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Abstract: Warehouse operations are very much dependent on manual works and hence, humans play critical roles. In order to have an excellent warehouse performance, it is imperative to address the human needs so that the errors can be minimised. Papers addressing human factors in warehouse operations are limited. The purpose of this paper is to develop an improvement model that can connect between warehouse performance, human errors in the process, and actions needed to improve the performance. We propose a two-stage deployment procedure, modified from the well-known quality function deployment (QFD). The first stage is to screen human errors that substantially affect warehouse performance. The second stage is to prioritise proposed improvement actions that could address most effectively the human errors. The model is applied to a warehouse operated by a logistics company that handles the storage and distribution of fast moving consumer goods.
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298 Int. J. Logistics Systems and Management, Vol. 27, No. 3, 2017
Copyright © 2017 Inderscience Enterprises Ltd.
Human errors in warehouse operations:
an improvement model
Parama Kartika Dewa
Department of Industrial Engineering,
Institut Teknologi Sepuluh Nopember,
Surabaya 60111, Indonesia
and
Department of Industrial Engineering,
Atma Jaya Yogyakarta University,
Yogyakarta, Indonesia
Email: paramadewa@mail.uajy.ac.id
I. Nyoman Pujawan* and Iwan Vanany
Department of Industrial Engineering,
Institut Teknologi Sepuluh Nopember,
Surabaya 60111, Indonesia
Email: pujawan@ie.its.ac.id
Email: vanany@ie.its.ac.id
*Corresponding author
Abstract: Warehouse operations are very much dependent on manual works
and hence, humans play critical roles. In order to have an excellent warehouse
performance, it is imperative to address the human needs so that the errors can
be minimised. Papers addressing human factors in warehouse operations are
limited. The purpose of this paper is to develop an improvement model that can
connect between warehouse performance, human errors in the process, and
actions needed to improve the performance. We propose a two-stage
deployment procedure, modified from the well-known quality function
deployment (QFD). The first stage is to screen human errors that substantially
affect warehouse performance. The second stage is to prioritise proposed
improvement actions that could address most effectively the human errors. The
model is applied to a warehouse operated by a logistics company that handles
the storage and distribution of fast moving consumer goods.
Keywords: human factors; improvement model; modified QFD; warehouse
operations.
Reference to this paper should be made as follows: Dewa, P.K., Pujawan, I.N.
and Vanany, I. (2017) ‘Human errors in warehouse operations: an improvement
model’, Int. J. Logistics Systems and Management, Vol. 27, No. 3, pp.298–317.
Biographical notes: Parama Kartika Dewa is a Doctoral candidate in the field
of Logistics and Supply Chain Engineering in the Department of Industrial
Engineering at the Institut Teknologi Sepuluh Nopember (ITS), Surabaya,
Indonesia. He received his Master degree in the Department of Industrial
Engineering and Management from the Institut Teknologi Bandung (ITB),
Indonesia. His research interests focus on operations supply chain management.
He is a Lecturer at Atma Jaya Yogyakarta University, Yogyakarta, Indonesia.
Human errors in warehouse operations: an improvement model 299
I. Nyoman Pujawan is a Professor of Supply Chain Engineering in the
Department of Industrial Engineering at the Sepuluh Nopember Institute of
Technology (ITS), Surabaya, Indonesia. He received his Bachelor of
Engineering in Industrial Engineering from ITS, Indonesia, Master of
Engineering in Industrial Engineering from Asian Institute of Technology
(AIT) Bangkok, Thailand, and PhD in Management Science from Lancaster
University, UK. He is a Certified Supply Chain Professional (CSCP) holder
from APICS, USA. He was a Lecturer in Operations Management at the
Manchester Business School, The University of Manchester, UK in 2003 to
2004. He has published 30 articles in various international journals including
the European Journal of Operational Research, International Journal of
Production Research, International Journal of Production Economics,
International Journal of Physical Distribution and Logistics Management,
Production Planning and Control, Business Process Management Journal,
among others.
Iwan Vanany is a Professor of Business Process Reengineering in the
Department of Industrial Engineerin at the Institut Teknologi Sepuluh
Nopember (ITS), Surabaya, Indonesia. He received his PhD degree from
Universiti Teknologi Malaysia (UTM) in Manufacturing and Industrial
Engineering and both Master’s and Bachelor’s degrees in Industrial
Engineering Department from ITS. His research interests are in the business
process reengineering/improvement, food supply chain management, and
operations management. He has published in International Journal of
Information System and Supply Chain Management, Meiji Business Journal,
and Supply Chain Forum: An International Journal. He teaches business
process reengineering, supply chain management, logistics management,
productions and planning control, and enterprise resources planning (ERP).
1 Introduction
Due to the increasing competition in today’s global market, business enterprises are
forced to improve their supply chains to reduce inventory cost and enhance customer
service levels (Wang and Shu, 2005). Warehouse decisions are important and require
close attention in supply chain (Routroy and Kodali, 2008). The warehouse, as one of the
links in a supply chain, plays a critical role in achieving excellent supply chain
performance, through mitigating variations in supply and demand (Ballestin et al., 2013,
Sainathuni et al., 2014) and increasing responsiveness in a cost effective way. In today’s
highly competitive business environment, a warehouse is not only storing inventory, but
also managing value adding activities (Sainathuni et al., 2014; Lam et al., 2015). This is
especially important in fast moving consumer goods (FMCG) industry where speed is
critical, while product variety tends to increase from time to time and thus adding
complexity to warehouse operations. In this industry, demand for promotional products is
also high and retailers often request warehouses to do such activities as repackaging,
product bundling, and labelling.
With the increase in product variety and the need to have more responsive supply
chain, warehouse workers are under high pressure to improve the speed and reliability of
the warehousing process. On the other hand, working conditions in warehouses often do
300 P.K. Dewa et al.
not support such needs. Lack of skills upgrading and inconvenient working conditions are
frequently encountered in warehouses. Warehouse workers constitute an occupational
group who perform a lot of strenuous material handling (Garg et al., 1986). A large part
of the working day of the warehouse worker was spent on loading and unloading of
trucks and picking orders (Braam et al., 1996; Burinskiene, 2010). On the other hand,
they have to maintain accurate record of inventory and thus it is important to design
convenient working conditions in warehouses to prevent various errors from happening.
Human productivity in a working system is influenced by their working environment
(Leaman, 1995; Oseland and Bartlett, 1999; Fitch, 2004; Leblebici, 2012; Village et al.,
2016). A comfortable working environment will influence human productivity (Oseland,
1999). The ergonomic discipline advocates the need to have humans working in a
comfortable condition, physically and mentally, to enable them to perform their job well
(McGuire and McLaren, 2009). Tools have to be suitable to their physical conditions,
temperature should be within comfortable ranges, noises should be under acceptable
level, and sufficient level of freedom should be given to operators to ensure that they can
exploit their capabilities. Brill (1992) suggested in his research that improvement in
physical condition increases 5%–10% of human productivity. Hence, it is imperative that
improvements in warehouse productivity should emphasise the needs for better human
skills and more human-friendly working conditions.
Human plays important roles in the warehouse operations both in physical activities
such as receiving, put away, storage, picking, and shipping as well as in non-physical
activities. Researchers estimated that about 80% of order picking in warehouse is done
manually (De Koster et al., 2007; Baker and Perotti, 2008; Napolitano, 2012) indicating
the importance of considering human aspects in warehouse operations. Various human
errors could happen in warehousing operations that in turn would affect the supply chain
performance. Errors in identifying product could end up in picking the wrong item and
lead to customer orders being wrongly fulfilled. Errors in placing items inside the
warehouse would ultimately lengthen the searching time during the picking process.
Various errors could also happen in the administrative activities, such as wrong item
numbers were selected during transaction input to the computerised system.
Various works on human aspects in the warehouse context have been conducted,
mostly in order picking area (see, for example, Grosse and Glock, 2015; Grosse et al.,
2015; Daria et al., 2015). Grosse and Glock (2015) studied the effect of learning in
manual order picking. The authors developed a model that incorporates workers learning
in the picking process. The results show that learning impacts the efficiency of order
picking and therefore in picking operations, workers learning should be taken into
account. Grosse et al. (2015) proposed a conceptual framework for integrating human
factors into planning models of order picking and they conjectured that doing so would
improve order picking productivity as well as workers welfare. Grosse et al. (2016)
proposed qualitative methods can be used to assess human factors in order picking. Daria
et al. (2015) presented a case study of ergonomic aspects in a warehouse picking
operations.
Other researchers suggest automation in some part of the processes and in warehouse
information system to reduce human errors. Rizzi and Zamboni (1999) proposed ERP
implementation, in addition to process redesign, to improve efficiency in manual
Human errors in warehouse operations: an improvement model 301
warehouse operations. Poon et al. (2009) suggested the use of RFID in order picking
process to improve warehouse performance. Mueck et al. (2005) proposed a so called
Augmented Reality application which adds virtual information in the employee working
space to support warehouse operations and hence could reduce human errors.
While humans play substantial roles in warehouse operations, we still lack of models
that can be used to identify frequent errors in warehouse operations and to select
appropriate actions to reduce the occurrence of such errors, the topic that this paper
addresses. The objective of this research was to develop an improvement model in the
context of warehouse operations. The basic idea is to reduce human errors that are
typically happening in warehouse operations. The model is developed with the use of
quality function deployment (QFD) logics, connecting between warehouse performance
and human errors. The model is useful to help the company identify and prioritise
intervention actions to reduce human errors and eventually to achieve the supply chain
objectives more effectively.
2 The model
In this section, we present a procedure adapted from the well-known QFD to prioritise
improvement actions. QFD has been very popular in the area of product development
where customer voices are identified and then connected to what is called technical
responses. Recently, it has also been applied to supply chain improvement (see, for
example, Tidwell and Sutterfield, 2012; Durga Prasad et al., 2014). In this paper, we
propose a two-stage procedure, following the idea of Pujawan and Geraldin (2009) that
used a similar approach for managing supply chain risk:
In the first stage, we establish relationships between supply chain performance
objectives and the types of human errors. The relationships are represented in the
scale of 0, 1, 3 and 9 which are normally used in the QFD model. In the context of
warehouse operations, for example, error in reading item number written in the
picking slip may result in the wrong item being delivered to the customer which
affects such performance indicator as ‘perfect order fulfilment’. Relationships do not
need to be one-to-one in the sense that one performance objective maybe affected by
more than one type of errors. Likewise, one type of error may affect more than one
performance objectives with the same or different degree of influence. The
step-by-step process of the first stage is described in Sub-Section 2.1.
In the second stage, we establish relationships between the error type and the
improvement actions. The objective is to prioritise improvement actions that can
maximise the improvements on the overall performance and yet cost effective to
implement. The step-by-step process of the second stage is described in
Sub-Section 2.2.
Detailed step by step process in the above two stages are presented below.
302 P.K. Dewa et al.
Table 1 The first phase, relating performance objectives and errors
Error type j or (ER
j
)
Business
processes
Performance
objectives (i)
Relative
importance
(g
i
) ER
1
ER
2
ER
3
ER
4
ER
5
… ER
j
Current
performance
(x
i
)
Desired
performance
(a
i
)
Improvement
ratio (u
i
)
Final
importance
rating (F
i
)
1 g
1
R
11
R
12
R
13
R
14
R
15
R
1j
Inbound
2 g
2
R
21
R
2j
3 g
3
R
31
R
3j
Value added
processing 4 g
4
R
41
R
4j
5 g
5
R
51
R
5j
Storage
6 g
6
R
61
R
6j
Outbound
i g
i
R
i1
R
i2
R
i3
R
i4
R
i5
R
ij
Frequency (FR
j
) FR
1
FR
2
FR
3
FR
4
FR
5
FR
j
Error type rating (RR
j
) RR
1
RR
2
RR
3
RR
4
RR
5
RR
j
Priority rank of error type (P
j
) P
1
P
2
P
3
P
4
P
5
P
j
Human errors in warehouse operations: an improvement model 303
2.1 The first phase
This first phase is developed through the following steps:
1 In the first step, we need to identify the performance objectives that exist in each
business process and then identify ‘error type’ that potentially hinder achieving each
performance objective. In Table 1, the performance objectives are put in the left
column while the error type (ERj) is placed on top row of Table 1. The Rij is a scale
measuring how much error type j is affecting performance objective i. At the bottom
row, the frequency of each error happening is recorded as FRj. Both Rij and FRj are
input to Table 1 whose values should be obtained from data, either through
brainstorming within a team or from analysing objective data. In addition, each
performance objective (i) has three attributes, namely relative importance (gi),
current performance (xi) and desired performance (ai). The ratio of the desired
performance to the current performance is usually referred to as improvement ratio
(ui), indicating how much improvement is expected over a certain period of time.
Relative importance, current performance and desired performance are all input data
that should be prepared to complete Table 1.
2 In the second step, we calculate the final importance rating (Fi) for each performance
objective. If gi is the relative importance and ui is the improvement ratio for each
performance objective then the final importance rating can be calculated as follows:
()
(
)
iii
F
gu= (1)
The relative importance (gi) for each performance objective is assessed by the
member of a group assigned to work in an improvement project. It is common to use
a scale to measure the importance but different users may use different scales to
measure the same thing (Chan and Wu, 2005). It is well recognised that valuation
involving perceptions are always subjective and thus imprecise. The linguistic terms
people use to express their judgements are vague in nature. When the estimated
coefficients of a system are not exact and vaguely disguised, then one suggested way
of handling it is through what is called fuzzy numbers (Zadeh, 1965; Gunasekaran
et al., 2006). Rajkumar and Malliga (2016) proposed fuzzy analytical hierarchy
process (AHP) to take into account inaccuracy of decision-making process when
selecting suppliers in sugar industry.
A rational approach to represent linguistic assessments is using fuzzy numbers so
that their vagueness can be captured. We can assign symmetrical triangular fuzzy
numbers (STFNs). STFN in the form of [a, c], is a special fuzzy set representing a
fuzzy concept ‘approximately b’ where b = (a + c) / 2 (Chan and Wu, 2005). It is
often convenient to work with STFN because they are useful in information
processing in a fuzzy environment (Liang and Wang, 1993). In addition, STFN are
the most utilised in fuzzy QFD (Chan and Wu, 2005). Triangular fuzzy numbers is
suggested to solve many practical and complex problems in a great amount of
literatures (Herrera et al., 2005; Liang et al., 2005). To operate these phase and make
relevant computations comparable, it is necessary to unify the various scales used in
these process. Chan and Wu (2005) provide two scales for measuring the relative
importance and measuring the current and targeted performance on their fuzzy QFD.
These scales are adopting nine-point corresponding STFNs. In this paper, we use the
304 P.K. Dewa et al.
following 1 to 9 scale for measuring the relative importance of the performance
objectives.
Very low low moderate importance high very high
||----------|----------||----------|----------||----------|----------||----------|----------||
1 2 345 6 7 89
[0,2][1,3] [2,4][3,5][4,6][5,7][6,8][7,9][8,10]
(2)
The same scale, but with different verbal interpretation, is used to measure the
current and desired performance of each performance objective:
Very poor poor neutral good very good
||----------|----------||----------|----------||----------|----------||----------|----------||
1 2 345 6 7 89
[0,2][1,3] [2,4][3,5][4,6][5,7][6,8][7,9][8,10]
(3)
The relative importance of performance objective (gi) is usually expressed as a set of
ratings that can be determined by letting the management reveal their perceptions on
the relative importance of the performance objective and then averaging their
perceptions.
Suppose that for performance objective i, decision maker p judges a relative
importance rating gip according to scale (2), where gip is one of the nine crisp number
or STFNs in scale (2). If there are P number of decision makers then the resulting
average relative importance rating for i is computed by:
()
12
1
,1,2,,.
P
iii ip ip
p
g
gg gP gPi I
=
=+++ = =
"" (4)
It should be noted that if STFNs are used following scale (2), then gi computed by
(4) is also STFN. For convenience, the relative importance rating of the I
performance objective can be described as an I-dimensional vector,
g = (g1, g2, …, gI).
The improvement ratio for each performance objective ui is determined by:
i
ii
a
u
x
= (5)
where ai is the desired performance for performance objective i and xi is the current
performance. Suppose that manager p supplies a rating xip on company performance
using scale (3), where xip is one of the nine crisp numbers or STFNs in scale (3), then
the rating of performance indicator is i given by:
()
12
1
,1,2,,
P
ip i i iP ip
p
x
xx xP xPi I
=
=+++ = =
"" (6)
Assume that for performance indicators i, a proper target performance ai has been set
according to scale (3). Thus, the management has a target performance vector for
each performance objective, denoted as a = (a1, a2, …, aI). In most cases, each target
Human errors in warehouse operations: an improvement model 305
performance level should not be lower than the current performance level, implying
the need or desire for further improvement. It is obvious that the higher the
improvement ratio (ui), the more the company should work on the performance
objective, and thus the more important that performance objective is for the
company.
3 Identify error type and assess the frequency of occurrence (FRj). Useful ways of
conducting this kind of identification are via individual interviews and the use of
historical data. The error event (ERj) is placed on top row of Table 1. We use a
1–10 scale to judge the frequency of occurrence of each error type, where 1 means
almost never occurred, 5 means moderate, and 10 means almost certain to happen
(Shahin, 2004).
4 Develop a relationship matrix between each performance objective (i) and each error
type (j), Rij. This represents what error type could technically relate to and influence
the achievement of performance objective. The value of are {0, 1, 3, 9} where 0
represents no relationship while 1, 3 and 9 represent, respectively, low, moderate,
and high relationships.
5 Calculate the importance rating of each error type referred to as RRj. Higher RRj
value indicates higher priority for that error type to get treatment. The rating of error
is based on three factors, i.e., final importance rating of the affected performance
objectives, the relationship between the error type and performance objective, and
frequency of occurrence of the error type. Mathematically it can be expressed as:
j
jiij
i
RR FR F R=
(7)
where Fi is the final importance rating of performance objective i, Rij is relationship
value between performance objective i and the error type j, FRj is frequency of
occurrence of error type j.
2.2 The second phase
The purpose of the second phase is to prioritise improvement actions that can effectively
reduce the errors identified in the first phase. The most important input would be the list
of errors that has been selected as having top priority, i.e., those with relatively large RRj
values. The stages of the second phase are as follows:
1 The prioritised error type from phase 1 will become the input for this phase. Those
types of errors will be placed in the left side of Table 2 along with the corresponding
RRj values in the right column.
2 Identify improvement actions considered relevant for reducing each type of error.
Brainstorming would again one of the possible methods at this stage.
3 Develop a relationship matrix. A matrix contains the relationship between each error
type and each action, denoted by Ejk which reflects how effective an action k would
be in reducing the occurrence of the error type j. The values could be {0, 1, 3, 9}
which represents, respectively, no, low, moderate, and high level of relationships
between action k and error type j.
306 P.K. Dewa et al.
4 Calculate the total effectiveness of each action, represented by TEk which is given by
the equation (8). The higher the value TEk, the higher is the effectiveness of action k
to address errors:
kjjk
j
TE RR E k=∀
(8)
where TEk is the total effectiveness of each action k, RRj is the rating of error type j
and Ejk is the relationship between each error type j and action k.
5 Assess the degree of difficulties in performing each action, Dk, and put those values
in a row below the total effectiveness. The degree of difficulties, which can be
represented by a scale, should reflect the fund and other resources needed in doing
the action. The difficulty of performing each action is classified into three categories:
low with a score of 3, medium with a score of 4, and high with a score of 5.
6 Calculate the total effectiveness to difficulty (ETD) ratio, i.e., ETDk = TEk / Dk. The
ratio would indicate the cost effectiveness of each action.
7 Assign rank of priority to each action (Rk) where rank 1 is given to the action with
the highest ETDk.
Table 2 Second phase relating error types and actions
Actions to manage error (k)
Human aspect Environment aspect Tools aspect
No. Error type
(ERj)
H1 H
2 H
n E
1 E
2 E
n M
1 M
2 M
n
Error type
rating
(RRj)
1 ER1 E11 E12 E1n E11 E12 E1n E11 E12 E1n RR1
2 ER2 E21 E22 E2n E21 E22 E2n E21 E22 E2n RR2
3 ER3 E31 E32 E3n RR3
4 ER4 E41 E42 E4n RR4
5 ER5 E51 E52 E5n RR5
… …
i ERj Ej1 Ej2 Ejn Ej1 Ej2 Ejn Ej1 Ej2 Ejn RRj
Total
effectiveness
of action k
TE1 TE2 TE3 TE4 TE5 TE6 TE7 TE8 TEk
Degree of
difficulty in
performing
action k
D1 D2 D3 D4 D5 D6 D7 D8 Dk
Effectiveness
to difficulty
ratio
ETD1 ETD2 ETD3 ETD4ETD5ETD6 ETD7ETD8ETDk
Rank of
priority
R1 R2 R3 R4 R5 R6 R7 R8 Rk
Human errors in warehouse operations: an improvement model 307
3 Case study
3.1 Brief background of the company
A case study in this section is presented for the purpose of illustrating how the model can
be used in practice. This kind of model testing has been used in many earlier
publications, including for example Pujawan and Geraldin (2009) and Rajkumar and
Malliga (2016). We applied the above procedure to a large logistics company in
Indonesia that handles warehouse and distribution operations of finished goods for a
number of manufacturing companies. This company operates a 18-hectare fully
integrated logistics park. The scope of its business includes forwarding, storage, value
added and brokerage services. In this study, we focus on one of the warehouses that
handle the storage and distribution of FMCG produced by a major FMCG company in
Indonesia. There are four major stages of operations in this warehouse. First, is the
inbound process which includes unloading, receiving and inspection. Second, is storage
related activities. Third, is what they call as taping and shrinking which is basically the
process of rearranging the products in different packages to meet varying customer
requests. For warehouses handling FMCG this is an important process as many retailers
would require repackaging and relabeling of goods to suit custom needs such as
promotions and product bundling. The last stage is outbound logistics where goods are
prepared for customer delivery. As expected, the company deals with a large number of
orders from retailers on daily basis and the number of orders vary from week to week.
The third and the fourth weeks of each month are much busier that the other two weeks
as an anticipation for increased shopping intensity in the fourth and first weeks. The
majority of warehouse operations are done by manual workers using such material
handling equipment as hand pallets and forklift trucks. With the relatively limited number
and skills of warehouse employee, errors often occurred in various areas of the
warehousing processes.
For this study we had a quite intensive interaction with the case company. One of the
authors visited the warehouse about 20 times to familiarise with the process and the
problems. During the visit, various discussions and interviews were conducted, involving
the operations manager of the warehouse and the supervisors of each stage of the
processes. We identified the performance objectives and the errors typically happening in
the process. In addition we also collected some hard data that support application of the
procedures, including the current warehouse performance and the frequency of the error
occurrence. The study aims to obtain a list of recommendations to reduce errors in
warehouse operations which detriment the achievement of the company’s performance
objectives. In the following sub-sections, we will apply the above two-phase procedure.
3.2 Case illustration: the first phase
In the first phase, as outlined above, our objective was to prioritise handling of errors in
the warehousing process. This is done by first mapping the business processes in the case
company and then identifying the performance objectives as well as the errors frequently
happened which hiders the achievement of those objectives. We discussed with the
company representatives and observed the relevant documents. The performance
objectives identified for each business process are shown in the second column of
308 P.K. Dewa et al.
Table 3. The relative importance of each performance objective can be seen in the third
column. Following the procedure above, we also converted the relative importance of
each performance objective using fuzzy logics. Conversion results from the management
assessment into crisp and fuzzy values can be seen in the fourth and fifth columns. The
management assessment related to current performance achievement and targeted
performance achievement is in the sixth and eighth columns. Improvement ratios and
final importance rating for each performance objective were then calculated. The results
are placed in the ninth and tenth columns respectively.
The next step after calculating the final importance rating is to identify error that
could happen in each process. Combining the results of the interview and historical data,
the management identified eight errors that could have impacts on the performance
objectives. These eight errors are shown in the top part of Table 4. The relationships
between performance objectives and errors were rated by the management. As mentioned
above, the relationships take the value of 0, 1, 3, or 9 depending on the strength of each
relationship. For example, a value of relationship between ‘unloading cycle time’ (IP2)
and ‘errors in operating MH or other tools’ (ER6) is 9, indicating that the performance
objective IP2 is strongly affected by ER6. The complete relationships can be seen in
Table 4.
The rating of each error is determined by
1 frequency of occurrence of each error type
2 final importance rating of performance objective
3 the relationship between the performance objective and the error.
As an illustration, consider ER1 (errors in writing/recording data). The frequency of
occurrence for this error is 8 in the 1–10 scale. This error has a low correlation (scored 1)
with four performance objectives, each with degree of final importance rating of (7),
(9.8), (9) and (9), a moderate correlation (scored 3) with three performance objectives
with an associated final importance rating of (7), (12.6), and (9). Hence, the error rating
(RR) of this type of error is calculated, following equation 7, as follows:
[
]
18 1(7 9.8 9 9) 3(7 12.6 9) 964.8RR + ++ + + + =
As can be seen from Table 4, the calculated values range from 96.6 to 2,963.2. The
Pareto diagram of the error rating is shown in Figure 1. Figure 1 shows that the first two
error types contribute to about 50% of the total RR values and four error types contribute
to 75% of the total error rating values.
Figure 1 Pareto plot of error ratings (see online version for colours)
Human errors in warehouse operations: an improvement model 309
Table 3 The performance objectives and their relative importance, the case study
Business
processes Performance objectives (i)
Relative
importance
(g: in scale)
Relative
importance
(g: in crisp)
Fuzzy
g
Current
performance
(x
i
: in scale)
Current
performance
(x
j
: in crisp)
Desired
performance a
i
Improvement
ratios.
U
i
= (a
i
) / (x
i
)
Final
importance
rating
(F
i
) = (g
i
)(u
i
)
The amount trip is fulfilled (IP1)
M 5 [4, 6]
Neutral 5 7 1.40 7.0
Unloading cycle time (IP2)
H 7 [6, 8]
Good 7 7 1.00 7.0
Accuracy of lot number. Product code. quantity check (IP3)
H 7 [6, 8]
Good 7 7 1.00 7.0
Accuracy of pallet labelling (IP4)
H 7 [6, 8]
Neutral 5 7 1.40 9.8
Accuracy of the input data to the warehouse management system (IP5)
H 7 [6, 8]
Good 7 7 1.00 7.0
Inbound
Accuracy of pallet placement (IP6)
H 7 [6, 8]
Good 7 7 1.00 7.0
Cycle time of pre-taping/shrink (IP7)
H 7 [6, 8]
Neutral 5 7 1.40 9.8
Cycle time of taping (IP8)
VH 9 [8, 10]
Good 7 7 1.00 9.0
Cycle time of shrink (IP9)
M 5 [4, 6]
Neutral 5 7 1.40 7.0
Cycle time of storage in taping/shrink area (IP10)
H 7 [6, 8]
Good 7 7 1.00 7.0
Quantity for each operating table (IP11)
VH 9 [8, 10]
Good 7 7 1.00 9.0
Taping and
shrink
Labelling accuracy of product bundles (IP12)
VH 9 [8, 10]
Good 7 7 1.00 9.0
Cycle time of bundle product labelling (IP13)
H 7 [6, 8]
Good 7 7 1.00 7.0
Accuracy of inventory control (IP14)
VH 9 [8, 10]
Neutral 5 7 1.40 12.6
Forklift utilisation rate (IP15)
H 7 [6, 8]
Good 7 7 1.00 7.0
Storage
Accuracy of documents pick list and quantity calculation (IP16)
H 7 [6, 8]
Good 7 9 1.29 9.0
Accuracy of released product (IP17)
H 7 [6, 8]
Good 7 7 1.00 7.0
Accuracy of the physical condition of the container preparation (IP18)
M 5 [4, 6]
Good 7 7 1.00 5.0
Accuracy of cycle time to incorporate pallet container (IP19)
H 7 [6, 8]
Very good 9 9 1.00 7.0
Outbound
Accuracy of inventory status updates (IP20)
H 7 [6, 8]
Good 7 9 1.29 9.0
310 P.K. Dewa et al.
Table 4 Phase 1 deployment of performance objectives and errors for the case study
Errors in
writing/recording data
Errors in using/inputting
data
Errors in arranging items
Errors in identifying items
Error specifies the type of
tools/methods/prog.
Errors in operating MH
or other tools
Misunderstanding of the
data/procedure
Errors in executing
procedures/instructions
Performance objectives
ER1 ER2 ER3 ER4 ER5 ER6 ER7 ER8
Final importance
rating
The amount trip is fulfilled (IP1) IP1 1 3 7
Unloading cycle time (IP2) IP2 1 3 9 7
Accuracy of lot number, product code, quantity check (IP3) IP3 9 1 7
Accuracy of pallet labelling (IP4) IP4 1 3 9 9.8
Accuracy of the input data to the warehouse management system (IP5) IP5 3 9 3 3 7
Accuracy of pallet placement (IP6) IP6 3 3 1 9 7
Cycle time of pre-taping/shrink (IP7) IP7 1 3 1 9.8
Cycle time of taping (IP8) IP8 1 3 1 9
Cycle time of shrink (IP9) IP9 1 3 1 7
Cycle time of storage in taping/shrink area (IP10) IP10 1 3 3 7
Quantity for each operating table (IP11) IP11 9 1 1 9
Labelling accuracy of product bundles (IP12) IP12 1 3 3 9
Cycle time of bundle product labelling (IP13) IP13 3 3 7
Accuracy of inventory control (IP14) IP14 3 3 9 1 3 1 12.6
Forklift utilisation rate (IP15) IP15 1 3 7
Accuracy of documents pick list and quantity calculation (IP16) IP16 3 3 3 1 9
Accuracy of released product (IP17) IP17 1 3 9 7
Accuracy of the physical condition of the container preparation (IP18) IP18 3 3 9 5
Accuracy of cycle time to incorporat e pallet container (19) IP19 1 9 7
Accuracy of inventory status updates (IP20) IP20 1 3 3 3 9
FR 8 4 6 2 1 8 3 3
RR 964.8 900.8 630 758.8 96.6 2,963.2 616.8 451.8
P 2 3 5 4 8 1 6 7
Human errors in warehouse operations: an improvement model 311
Table 5 Phase 2 deployment of errors and actions for the case study
Human aspect Environment aspect Tools aspect
Training in data
management capabilities
Training to use the
equipments
Spreadsheet training for
data analysis
Programs for team
working
Reducing noise
disturbance
Add lighting
Add more air conditioners
Adjust shapes and
dimension of some tools
Training and empower
people to follow SOP
Pooling the placement of
equipments/tools
Error type H1 H2 H3 H4 E1 E2 E3 T1 T2 T3 RR
Errors in writing/recording data ER1 9 1 3 9 1 9 965
Errors in using/inputting data ER2 3 9 3 9 3 3 901
Errors in arranging items ER3 1 3 3 3 1 3 1 630
Errors in identifying items ER4 3 3 3 9 3 759
Error specifies the type of tools/methods/programs ER5 3 9 1 1 3 3 97
Errors in operating MH or other tools ER6 9 3 3 3 3 3 9 2,963
Misunderstanding of the data/procedure ER7 1 9 1 3 3 3 9 617
Errors in executing procedures/instructions ER8 9 3 1 3 3 1 9 3 452
TE 14,909 31,025 27,628 1,069 21,955 29,846 16,489 31,297 39,482 1,645
D 4 4 4 5 3 3 3 4 4 3
ETD 3,727 7,756 6,90 7 214 7,318 9,949 5,496 7,824 9,871 548
R 8 4 6 10 5 1 7 3 2 9
312 P.K. Dewa et al.
3.3 Case illustration: the second phase
In this phase, we first select the type of errors having large error ratings indicating that
these should be given top priority to be addressed. We then propose a number of actions
that could prevent those errors from happening. The subjective judgement on the
effectiveness of each action in addressing the error is also given in the form of a
relationship matrix, again here we are using the score of 0, 1, 3, and 9. The result of this
phase is presented in Table 5. As we can see, there are 10 proposed actions on top of
Table 5. The actions may address the skills of the employee, the appropriateness of tools
used, and the condition of working environments. As an example, the relationship
between ‘errors in writing/recording data’ (ER1) and ‘training in data management
capabilities’ (H1) was judged 9, indicating that improving the data management
capabilities (H1) is a highly effective action to reduce the error type ER1. The total
effectiveness of each action was calculated based on equation 8 and the results can be
seen at the bottom part of Table 5. The difficulty of performing each action is classified
into three categories: low, medium, and high with a score of 3, 4, or 5. The degree of
difficulty should also reflect the resources (human, money, and other resources) needed
to perform the corresponding action. Hence, the total ETD ratio indicates the cost
effectiveness of each action. The priority for each action (Rk) is placed based on the value
of the ETD ratio.
4 Discussions
This paper presents an improvement model and applies it to the context of warehouse
operations. The improvement model centres on reducing human errors that are typically
happening in warehouse operations. The focus is how to reduce the errors through a set of
actions which eventually is expected to improve the performance. We believe that in any
supply chain area, in particular in a warehouse, the performance is very much affected by
human factors. As suggested by Grosse and Glock (2015) warehouse is one of the areas
that still have significant potentials for improvements. The human hard skills such as the
skills to correctly identify and pick the right item, to correctly input the transaction into
the warehouse management systems, and to safely operate material handling equipment
are critical for warehouse operations, while the soft skills such as the ability to coordinate
with internal and external stakeholders to improve the flows of goods across the
warehouse are also critical for wider supply chain context.
Human in a working system acts as a planner and executor of the process to produce
results. On the other hand humans also have the potential to make mistakes because of
their limitations (Sanders and McCormick, 1993). Human performance is influenced by
the working environment (Geyer and Linner, 2005; Zheng et al., 2012) and equipment or
machine (Mital and Pennathur, 2004; Fereidunian et al., 2007; Ghobakhloo et al., 2011).
The proposed actions therefore address not only human skills, but also tools and the
working environment.
In the case study we identified 20 performance objectives specific to a warehouse
operations. In addition, we identified ten errors that are potentially occurring in the
warehouse operations. It is interesting to note that the inventory record accuracy was
considered the highest in terms of importance rating and then followed by the accuracy of
data labelling and the cycle time of pre-taping/shrink processes. The results suggest the
Human errors in warehouse operations: an improvement model 313
importance of reliability and speed in warehouse operations. Low inventory record
accuracy could result in wrong items being shipped to the customers or long order
fulfilment time as it may take a significant time to find the requested items. The issue of
inventory record accuracy has been recognised as a major problem in practice. Some
studies indicated that the inventory record accuracy in retailing industry tends to be very
low (seem for example, Kang and Gershwin, 2005; Raman et al., 2001). Inaccuracy in
inventory record is very often attributable to human errors. It is related to the problems in
reading documents and in identifying items. With the increase in product variety, the
potential errors increase. As also happening in the case company in this study, the
product variety increases from year to year and the inventory record accuracy is
considered as a major issue that needs serious attention.
The second phase relates the errors in the process and the actions (related to human,
tools, or environment). The idea is to find a set of cost-effective actions that could reduce
the errors occurrence. An action is considered cost-effective if it could reduce multiple
errors but can be performed without substantial challenges both in terms of resources
needed and technical difficulties involved. In Table 5, we present ten actions, four are
related to human and three for the environment and tools. It appears from Table 5 that the
two most cost-effective actions are to add lighting (E2) and training and empower people
to follow SOP (T2). Even though the results may be case specific, we strongly believe
that our work give a strong foundation for giving more emphasis on the human side of
warehouse operations.
The increasing importance of warehouse operations within a supply chain has been
recognised in the last decade. Warehouse operations very much affect the supply chain
responsiveness, reliability and costs. On the other hand, effective and efficient warehouse
operations are getting more and more challenging. With the increasing stock keeping
units (SKU) handled by warehouses, increasing demand fluctuation, shortening product
life cycle, and intensifying promotional activities, warehouse operations need more
sophisticated management approaches and technologies. Grosse et al. (2015) also suggest
the inherent flexibility of humans in handling changing jobs due to their combination of
cognitive and motoric skills which is absolutely important in handling non-routine works.
It is interesting to note that the proposed actions to reduce human errors in the
warehouse operations are not only related to skills of the warehouse operators, but also
related to temperature, noise, tools, and team-working. As the proposed actions were
mostly a result of discussions with the warehouse operations manager and the
supervisors, this indicates that they recognised those various factors as determinants for
effective warehouse operations. As suggested by previous authors, warehouse jobs are
often boring with relatively low appreciations. Many warehouses in countries like
Indonesia are operated under uncomfortable working conditions, making it challenging to
motivate workers to involve in improvements initiatives. Relatively high temperature
without air conditioners and noisy working area are common in many warehouses. Autry
and Daugherty (2003) and Min (2007) pointed out that the turnover rate of warehouse
workers was very high. Min (2007) also suggested that the high turnover rate is mostly
due to lack of human attention rather than monetary incentives. These indicate that efforts
are still needed to improve working condition in warehouses both physically and
mentally. With the fact that warehouse operations are very much dependent on manual
works, it is imperative to give more attention to human needs when designing and
operating warehouses. More future research works are obviously desirable in this area.
314 P.K. Dewa et al.
5 Conclusions
This paper presents a two-stage improvement model that is specifically designed to
handle human errors in warehouse operations. The proposed model is adapted from the
well-known QFD where in the first stage we relate between the performance objectives
and the error type with the aim of giving priority to each error type. In the second stage,
we relate the error type and the actions. The aim was to select a set of cost-effective
actions in order to reduce the human errors. The model can be used to help managers to
systematically improve the performance of an organisation (in this case the warehouse)
by reducing the errors that potentially occur in the related business process. While we
believe that human aspects are critical in warehouse operations, the discussion about this
issue has received very little attention in the literature. The lack of human factor
consideration in warehouse operations, especially in the picking process, was also
pointed out in a couple of publications, including for example in Grosse et al. (2015). Our
paper enriches the discussion about human aspects in warehouse operations, more
specifically on the improvement model that has received only limited attention so far. As
pointed out in the above discussion, warehouses are often operated under uncomfortable
condition and thus improvements to achieve better warehouse performance are important.
The case study suggests that process accuracy and speed are the two most dominant
performance objectives to achieve. This is in line with the current trend in supply chain
where reliability and speed are extremely important to achieve better customer
satisfaction. More specific results of the case study suggest that accuracy of inventory
record and accuracy of pallet labels are considered as the two most important objectives.
The results imply the need to design reliable and responsive warehouse systems. People
are clearly a central role in establishing reliable and responsive warehouse systems.
Initiatives to reduce errors in warehousing operations are absolutely important in the
attempt to improve reliability and speed.
There are a lot of important issues that require further investigation in the context of
human aspects in warehouse operations. One of the most important issues is the design of
working environments that leads to optimum human performance. What kind of working
environment that is specifically improves speed and reliability of warehouse operations?
This kind of research requires experimenting under different set of parameter values.
Another issue is the trend of using technology to replace human works in warehouses. As
it is not possible to remove all of human interventions, it is important to study the
optimum mix of technology and manual work under various different situations.
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