concept is that the larger the amount of a product,
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the higher its weight, volume and actual picking diculty, which, in turn, increases the
probability to commit a mistake.
As it is described below, a complex cognitive model of the picking operation results from
the inclusion of the additional relationships introduced above (g. 3).
Discussion and Further Research
The complex cognitive model of the picking operation highlights the way mental and per-
ceptual aspects can be affected by the conditions imposed by the logistic context of picking.
Figure 3 shows how “experience level”, “picking zone type” and the “combination of products
to be picked” are likely to inuence perceptual and mental aspects such as “information
employed” or “memorization mode” (storage of information in the operative or long term
memory), which nally affect attention at the moment of product selection and counting.
Figure 3. Complex Cognitive Model Based on the Integration of Some Cognitive Aspects and their Logistic Context.
The model above constitutes a qualitative approach, which should be validated through
further research in controlled scenarios, thus allowing the experimental study of the factors
involved [28], in order to determine their inuence on picking error.
From the current analysis it is clear that the picking operation constitutes an interaction
between human factors and their logistic context, which is, e.g., the one imposed by a semi
mechanized operation.
Understanding the cognitive and logistics parameters that are likely to inuence human
error opens new research avenues related to the design of training or information systems,
among others. These systems should include said parameters as inputs, in order to improve
operator performance and, therefore, the picking operation.
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Furthermore, the current model provides elements for quantitative modeling, which
could include this type of factors in order to optimize the picking operation of a supply chain.
This model constitutes a step forward in the study of the human factors of picking with-
in the framework introduced by (11), where they are highly important for the quality and
eciency of the operation.
Acknowledgements
The authors would like to thank Comercial Nutresa S.A.S. for their collaboration and un-
conditional support in the development of this project.
Disclaimer
The authors declare that they do not have any conict of interest. This study has not recei-
ved any nancial support apart from the internal resources from the executing institution.
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