The impact of the applied technology on health and productivity in manual “picker-to-part” systems

Abstract

BACKGROUND:

Manual “picker-to-part” order picking takes place in a labour-intensive and time-consuming working environment where humans are the central actors and co-determine the effectiveness and efficiency of the process. Throughout Europe, work-related musculoskeletal disorders affect millions of workers, especially in the logistics sector, and cost employers billions of euros.

OBJECTIVE:

This paper studies how order pickers relate the use of technology as well as their relationship with the logistics company to their well-being, health and productivity.

METHODS:

To obtain data, a survey consisting of questions regarding work characteristics, health problems and the logistics company’s relationship with employees was conducted in Poland, Slovenia and Croatia.

RESULTS:

Workers who carry most items manually experience more health problems than cart and forklift users. The most common complaint is lower back pain – only 6% of order pickers (n = 221) never experienced it. The use of barcode or RFID scanner/terminal/smart phone correlates with more health problems than the use of other technologies. Participation in the selection of transport means or in training on health preservation can reduce the perceived health problems.

CONCLUSIONS:

Workers’ perception of the impact of the applied technology on health and productivity can differ from the impact that is calculated or measured. Through their relationship with employees, logistics companies can influence employees’ perception of their health problems.

Keywords

Picker-to-part productivity human factors work-related musculoskeletal disorders technology

1 Introduction

A key priority of the European Union’s employment strategy and Europe 2020 is to create more jobs in Europe, while improving their quality and ensuring better working conditions [1]. Maintaining adequate working ability of workers is of considerable importance for society; especially in sectors burdened with high prevalence of work-related diseases and health-issues [2]. Improvements are additionally necessary due to an aging population and the high costs of absenteeism. In the most critical position are jobs that require a large amount of physical work, constant repetition of certain movements and harmful postures. An example of such a demanding work environment is the manual “picker-to-part” order picking system in which order pickers retrieve items from their storage locations in a warehouse to fulfil customers’ orders [3, 4]. These activities are labour-intensive and time-consuming, and they account for more than 50% of warehouse operating costs [5 –7]. Order picking (OP) is the most expensive warehouse operation and is directly linked to customer satisfaction. Any wrong pick will lead to an unhappy customer and additional costs for the company. According to several authors [8, 9], more than 80% of all orders processed by warehouses are picked manually. Despite the automation of warehouse processes, a large share of orders will probably still be picked manually in the foreseeable future due to the low investment in infrastructure and equipment by startups and the superiority of human flexibility and ingenuity in unpredictable and urgent situations, dependent on personal cognitive and motor skills.

Manual order picking (MOP) has been intensively researched from the technical, technological and organizational perspectives [10]. Studies on the topic most frequently mention that the efficiency of MOP depends mainly on the demand pattern, the configuration (layout) of the warehouse, the storage strategy, the batching method, and the routing and sorting method [11, 12]. It is much less frequently mentioned that MOP system performance depends also on human characteristics and health, storage equipment design, forklifts’ characteristics and the applied information technology. These aspects are largely studied by ergonomics practitioners. It is indisputable that the human is the main performer of the OP process and therefore introduces to the process his/her unique combination of physical and personality traits. Even an optimal model becomes vulnerable when being operated by a human. For example, in order to reduce total picking time, academics proposed very complex batching techniques with near optimal results that consider both order size and product volumes [13], which were quickly dismissed by the subject firm because the logic for these batching methods was difficult to convey to the employees [14]. This and similar real cases should force researchers to reconsider their findings in the field of MOP with respect to the human factor [15]. As expected, researchers [4] have only recently started to consider that the human will always have an effect on the successful implementation of mathematically proven, optimal policies and layouts. Grosse, Glock, and Neumann [4] proved with a content analysis approach the existence of a clear research gap in considering human factors (HF) aspects in MOP planning models. They concluded that HF aspects are the missing link in OP system design to achieve long-term efficient OP processes with a reduced risk of occupational diseases.

The focus of this study is on picker-to-part MOP systems which require high productivity (measured by picks per time unit) while maintaining the well-being of skilled employees. Health maintenance is becoming an important topic due to the high amount of manual material handling required, often in awkward postures, which exposes employees to a high risk of developing one of the most common work-related ailments called work-related musculoskeletal disorders (WRMSDs) [4, 16]. Throughout Europe, they affect millions of workers and cost employers billions of euros. The importance of our research is exhibited in that we may notify some authors that considering HF aspects is not only relevant for reducing health risks, but that it may also significantly improve the performance and quality of order picking [3]. In practice, it is often understood that the productivity of employees in MOP systems and their well-being increase with the use of technology. For example, travel speed increases and fatigue due to walking is reduced thanks to the introduction of forklifts, but muscular fatigue increases due to the pulling of the pallet truck [17]. On the other hand, it is not known how order pickers relate the use of technology to their well-being and health. The content analysis showed that typical HF related keywords, such as “back”, “manual material handling”, “musculoskeletal disorders”, “fatigue”, “posture”, “pick-by-light”, “pick-by-voice”, “picklist”, “perception”, “information processing”, “ergonomics”, “human factors” were mentioned from 0 to 18 times in a literature sample of 98 works. In comparison, the keywords “routing”, “batching”, “zone” were mentioned more than 1,500 times. Research on the perceived correlations between the applied technology and health problems or productivity in picker-to-part MOP systems is relevant and original and contributes to the field of human factors in manual order picking. It also contributes to development of integrated workplace design tool, which includes methods for identifying MSD risks, obtaining user requirements, prioritizing user requirements, checking feasibility of design solutions, integrating the design process and recording of knowledge for improvements/future applications [18].

2 Literature review

Picker-to-part MOP systems are systems where the picking units of different shapes, weights, dimensions and colours are placed in fixed storage locations and the order picker walks to individual products according to the order list [19]. Compared to automated systems, they are cheaper at the implementation phase but more onerous for the employee and management. Two types of picker-to-part systems are distinguished: low-level picking and high-level picking [3]. Our research focus is on low-level picking systems in which the order picker picks requested items from storage racks or bins while travelling along the storage aisles. There is no need for vertical movements on the higher levels of the warehouse racks. Low-level, picker-to-part systems employing humans are increasingly labour intensive with up to 1,000 picks per person-hour and form the very large majority of picking systems in warehouses worldwide [8]. The human enters the MOP system with his/her unique anatomical, physiological and psychomotor characteristics, which relate to the worker’s physical activity and to a certain degree his adaptation to the current situation. Work in such flexible systems requires continuous mental processes, such as perception, memory, reasoning, and motor response, whose efficiency, through interactions between humans and other elements of the work system, influences productivity, quality and worker well-being. In the MOP system and its roughly predefined organizational structures, policies, and loose processes employees are faced with a low level of optimization (as compared to smart automated systems), therefore they are largely left to their own ingenuity and experimentation. Order pickers work in predefined MOP systems, which allow for a small degree of immediate improvisation and adaptation. According to De Koster et al. [8], MOP systems are predefined on the strategic (system characteristics) and policy level (OP organization and operational policies), with the possibility of increasing complexity at each level. The level of automation from manual to fully-automated, information availability from static to dynamic and warehouse dimensionality from vertical carousel to many aisles are those system characteristics, which are often considered at the design stage. Routing, storage, batching, zoning and order release mode are MOP organisation and operational policies, which form the soft part of MOP system design, because they can be changed during the warehouse life cycle. More than 90% of the literature on MOP planning models in connection to “outcomes” focuses on minimizing travel distance, total costs and throughput time, while less than 10% focuses on minimizing the risk of injury, maximizing occupational safety and improving working conditions [3]. According to the same source, 16.4% of the literature focuses on read/picklist and only 6.9% on other techniques like pick-by-light/vision/voice. However, because paper is no longer the sole interface between the order picker and the data about tasks he or she has to perform, and walking between picking locations is not the dominant way of traveling, it is important to understand the influence of various technical and information communication equipment on workers’ health as it is a less researched area. In MOP systems, the type and characteristics of different technical and information communication equipment, along with unique personal HF aspects, have an effect on productivity, quality and employee health as order picking outcomes. Below we specify the technologies and means of transport and summarize the theoretical findings on their impact on health. Although technology can make order pickers more efficient, it can also lead to overlooking HF risks. One of the dimensions of so-called decent work and appropriate working conditions is also a perception of health and safety [20]. Perceptual awareness may be the most ancient mode of consciousness, since the sensory systems are evolutionarily old. Nothing in human experience is as rich and full of subtle details as the sensory world [20]. When this fact is used in workplace conditions, it refers to the perception of being protected from harm to physical health and having safe environmental conditions in the workplace [21]. Since manual order picking is physically and mentally very intense, human perception is even more interesting for research. Understanding of the workers’ and managers’ knowledge and perception of ergonomic issues can play a critical role to develop and implement effective ergonomic programs and policies [22].

2.1 Technical equipment in MOP systems

Order pickers spent approximately 50% of their time traveling between picking locations and 15% on picking [6]. Excessive and repetitive manual materials handling renders the OP process an environment that puts workers at risk to develop WRMSDs, among them back injuries, sprains, strains, and/or tears [23, 24]. Travelling and picking, the activities most often repeated by workers, can be performed by walking and manually or by use of various carts and forklifts. Different implementation alternatives require a different amount of walking, manual manipulation and pulling/pushing (Fig. 1). A cart in a warehouse environment is usually a kind of four-wheeled vehicle designed for transport and temporary storage, pulled or pushed usually by one person. The main advantage is in eliminating the need to collect and carry loads in the hands. Consequently, the order picker has free hands for other activities and reduces energy consumption. A hand pallet truck is used for the transportation of cargo on pallets. Although it still needs to be pushed and pulled, the load displacement between locations is eliminated. The need to push and pull is eliminated using an electric pallet truck whose traveling speed is limited by the walking speed. The order picker saves energy that he/she would otherwise use for pushing and pulling. Various types of electric forklifts (as shown in Fig. 1) on which order pickers can drive themselves additionally eliminate need for walking between picking locations and shorten the transportation activities because of increased travel speed.

Fig. 1

Various types of technical equipment and the estimated energy expenditures.

Garg, Chaffin, and Herrin [25] introduced a method for ergonomics evaluations of manual tasks. This method is based on the energy expenditure assessment of standard operations execution as a function of oxygen consumption. High workloads lead to a significant metabolic cost, causing risks health for the workers [24 , 26–28]. The formulation introduced by Garg et al. [25] estimates the energy expenditure of each single task execution, such as lowering or lifting an item, grasping an item from a certain distance, walking, or standing in a fixed position. The standing metabolic rate depends on the subject’s age, body weight and stature. For a 22-year-old male it is on average 1.89 Kcal/min. Total metabolic rate for a level walk increases by 3.39 to 5.51 Kcal/min, depending on walking speed.

The literature suggests that multifunctional, more technologically complex forklifts can cause certain health problems, despite the reduction of needed physical effort. A literature review showed that in the analysed papers 67% mention lower back pain (LBP), 25% discomfort, 17% neck problems and 8% spinal disorders, musculoskeletal disorders, shoulder and arm health problems, which may occur for drivers of forklifts [29]. Among them LBP is the most frequently mentioned and more prevalent amongst forklift drivers and driving postures in which the upper body is considerably twisted or bent forward. Forklift drivers have a more than twice greater risk of LBP than those not exposed to driving forklifts. The lower back, neck and shoulders were scientifically proven as the most affected body parts by musculoskeletal disorders.

In theory, the choice of technical equipment affects productivity and health and should, as such, be a part of comprehensive MOP system design. We conclude that order pickers should perceive the different impact of various types of technical equipment on their productivity and well-being because they require a different energy consumption and engage different parts of the body to perform the same tasks.

2.2 Information systems in MOP systems

An information system (IS) is an orderly, arranged and organized system that provides order pickers with all the necessary information to carry out picking and for the necessary decision-making. The basic activities of the IS are collecting, storing, processing and delivering information/results. Traditionally, these systems are based on paper documentation. Operatively they are time-consuming because of the need for handwriting, rewriting, copying, reading, physical delivery of documents to workers, etc. In line with technological development, order pickers are now often aided by advanced technological tools, which help them maximise picking performance and reduce the chance of errors [30]. The health aspect of this technological development is only slightly researched. Today, the most mature and widely used in practice technological tools (shown in Fig. 2) are:

order picking with paper picking list – a paper picking list specifies the storage location’s address of each type of item, the number of items to be picked, and sequence in which the items will be picked. An order picker collects the items from storage locations and transports them to a specific location;

order picking with electronic picking list – the order picker uses a hand terminal, tablet or smart phone with the possibility to view the picking list and scan barcodes or RFID codes. There is no need to collect the picking list in a remote location and to input data about the performed tasks in a computer, again in a remote location. Information is presented in text form;

pick-by-voice – the order picker gets auditory information through the headphones. He confirms executed tasks by speaking to a microphone. In this way, he has free hands. Minor delays occur partly due to waiting for the next information. The system does not have built-in route guidance to the next location. Searching activity is not optimized;

pick-by-light – alphanumeric displays are illuminated to guide the order picker to the right storage location and indicate the number of items to be picked. The order picker places the items in the container and confirms the activity, usually by pressing a button near the display. Displays continue to light up, directing the order picker to the next picking location.

Fig. 2

Various ISs and the estimated time consumption.

Order pickers spent approximately 10% of their time for setup and 20% for search [6]. “Setup” includes the preparation of technical equipment and getting information for picking. With the development of ISs it is evident that the time and travelled path can be drastically decreased by bringing information to the order picker. “Search” involves searching for a storage location, a specific item at that location, and the required number of items. The time spent and the path taken depend on the precision of routing, which can be done in different ways by the IS. Again, to increase productivity information should be brought to the order picker, preferably visualized. Information presented by light, voice, symbols instead of in text form are improved presentations of traditional paper textual pick lists. More complex and technologically supported MOP ISs tend to optimize the presentation of pick lists to improve setup, search, and pick times and accuracy [31]. In this way, research of correlations between elements of IS and productivity/errors/health becomes meaningful.

2.3 Health problems in MOP systems

An important issue reducing productivity and prolonging order retrieval time in MOP is worker fatigue, which has increasingly been viewed by society as a safety hazard [32]. Fatigue is not just a result of not enough sleep, but also results from a worker’s individual characteristics, work type or organization and the working environment. For example, long-lasting repetitive work movements, awkward postures and manual carrying and lifting of large, bulky and/or heavy objects can cause musculoskeletal injuries. Maintaining the same posture for extended periods causes excessive fatigue. Repetitive work, tasks with little variety and/or few events and confusing and/or missing information may lead to boredom and errors being made. Too high physical load may also cause excessive fatigue, especially in a hot environment. WRMSDs are the most often reported causes of absence from work [22] and account for over 52% of all work-related illnesses and more than 2% of the gross national product in the European Union [33], where low back disorders are the costliest of the musculoskeletal disorders [34]. For this reason and considering demographic changes and an increasing work lifetime, HF issues at work have gained importance. This is paralleled by legal initiatives in many countries, which lead to an increase in regulations that enforce occupational safety in logistics [3].

2.4 Accessing human perception

Grosse et al. [3] argued that HF are relevant in each order picking task, and that they have an impact on the MOP outcomes performance, quality and worker health. The Grosse et al. [4] content analysis of 98 journal papers for the identification of main research topics in the MOP literature revealed that HF are mostly neglected in OP planning models. “Ergonomics” and “human factors” were mentioned only 18 times in the whole sample. In the category of perceptual HF aspects, pick-by-light was mentioned 12 times, pick-by-vision 110 times, pick-by-voice 14 times and picklist 2 times. In the category of physical HF aspects, manual material handling and WRMSDs were mentioned most often, back pain was never mentioned, while fatigue was brought up 8 times. These topics are interesting but less often researched because the economic aspect is more appealing than the ergonomic one. The already rare studies on the mentioned subjects are based on mathematical models and measurements. Research on the perception of the impact of used technology on health and productivity in MOP systems is non-existent, although it could contribute to new insights that may also affect the economic aspect.

Perception is mostly defined as “the process of recognizing (being aware of, organizing, gathering and storing) and interpreting (binding to knowledge) sensory information” [3]. Several studies reveal that perceptions help in shaping a person’s goal [35]. Their primary purpose is to guide action [36]. They involve signals that go through the nervous system, which in turn result from physical or chemical stimulation of the sensory system [37]. Perception is not only the passive reception of these signals. Perception is also shaped by the recipient’s learning, memory, expectations, and attention [38, 39].

3 Methodology

3.1 Description of the research instrument and sample

We surveyed order pickers employed in randomly selected international logistics companies, classified under “Section H - traffic and warehousing”, in three above average logistics-friendly countries, namely, Slovenia, Croatia and Poland. According to the World Bank Group, Poland and Slovenia rank in the top quintile of the logistics performance index (LPI), and Croatia ranks in the second quintile of LPI, which ranks countries on six dimensions of trade logistics – including customs performance, infrastructure quality, and timeliness of shipments.

To ensure the questionnaire’s clarity and relevance as a survey instrument, two academics and one industry expert were asked to review it. Their input was used to develop the final questionnaire, which, excluding the demographics section, consisted of five sets of questions with sub-questions. Responses were provided in the form of a 1 to 5 scale. The meaning of the scale is adjusted according to the content of the questions.

The first question was: How often can your work be characterized the following way? We provided the respondents with 10 claims divided into three sets. The first set of 8 claims refers to technical equipment. Respondents were asked how often they use (1) only physical strength, (2) cart, (3) hand pallet truck, (4) electric pallet truck, (5) electric pallet truck with personal transport, (6) electric order picker with the possibility of lifting the operator and cargo, (7) electric order picker with the possibility to adjust the height of the storage surface on the pallet to the optimal height for manual disposal of items, and (8) order picker forklift for picking and transportation. The second 4 claims refer to the type of used identification and communication equipment. Respondents were asked how often they use (1) paper documentation and visual recognition of items, (2) barcode or RFID codes reader/terminal/tablet/smartphone, (3) pick-by-voice technology, and (4) pick-by-light technology. The third set of 5 claims refers to work ergonomics characteristics. Respondents were asked how often they perform harmful movements, known from ergonomics papers, namely, (1) lowering loads from a height above the respondent’s shoulders and head, (2) lifting loads from a height lower than the height of the respondent’s knee, (3) looking backwards while driving, (4) entering the forklift, and (5) walking between picking locations.

The second question concerns the health problems caused by order picking according to respondents. We prepared a list of nine health problems, which were found in the scientific literature and statistical reports, namely, pain in the lower back, neck pain, pain in the shoulders, pain in the hand muscles, pain in the leg muscles, pain in the wrists, loss of vision, swelling of the legs and mental fatigue. We asked respondents to mark the frequency of the listed problems.

In the third question, we investigate order pickers’ perception of the impact of the above-mentioned types of technical equipment and information systems on possible health problems.

In the fourth question, we investigate order pickers’ perception of the impact of the above-mentioned types of technical equipment and information systems on productivity.

Two hundred twenty-one respondents completed paper surveys between January and April 2018, 84.6% of them were male. Most of them were 31 to 40 years old (40.7%), 34.8% up to 30 years old, 19.9% from 41 to 50 years old and 4.5% more than 51 years old. Of the respondents, 47.5% finished secondary school, 33.5% had a higher education, 9.0% university and 10.0% primary school. Of the surveyed order pickers, 63.8% are employed in large logistics companies with more than 250 employees, 24.9% in medium logistics companies with 50 to 250 employees, 11.3% in small logistics companies with less than 5 employees. We mostly surveyed experienced order pickers who have worked at this position for 1 to 5 years (47.7%) or more than 5 years (37.3%). Only 15% of respondents have worked at this position for less than a year.

Statistical analyses were conducted using SPSS 21.0. Levels of significance were set to a P-value below 0.05 or 0.01. Owing to the small sample size and non-normally distributed data, we used nonparametric tests.

There are some limitations in the research. 221 respondents were included in the research in logistics companies in Poland, Slovenia and Croatia, all classified under “Section H - traffic and warehousing”. The sample is big enough to provide a decent research analysis, but of course results can be generalised only in these countries in this sector. One of the limitations is also that we only examined the perception of the effect on health and productivity in order picking system.

3.2 Research question and hypothesis

The main research question is: “How do order pickers in manual “picker-to-part” systems perceive the impact of the used technology on their health and productivity?” Since it is impossible to analyse all the applied technologies, we have decided to analyse only the ones already mentioned above in the theoretical part of this paper. MOP systems differ in transportation means (cart, hand pallet jack, electric walkie pallet jack, electric rider pallet jack, forklift with possibility of lifting the order picker, forklift with lifting cabin) and IT supporting systems (paper, barcode and RFID scanning, pick-by-light, pick-by-voice).

For answering the research question of this paper we formulated the following research hypotheses:

H1: There is a significant correlation between different types of transport means and health problems.

H2: There is a significant correlation between different types of IT support of the order picking process and health problems.

H3: The relationship between a logistics company and its order pickers can influence order pickers’ perception of their health problems.

4 Results

From a predefined set of technical equipment, respondents mostly selected that they carry picked items in their hands (average on a scale from 1 (never) to 5 (always) was 3.11) and walk between picking locations (3.27). Less frequently they use electric pallet trucks with the possibility of personal transport (2.53), hand pallet trucks (2.52), carts (2.43), electric pallet trucks without the possibility of personal transport (2.20), forklifts with the possibility of adjusting the height of the pallet (1.52), forklifts with the possibility to lift the driver (1.30), and order picker forklifts with a lifting cabin (1.23). The frequency of looking backwards during the ride (2.86) and when entering the forklift (2.85) is high.

From a predefined set of supporting IT solutions, respondents mostly selected that they use barcode or RFID scanner/terminal/smart phone (average on a scale from 1 (never) to 5 (always) was 3.81) and paper documents combined with visual identification (3.02). Less frequently they use newer technologies such as “pick-by-light” (1.30) and “pick-by-voice” (1.20).

Respondents more frequently lift loads from below knee height (3.40) than they lower loads from above shoulder and head height (2.88). Of the respondents, 50.3% of them mostly lift loads from 10 to 20 kg, 35.7% lift loads lighter than 10 kg, 12.6% lift loads above 20 kg, and only 1.5% never lift loads. Of the respondents, 48% mostly lower loads from 10 to 20 kg, 37.6% lower loads lighter than 10 kg, 6.9% lower loads above 20 kg, and 7.4% never lower loads.

Respondents perceive various health problems arising from everyday order picking activities (see Table 1). The most common health problem is LBP (average on a scale from 1 (never) to 5 (all the time) is 3.45), followed by mental fatigue (3.14), neck pain (3.14), pain in the shoulders (3.14), muscle pain in the legs (3.14) and arms (3.09), pain in the wrists (2.89), swelling of the legs (2.49), and decreased vision (2.35).

Table 1
Average of reported health problems on a scale from 1 (never present) to 5 (always present)

Health problems Average

Pain in the lower back 3.45

Neck pain 3.14

Pain in the shoulders 3.14

Muscle pain in the arms 3.09

Muscle pain in the legs 3.14

Pain in the wrists 2.89

Decreased vision 2.35

Swelling of the legs 2.49

Mental fatigue 3.14

Health problems	Average
Pain in the lower back	3.45
Neck pain	3.14
Pain in the shoulders	3.14
Muscle pain in the arms	3.09
Muscle pain in the legs	3.14
Pain in the wrists	2.89
Decreased vision	2.35
Swelling of the legs	2.49
Mental fatigue	3.14

Respondents were asked on a scale from 1 (no effect) to 5 (entirely positive impact) how would the predefined work characteristics, types of technical equipment and information systems impact their health problems and productivity (Table 2). All the proposals were rated between 3 (medium) and 4 (noticeable), regardless of the impact area. According to the respondents, the introduction of the “part-to-picker” method would greatly contribute to health maintenance (3.82) and increased productivity (3.67). Reduced lifting from below knee height (3.54) would contribute a little more to health maintenance than reduced lowering from above shoulder height (3.38) and both contribute almost the same amount to increased productivity (from below 3.25 and from above 3.24). The use of barcode or RFID scanner/terminal/smart phone and the use of a forklift with the ability to lift the worker to 1st level are the only proposals that contribute more to increased productivity (use of barcode/RFID scanner/terminal/smart phone 3.66 and use of forklift 3.27) than to health maintenance (use of barcode/RFID scanner/terminal/smart phone 3.49 and use of forklift 3.19). The possibility of driving a forklift (effect on health 3.52 and effect on productivity 3.42) contributes more to health maintenance and increased productivity than the use of a forklift with the ability to raise the pallet (effect on health 3.32 and effect on productivity 3.08).

Table 2

Proposed solutions and their effects on health and productivity

Proposed solutions	Effect on health	Effect on productivity
Reduced lowering from above shoulder height	3.38	3.24
Reduced lifting from below knee height	3.54	3.25
Use of a forklift with the ability to lift the worker to 1st level	3.19	3.08
Use of a forklift with the ability to raise the pallet	3.32	3.27
Possibility of driving a forklift	3.52	3.42
Use of barcode or RFID scanner/terminal/smart phone	3.49	3.66
Work according to the “part-to-picker” method	3.82	3.67

1 - no effect on health/productivity. 5 - entirely positive impact on health/productivity.

The research results (Table 3) show some interesting correlations between the used means of transport and health problems. Workers who mostly carry items in their hands experience all the listed health problems (LBP, neck pain, pain in the shoulders, muscle pain in the arms and legs, pain in the wrists, decreased vision, swelling of the legs and mental fatigue). All correlations are positive, moderate and can be generalised (significance less than 0.01). The situation is almost the same for people who use carts. The correlations are all positive but a bit weaker in comparison to those who carry by hand. On the other hand, some negative correlations are observed when respondents mostly use a forklift with the possibility of lifting them. They report less LBP, less pain in the wrists and less mental fatigue. Respondents who use an order picker forklift with a lifting cabin report less LBP, less pain in the shoulders, less muscle pain in the arms and less mental fatigue.

Table 3

Correlations between the used means of transportation and health problems

Means of transportation/health problems	Pain in the lower back	Neck pain	Pain in the shoulders	Muscle pain in the arms	Muscle pain in the legs	Pain in the wrists	Decreased vision	Swelling of the legs	Mental fatigue
Walking	0.292**	0.308**	0.320**	0.314**	0.195**	0.198**	0.224**	0.235**	0.205**
Cart	0.206**	0.213**	0.250**	0.213**	0.229**	0.201**	0.249**	0.305**	0.264**
Hand pallet jack									–0.135*
Electric rider pallet jack	0.163*				0.141*				0.153*
Forklift with the possibility of lifting the order picker	–0.172*					–0.137*			–0.164*
Order picker forklift with lifting cabin	–0.181**		–0.135*	–0.163*					–0.134*

*- p < 0.05. **- p < 0.01.

Furthermore, correlations between the type of used IT support solutions and health problems were examined (Table 4). The use of barcode or RFID scanner/terminal/smart phone correlates with all the proposed health problems except decreased vision. Correlations between the use of “pick-by-voice” and “pick-by-light” technologies and health problems are negative, which means that more frequent use of technology results in less pain in the shoulders, muscle pain in the arms, legs and wrists. Respondents who use the “pick-by-light” technology report less LBP and less mental fatigue. All correlations are positive, moderate and can be generalised (significance less than 0.05). Users of barcode or RFID scanners/terminals/smart phones significantly correlate reduced lifting of loads from below knee height (r_s = 0.168, p < 0.05), the use of barcode or RFID scanner/terminal/smart phone (r_s = 0.291, p < 0.01), and utilizing the “part-to-picker” method (r_s = 0.137, p < 0.05) with maintaining health. On the contrary, users of “pick-by-voice” technology believe that employing the “part-to-picker” method (r_s = -0.137, p < 0.05) negatively affects health. Of the respondents (n = 198) who use barcode or RFID scanners/terminals/smart phones 44% very often lift loads from below knee height. Among them, 50% lift loads between 10 and 20 kg and 29% loads lighter than 10 kg.

Table 4

Correlations between the used IC technology and health problems

ICT /health problems	Pain in the lower back	Neck pain	Pain in the shoulders	Muscle pain in the arms	Muscle pain in the legs	Pain in the wrists	Decreased vision	Swelling of the legs	Mental fatigue
Use of barcode or RFID scanner/terminal/smart phone	0.290**	0.249**	0.269**	0.232**	0.395**	0.265**		0.226**	0.265**
Use of “pick-by-voice” technology			–0.143*	–0.189**	–0.143*	–0.150*
Use of “pick-by-light” technology	–0.138*								–0.149*

*- p < 0.05. **- p < 0.01.

Table 5 shows several positive correlations between the frequency of lowering loads from above shoulder and head height and health problems, with the exception of decreased vision and mental fatigue. All correlations are moderate. The frequency of lifting loads below knee height correlates only with LBP and pain in the shoulders. According to the respondents, lowering loads is more problematic for health than lifting them; the latter is more common in their everyday work. Order pickers believe that the more frequently they walk between picking locations the more LBP, neck pain, pain in the shoulders, muscle pain in the arms and decreased vision they experience.

Table 5

Correlations between the type of order picking work and health problems

Work /health problems	Pain in the lower back	Neck pain	Pain in the shoulders	Muscle pain in the arms	Muscle pain in the legs	Pain in the wrists	Decreased vision	Swelling of the legs	Mental fatigue
Frequency of lowering loads from above shoulder and head height	0.319**	0.254**	0.310**	0.275**	0.334**	0.320**		0.216**
Frequency of lifting the load from below knee height	0.234**		0.183**
Frequency of walking between picking locations	0.219**	0.250**	0.252**	0.172*			0.159*

*- p < 0.05. **- p < 0.01.

Furthermore, we examined the correlations between the relationships that logistics companies have with respondents and their health problems (Table 6). Respondents who can participate in the selection of transport means report less muscle pain in the legs (r_s=-0,162, p < 0.05), pain in the wrists (r_s=-0.142, p < 0.05) and mental fatigue (r_s = -0.223, p < 0.01). Workers who are better educated about ergonomics and health preservation in their companies report less LBP (r_s = -0.229, p < 0.01), neck pain (r_s = -0.287, p < 0.01), pain in the shoulders (r_s = -0.181, p < 0.01) and wrists (r_s = -0.173, p < 0.05), muscle pain in the arms (r_s = -0.149, p < 0.05), and swelling of the legs (r_s = -0.236, p < 0.01).

Table 6

Correlations between worker/logistics company relationships and the used means of transportation

Worker /logistics company relationships/means of transportation	Walking	Cart	Hand pallet jack	Electric walkie jack pallet	Electric rider pallet jack	Forklift with the possibility of lifting the order picker	Forklift with the possibility of adjusting the height of the pallet	Order picker forklift with a lifting cabin
I can choose how I will perform order picking				0.167*		0.168*
I do not feel the need to replace the type of forklift used most often			–0.198**
I just use the available forklift		–0.138*			–0.212**	–0.146*
Organized daily recreational program in the workplace		0.200**				0.205*		0.144*

*- p < 0.05. **- p < 0.01.

If they could choose, workers would prefer to use an electric pallet jack behind which they have to walk (r_s = 0.167, p < 0.05) and a forklift with the possibility of lifting them to reach items (r_s = 0.168, p < 0.05). Workers who use a hand pallet truck felt the need to replace the type of forklift used most often (r_s = 0.198, p < 0.01).

We also examined the perceived effects of proposed technological, information and process solutions (Table 2) on (1) health and (2) productivity among users of different types of transportation means. Users of advanced forklifts (order picking forklift with mounted aerial platform for order picking above the ground / order picking forklift with the possibility of adjusting the height of the pallet for picked items / rack order picker with mounted aerial cab) do not statistically significantly correlate the proposed solutions with having an effect on health or productivity. Users of order picking forklifts with the possibility of adjusting the height of the pallet for picked items agree that reduced lowering of loads from above shoulder height (r_s=-0.159, p < 0.05) and reduced lifting of loads from below knee height (r_s=-0.144, p < 0.05) have a negative impact on health. Additionally, they agree that their forklift helps them be more productive (r_s = 0.175, p < 0.05). Users of electric rider pallet jack argue that reduced lowering of loads from above shoulder height (r_s = 0.147, p < 0.05), the use of a forklift with the ability to lift the order picker to the height of picking (r_s = 0.142, p < 0.05) and the possibility of driving a forklift (r_s = 0.216, p < 0.01) improve productivity. In their opinion, health could be maintained with the use of a forklift with the ability to raise the pallet to the golden zone area (r_s = 0.172, p < 0.05), the use of barcode or RFID scanner/terminal/smart phone (r_s = 0.254, p < 0.01), and with the introduction of the “part-to-picker” method (r_s = 0.179, p < 0.01). Users of electric walkie pallet jacks (r_s = 0.190, p < 0.05) and hand pallet jacks (r_s = 0.171, p < 0.05) agree with them in part, that the use of a forklift with the ability to lift the order picker to the height of picking improves productivity. Users of electric walkie pallet jacks (r_s = 0.176, p < 0.01) and hand pallet jacks (r_s = 0.168, p < 0.01) also agree with them in part, that the possibility of driving a forklift improves productivity. Users of electric walkie pallet jacks statistically significantly correlate the use of a forklift with the ability to “ a positive impact on health (r_s = 0.144, p < 0.05) and productivity (r_s = 0.161, p < 0.05). They are the only ones who argue that utilizing the “part-to-picker” method would improve productivity (r_s = 0.136, p < 0.05). Users of carts and manual workers agree that the use of a forklift with the ability to raise the pallet to the golden zone area would negatively affect health (r_s = 0.142; r_s = 0.151, p < 0.05). This belief could be prompted by the fact that the introduction of such forklifts would increase productivity and, due to the larger amount of work, health issues are more likely to occur. There were no significant correlations between the users of carts and manual workers and the belief that the use of a forklift with the ability to raise the pallet to the golden zone area increases productivity. Respondents who mostly carry loads in their hands statistically significantly correlate reduced lowering of loads from above shoulder height with a positive impact on health (r_s = 0.177, p < 0.01).

Order pickers who mostly walk between picking locations significantly correlate reduced lowering of loads from above shoulder height (r_s = 0.141, p < 0.05), reduced lifting of loads from below knee height (r_s = 0.200, p < 0.01), and the use of a forklift with the ability to raise the pallet to the golden zone area (r_s = 0.151, p < 0.05) with improved productivity. Respondents who frequently lift loads from below knee height believe that the reduction in the frequency of this practice would have a significant impact on raising productivity (r_s = 0.175, p < 0.05).

5 Discussion

In analysed MOP systems, 71% of respondents often walk between picking locations, of whom 17.7% always walk. Only 5.9% of respondents never walk between picking locations. Of the respondents, 62.6% often carry loads in their hands between picking locations, of whom 19.5% always do that. Only 11.3% of respondents never carry loads in their hands between picking locations. We closely analysed the respondents who carry loads in their hands as well as those who transport loads on carts. All of them walk between picking locations. Around 30% of them finished only primary school, 50% are younger than 30 and most of the rest is younger than 40. This may mean that younger workers are more frequently assigned to walking jobs. Although order pickers (at least according to theory) spent approximately 50% of working time traveling between picking locations, we did not observe any significant correlations between their perception of the effects of predefined work characteristics, types of technical equipment and information systems and their productivity and health. For example, the possibility of driving a forklift would be a very logical solution to improve their productivity and lower fatigue. However, that is not their opinion. On the other hand, they significantly correlate all the health problems proposed in Table 4 with the nature of their work, walking and carrying loads. Opposite, order pickers who use forklifts do not correlate their use with specific health problems, with the only exception being those who use an electric rider pallet jack. Its users report LBP, muscle pain in the legs and mental fatigue. Forklift drivers in general correlate the use of a forklift with decreased mental fatigue, except drivers of electric rider pallet jacks. Although the literature review revealed that LBP, discomfort, neck problems, spinal disorders, musculoskeletal disorders, shoulder and arm pain may occur in drivers of forklifts, only users of electric rider pallet jacks significantly correlate LBP, muscle pain in the legs and mental fatigue with the use of this specific type of forklift. For our sample, according to the perception of respondents, we cannot agree that users of forklifts experience LBP more often than those not exposed to driving forklifts.

The use of barcode or RFID scanner/terminal/smart phone is the most commonly used technology in MOP systems, followed by paper, “pick-by-light” and “pick-by-voice”. Respondents significantly correlate its use to all proposed health problems except decreased vision. Although they rarely use “pick-by-light” or “pick-by-voice,” they believe that the use of those technologies can have a more positive impact on their health.

The relationships that logistics companies have with their order pickers proved to be very important for workers’ perception of their health problems. Workers who can participate in, for example, the selection of transport means, report less muscle pain in the legs, pain in wrists and mental fatigue. Further, those who are better educated about ergonomics and health preservation in their companies report less LBP, neck pain, pain in the shoulders and wrists, muscle pain in the arms, and swelling of the legs.

6 Conclusions

Research has shown that workers’ perception of the impact of the applied technology on health and productivity in manual “picker-to-part” systems slightly differs from that which has been calculated or measured and presented in the rare scientific publications on HF in MOP systems thus far. Convinced that workers’ perception serves primarily to guide their actions, we see a great potential for logistics companies to improve their relationships with order pickers. They should openly discuss the type of technology used and available on the market, the health problems in their local working environment, preventive and curative care, as well as invite workers to participate in decision-making.

Although the study was carried out in three countries, we did not investigate the existence of possible differences between the countries, which, in addition to the above, remains a potential area for future research.

Conflict of interest

None to report.

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