22th International Scientific Conference
Strategic Management and Decision Support Systems
in Strategic Management
May 19, 2017, Subotica, Republic of Serbia
University of Niš, Faculty of Economics
Niš, Republic of Serbia
University of Niš, Faculty of Economics
Niš, Republic of Serbia
IMPROVING ORDER-PICKING PROCESS THROUGH
IMPLEMENTATION OF WAREHOUSE MANAGEMENT SYSTEM
Abstract: For the purpose of timely response to requests of any participant in the supply chain, warehouse, as an
integral part of every logistics system, can be found at any place in the supply chain, between suppliers and customers.
In that sense, warehouse management involves the control and optimization of the complex warehouse and distribution
system. It might be said that warehousing and inventory management represent support to the production process and
strive to complete coordination in relations with all functions, such as marketing, finance, human resources etc.
Therefore, any disruption in coordination can cause serious problems throughout the whole business process.
When there is a need for achieving operational efficiency and cost savings, warehouse management and warehouse
operations are appropriate areas, in terms of achieving savings which will not jeopardize the quality of products and
services. Especially, order-picking, as part of the warehousing process, is one of the most important activities when it
is about providing savings. Order-picking process involves taking raw materials/products from a specific location in
the warehouse, for responding to requirements of production and/or customers. It is estimated that the costs of order-
picking participate with 55% in total cost of warehousing. However, elimination of this activity can increase the level
of dissatisfaction of partners in the supply chain, and, in that way, increase the cost of lost sales. In this regard, one
should not consider elimination of order-picking from the warehousing process as an option, but rather find a way to
increase its efficiency.
Due to their flexibility in the order-picking process, people cannot be fully replaced by machines and technologies.
However, equipment of orders-picking process by the adequate technology could increase efficiency of process and
productivity of employees in the warehouse. In that sense, warehouse management system (WMS) is an information
technology whose implementation has a aim to increase efficiency of processes performed in warehouse. Therefore,
the aim of the paper is to emphasize the importance of implementing a warehouse management system for improving
the order-picking process, as warehouse activity. In order to accomplish this aim, empirical research has been
conducted. A random sample of companies specialized for performing distribution activities has been chosen. Authors
of the paper have analysed which segment of order-picking process can achieve maximum benefits from
implementation of this technology, but also are there any limitations in terms of implementation of WMS. Based on
the statistical methods (descriptive statistics and cluster analyses), through the SPSS software package, the results
presented in the paper indicate the segments of order-picking process which are mostly improved by implementing the
WMS, as information technology.
Keywords: warehouse, process, warehouse management system, order-picking
Warehouse is very important for every company, especially for production and retail sector, but also for the whole
supply chain. Term warehouse is often mentioned in a negative context, as the cause of the high costs and waste of
time, without adding value to the product. Such understanding of warehouse and warehousing process is limited and
does not observe the key tasks of warehouse management, such as: reducing the warehouse cost and holding inventory,
increasing efficiency, increasing accuracy, increasing productivity while achieving greater value for customers and
higher levels of service quality (Richards, 2014, 5). According to the list of tasks one can conclude that warehousing
could be used as a source of competitive advantage.
Modern market conditions include increasing customer’s power, and their greater and different requirements in terms of
quality, but also faster and accurate delivery. Therefore, fast and accurately delivery could be used by companies and
supply chains as a way for increasing competitive advantage (Yu, 2008, 2). But, fast and accurate reaction of companies
or supply chains need appropriate warehouse system and capacity. Beside this, customer needs are characterized by
reduction of order sizes and increase of frequencies of order. For this reason, companies need to keep inventories on the
low level, with a possibility of variety of products for satisfying customer's needs. Also, warehouses and warehousing
process, in modern supply chains, are characterized by centralization of facilities (Christopher, 2016). This is the way
for reducing warehouse costs and decreasing inventory level. But, at the same time this is the challenge for
warehousing, in terms of providing higher level of service quality and larger product variety.
These trends are challenges for order-picking, as segment of warehousing process. Order-picking process implies
retrieving individual row materials and/or products from certain location at warehouse, with the purpose of fulfilling
customer orders. So, order-picking could be factor of fast and accurate delivery, and a high level of service quality.
Order-picking, as labour-intensive warehousing operation, involves checking the availability of raw materials and/or
products, assembling documents, defining the schedule for preparing orders and transportation. This operation could be
very capital-intensive in situation when warehouse is automated (De Koster, et al., 2007).
Costs of order-picking are result of the following activities: traveling (55%), searching (15%), extracting (10%) and
paperwork (20%) (Karasek, 2013, 115), and share of order-picking cost in total warehouse cost is about 55% (Fumi,
Scarabotti, Schiraldi, 2013; Tompkins et al., 2003). The high share of order-picking cost within the structure of
warehouse cost indicates that this segment of warehousing process could be used for increasing efficiency and
improving warehousing process. This is the reason why researchers and practitioners have recognized order-picking
process as part of warehousing, which need to be continuously improved.
Development of information technology and its implementation in warehouse, contribute that this logistics activity
becomes more competitive. In that sense, warehouse management system, as an information technology, could be used
for improving order-picking process, in context of minimizing cost and time for order-picking, and achieving higher
level of service quality and competitiveness.
2. THE IMPORTANCE OF IMPROVING ORDER-PICKING PROCESS
Warehouse operations are critical for each supply chain. According to some authors (Rouwenhorst et al., 2000), the
efficiency and effectiveness of the supply chain network depends from warehousing operations and its performances.
Through warehousing operations, supply chains are able to answer the ever changing market conditions and uncertainty
of demand fluctuations (Kim et al., 2013, 414).
High warehouse cost in total logistics cost indicates importance of managing and optimizing of warehousing process.
According to different researches, warehousing costs (operating and capital) amount about 23% of total logistics costs
in the United States (Baker, Canessa, 2009, 426), while in Europe these costs are 39% of total logistics costs (Fumi,
Scarabotti, Schiraldi, 2013).
Warehousing process includes receiving, putting away, storage, order-picking and despatching of raw
materials/products (Berg, Zijm, 1999; Kim et al., 2013). Order-picking is one of the most important activities in
warehouse. This warehouse activity includes retrieving raw materials and/or products from warehouse at the request of
customer (Moellera, 2011, 178; Tompkins et al. 2003; Đukić, et al., 2010) or presents a process of gathering raw
materials or products which are prepared according to some customer orders (Reif et al., 2010). Order-picking involves
defining a sequence of visiting the specific locations in warehouse space where each part of order is stored, according to
the model of travelling salesman (Daniels, et al., 1998). Also, this process could be defined as consolidation of one or
more ordered items.
Order-picking is the most laborious and the most costly activity in warehouse (Đukić, et al., 2010). This process has a
direct influence on speed of delivery, and on level of customer services. So, each company in the supply chain needs to
strive for reducing time of order-picking activity and for improving order-picking efficiency.
Importance of order-picking process derives from facts that this warehousing operation requires the most resources, and
is the most customer-sensitive (Miller, 2004). Importance of this process is greater because this is the last process
before delivering orders, so possible mistakes could have a great influence on quality of delivery, and future
relationships with customers and their satisfaction.
Designing of order-picking system is very complex task, because of close relationship between facilities, organizational
structure and information management (Hompel, Schmidt, 2007, 30). Manufacture's trends - smaller lot-sizes, point-of-
use delivery, postponement and customisation of product, reduction of cycle time, as well as distribution's trends -
accepting late orders, rapid and timely delivery have made order-picking more important and complex process (De
Koster, et al., 2007).
The main factors which determine efficiency of order-picking process are: demand for raw materials/products, the
warehouse layout, location of the items, the picking method in combination with the routing methods, experience and
knowledge of employees, as well as the level of automation of warehouse (Gattorna, 1997). Although the last one is
very important, sometimes companies, due to the high cost of order-picking process automation, are forced to use
manual operation at the expense of efficiency and time.
Experiences from practice have shown that half of warehouse costs arise from order-picking process (Tompkins et al.,
2003). The basic reasons for this situation are complexity and labour-intensity of order-picking process. Order-picking
depends from labour, and also cost and productivity of this operation. Completely automated warehouse and it’s
efficiently and accurately is dependent from labour (Miller, 2004).
The one of the major objectives of order-picking is maximizing the level of service quality by available resource
(labour, equipment, capital). Service quality level depends from a lot of factors as variation of order delivery time, order
integrity, and accuracy. Faster order-picking operations have influence on service quality level, because faster order of
retrieving means faster delivery to the customer. Also, minimising of the total cost is the one of the most important
objectives of order-picking process. Other objectives of order-picking process are (De Koster, et al., 2007):
Minimising the throughput time of an order;
Minimising the overall throughput time;
Maximise the use of space;
Maximise the use of equipment;
Maximise the use of labour;
Maximise the accessibility to all items.
Optimisation of order-picking process includes optimisation of duration of the following phases (Broulias, et al., 2005,
1. Travel time required for the picker to reach the pick point,
2. Search time required for the products to be found,
3. Retrieval time required for the products to be retrieved, and
4. Return time required for the picker to transport the products to the order point.
Different methods of order-picking, equipment or information technology could be used for improving order-picking
process. It is well known that implementation of Warehouse Management System (WMS) means integration in day-to-
day planning and controlling processes. This software system presents a great support to warehousing process. Before
WMS companies were using Inventory Control System. But WMS has greater results in terms of functionality and
optimisation routines (Moellera, 2011, 178). When its usage started, WMS was considered important for providing
information of materials/products warehouse location, but today this is a complex and advance technology with main
goal efficiently control of all items within the warehouse. The primary functions of WMS may be summarized in this
way (Inoday consultancy services pvt., 2016):
Minimizes the paperwork and write off;
Fully integrated tool – organization can make the connection between two different systems;
Picking, Packing and Shipping services;
Multi-Carrier Shipping Toolkit.
The basic purpose of WMS is managing of warehouse. Advantages of WMS could be used for keeping record of
warehouse capacity (location management), looking to stored units (inventory management) and optimizing warehouse
activities (Hompel, Schmidt, 2007, 46). WMS could be appropriate factor of improving productivity and efficiency of
all process in warehouse. Also, implementation of WMS contributes to planning and controlling order-picking process
with the purpose of increasing its productivity and optimisation. Implementation of WMS could be way for solving
following problems (Inoday consultancy services pvt., 2016):
Manually tasks and errors as well;
Late invoices and shipments;
Not proper information of inventory control;
Storage location of materials or products is not fixed.
Importance of implementation WMS is reflected on facilitating and speeding up of product tracing. Expectations from
implementation also include significant reduction of search time, which is over a 30% of total order-picking time.
(Broulias, et al., 2005). Implementation of WMS creates possibilities for developing green warehouse or distribution
center. For example, implementation of WMS creates opportunities for green warehouse by reducing paper
consumption. By implementation of WMS each company could reduce overall warehouse costs through the
optimisation of activities. Optimizing activities by using transportation equipment, according to WMS requirements,
could contribute to reducing energy consumption and CO2 emission.
It is true that a lot of elements of warehouse are designed before application WMS, as warehouse layout, selection of
handling and warehouse equipment, methods and procedures of order-picking process, and that could be a great
problem for later implementation WMS (Benson, 2013). However, WMS is not equally important and needed for all
companies. For example, WMS is particularly important for companies which sell their products by the Internet, or
serve a huge number of customers and/or consumers, or have a large number of disparate products in their assortment.
In the above mentioned examples, is more difficult to carry out order-picking process, and that is the reason why the
implementation of WMS is more important.
Table 1: Effects of WMS implementation on order-picking process
Increased data accuracy
Improved information sharing between
supply chain partners
Better determining of arrival and
Reduced material handling
Faster exception management
Supply and production continuity
Better customer services
Raw materials/Product related
Raw materials/Product tracking
Source: Kim et al., 2013, 414
Table 1 presents different indicators of benefits after implementation WMS. All indicators are classified at three groups
of benefits: informational, operational, and raw materials/product related benefits. With purpose of analyzing the WMS
implementation importance for order-picking authors have used the indicators from Table 1. For analyzing the
importance of implementation of WMS technology for order-picking process, the following hypotheses are defined:
H1: Implementation of WMS in the warehouse contributes to improving the order-picking process, in terms of
informational, operational and raw materials/products related benefits.
H2: Benefits of WMS implementation are more evident in the companies that have greater number of clients (customers
3. RESEARCH METODOLOGY
Analysis of benefits, as the results of WMS implementation, is necessary for justification of the improvement of order-
picking process. This analysis is important since order-picking process is significant factor of competitive advantage, in
sense of lead time and cost of order-picking. Long lead time of order-picking process influence the delivery delay,
which can be transferred through domino effect to other partners of supply chain. Also, high cost of order-picking is
factor of product price, and leads to consumer dissatisfaction, especially those which are price-oriented. Mentioned
problems may be solved through the information technology, such as WMS.
In order to test the research hypotheses, empirical research has been conducted in November and December 2016.
Companies specialized for warehouse activities were in the research focus, due to the fact that order-picking process
performs into warehouse. According to that, the survey questionnaires were sent to the managers of distribution centres
at the territory of the Republic of Serbia. The total number of sent questionnaires was 114, while the number of
responses was 34. Thus the response rate is 29.82%.
The sample includes 21 small and medium enterprises (SME) (62% of the sample) and 13 large companies (28% of the
sample), and considering the origin of the capital 8 companies have foreign origin of the capital (24%). Also, some of
the observed companies not have implemented the WMS yet (20% of the companies in the sample are those who have
not yet implemented the WMS). The questionnaire consists of two groups of questions. The first group is concerned by
general questions about the company (name and headquarters of the company, number of employees, the origin of
capital, legal form, revenue, and number of customers). The second part of the questionnaire is made up of specific
questions concerning the implementation of WMS within warehouses and assessment of benefits which are results of
WMS implementation. Respondents (warehouse managers) were asked to express their opinion and give marks
according to effects of implementation WMS on order-picking process (marks vary from 1 to 5, where 1 means the
lowest mark and 5 means the highest mark).
In the process of researching and hypotheses testing, authors used statistical methods. Beside the descriptive statistics
(mean value, standard deviation and variance) cluster analysis was used, for grouping objects of research in
homogeneous groups. Forming a group of objects should show high internal homogeneity or similarity within the
cluster and high external diversity or between clusters (Chakrapani, 2006, 59). Cluster analysis is used as an objective
methodology for classifying. Authors used a hierarchical method and for determining distances - the centroid method.
4. THE RESULTS AND DISCUSSION
The authors of the paper use descriptive statistics, as a way for assessing the benefits of implementation WMS in sense
of improving process of order-picking according to the surveyed managers. Descriptive statistics (Table 2) shows how
managers assess individual segments of order-picking after the implementation of WMS. The largest contribution of
WMS has been reflected in Lower inventory (4.8824). The implementation WMS was the least useful, according to
manager's opinion, for providing Reduced labour (1.6765). According to the managers, the number of employees has
not decreased since the employees are not assigned to other jobs. Among the benefits the best results from WMS
implementation are recorded for the group Raw materials/Product related benefits. The largest discrepancy between the
managers in terms of contribution of WMS exists in Better customer services (standard deviation is 1.34873), while
they agreed concerning Lower inventory (standard deviation is 0.32703). For all variables except for Better determining
of arrival and despatch times, Reduced material handling, Reduced labour and Reduced shrinkage average marks are
higher than 3. These results show that managers positively assessed the importance of WMS for improving the order-
picking process and recognized the benefits of implementation this software solution.
Table 2: Descriptive Statistics
N Mean Std.
Increased data accuracy 34 3.4412 1.07847 1.163
Improved information sharing between supply chain partners 34 3.0294 .96876 .939
Better determining of arrival and despatch times 34 2.5882 1.10420 1.219
Reduced material handling 34 2.9412 1.15316 1.330
Faster exception management 34 3.3824 1.18103 1.395
Quality control 34 3.7059 1.33778 1.790
Supply and production continuity 34 3.2353 1.12973 1.276
Better customer services 34 3.3824 1.34873 1.819
Reduced labour 34 1.6765 .76755 .589
Lower costs 34 3.8824 .97746 .955
Reduced shrinkage 34 2.9412 .95159 .906
Raw materials/Product tracking 34 4.4706 .61473 .378
Space utilisation 34 3.4412 1.02073 1.042
Reduced stockouts 34 3.6765 1.06517 1.135
Lower inventory 34 4.8824 .32703 .107
Valid N (listwise) 34
All companies from random sample are divided into two clusters by Cluster analysis. According to Table 3 and clusters
average marks it can be concluded that first cluster make up companies which have lower marks for benefits from the
WMS implementation in relation to the other cluster. After examining the affiliation of clusters, it can be seen that all
companies from first cluster belong to the SME category. This could be explained by the fact that SMEs, considering
limited resources, are not able to realize the full benefit from the implementation of WMS.
Table 3: Final Cluster Centers
Increased data accuracy 2.84 4.20
Improved information sharing between supply chain partners 2.84 3.27
Better determining of arrival and despatch times 2.00 3.33
Reduced material handling 2.32 3.73
Faster exception management 2.58 4.40
Quality control 2.79 4.87
Supply and production continuity 2.79 3.80
Better customer services 2.63 4.33
Reduced labour 1.53 1.87
Lower costs 3.63 4.20
Reduced shrinkage 2.53 3.47
Raw materials/Product tracking 4.42 4.53
Space utilisation 2.95 4.07
Reduced stockouts 2.95 4.60
Lower inventory 5.00 4.73
According to Table 4 first cluster has 19 companies from tested sample. Only two companies from group of the SME
has found into second cluster. By calculating the average marks of benefits from WMS implementation in large
companies and SMEs significant difference could be noticed. The average mark of improvement of order-picking
process by WMS implementation in large companies is 3.987615, while this result for SME's is 3.003175. These marks
are also confirmation of cluster analysis results.
Table 4: Number of Cases in each Cluster
In order to analyse the relationship between benefits from the implementation of WMS for improving order-picking
process and number of company's partners, authors used the Pearson Chi-Square test. Most of the companies in the first
cluster have a smaller number of users/partners (up to 10). Application of Pearson Chi-Square test showed a
relationship and justification of second hypotheses. The value of Pearson Chi-Square test (10.482) and p value less than
0.05 (p = 0.005) confirm the second hypothesis, that the benefits from WMS for improving order-picking process
depends from number of partners.
It is undoubtedly that process of order-picking is very significant in terms of contribution to competitiveness of
company. However, this part of warehouse could be used as a source of competitiveness in terms of providing a higher
level of service quality, but also in terms of minimizing costs. In the first case order-picking process is used for
customer's needs that are business oriented, and for the second case focus is on price-oriented customers. In any case,
the functioning of order-picking process depends on information technology, such as WMS.
Empirical research results show that managers confirm high contribution of WMS for improving order-picking process.
However, the results also show that 90% of companies from the first Cluster are SME. Taking into account that this
group of tested sample is limited in terms of lack of material and immaterial resources, it could be possible, that this is
the key reason why this group of companies could not get maximum benefits from WMS. Also, reasons could be
insufficient number of employees, employees that are inadequately trained for using WMS, lack of funds for
maintenance of the software, use of incomplete software solutions. Moreover, in some situations it is not justified to use
WMS. One such situation is a small number of partners, i.e. when the warehouse or distribution centre serves a small
number of users. In addition, case studies have shown that implementation of the WMS system does not give positive
results in all cases in large companies, especially if the hardware is not in accordance with the installed software. For
example, in 1993, Adidas tried to implement WMS (combination of WMS from two different producers). The system
just did not work (Supply Chain Digest, 2006, 6). In 1996, Adidas was able to respond only to 20% of total orders in
North America. For several months system was not able to reach its full speed. The results were huge losses of the
Company (Supply Chain Digest, 2006, 6).
Anyway, the research which results are presented in this paper could be observed as a pilot study. It points out the need
for further analysis of the importance and contribution of WMS to order-picking process and warehouse. In addition,
results of the research could be used as a basis for examining other factors which also could be limitation in terms of
providing the WMS benefits, such as serving one or a small number of markets, a narrow assortment for warehousing,
Baker, P., & Canessa, M. (2009) Warehouse design: a structured approach. European Journal of Operational Research,
Berg, J. P. V. D., & Zijm, W. H. M. (1999). Models for warehouse management: Classification and examples.
International Journal of Production Economics, 59, 519-528.
Broulias, G.P., Marcoulaki, E.C., Chondrocoukis, G.P., & Laios, L.G. (2005). Warehouse Management for Improved
Order Picking Performance: An Application Case Study From the Wood Industry. In C. Papadopoulos (ed.),
Proceedings of the 5th International Conference on Analysis of Manufacturing Systems – Production Management
(pp. 17-23). Zakynthos.
Chakrapani, Ch. (2006). Statistics in Market Research. London: Arnold Publishers.
Christopher, M. (2016). Logistics & Supply Chain Management (5th ed.). Financial Times.
Daniels, R. L.., Rummel, J. L., & Schantz, R. (1998). A model for warehouse order picking. European Journal of
Operational Research, 105, 1-17.
De Koster, R., Le-Duc, T., & Roodbergen, K.J. (2007). Design and control of warehouse order picking: a literature
review. European Journal of Operational Research, 182 (2), 481-501.
Đukić, G., Česnik, V., & Opetuk,. T. (2010). Order-picking Methods and Technologies for Greener Warehousing.
Strojarstvo, 52 (1), 23-31.
Fumi, A., Scarabotti, L., & Schiraldi, M. M. (2013). The Effect of Slot-Code Optimization in Warehouse Order Picking.
International Journal of Engineering Business Management, 5 (20), 1-10.
Gattorna, J. (1997). Handbook of Logistics and Distribution management (4th ed.). Gower Publisher Company.
Hompel, M., & Schmidt, T. (2007) Warehouse Management: Automation and Organisation of Warehouse and Order
Picking Systems. New York: Springer.
Inoday consultancy services pvt. Ltd. (2016). Web and software application development. Retrieved 2nd March 2017.
Karasek, J. (2013). An Overview of Warehouse Optimization. International Journal of Advances in Telecommunications.
Electrotechnics. Signals and Systems, 2 (3), 111-117.
Kim, K. M., Bahr, W., & Leung, S. C. H. (2013). RFID in the Warehouse: A Literature Analysis (1995-2010) of its
Applications. Benefits, Challenges and Future Trends. International Journal of Produciton Economics, 145 (1), 409-
Miller, A. (2004). Order Picking for the 21st Century: Voice vs. Scan Technology. Tompkins Associates. Retrieved from
Moellera, K. (2011). Increasing warehouse order picking performance by sequence optimization. Procedia Social and
Behavioral Sciences, 20, 177–185
Reif, R., Gunthner, W. A., Schwerdtfeger, B., & Klinker, G. (2010). Evaluation of an Augmented Reality Supported
Picking System under Practical Conditions. Computer Graphics Forum, 29 (1), 2-12.
Richards, G. (2014). Warehouse Management. London: KoganPage.
Rouwenhorst, B., Reuter, B., Stockrahm, V., Van Houtum, G. J., Mantel, R. J., & Zijm, W. H. M. (2000). Warehouse
design and control: Framework and literature review. European Journal of Operational Research, 122, 515-533.
Tompkins, A.T., White, J.A., Bozer, Y.A., & Tanchoco, J.M.A. (2003). Facilities Planning (3rd ed.). Hoboken: John Wiley
Yu, M. (2008). Enhancing Warehouse Performance by Efficient Order Picking. Rotterdam: Erasmus University.
Supply Chain Digest (January 2006). The 11 Greatest Supply Chain Disaster. Springboro. Retrieved from
Benson, D. (3th March 2013). WMS and Picking. We Are The Practitioners. Retrieved from