The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance.
A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators.
The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities.
The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes.
Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.
1. Introduction
The expanding globalization, the advancement of the world economy and developing industrialism of consumer orders have prompted an increased demand for logistics, transport, as well as warehousing services. The role of warehouses in the supply chain is very crucial because they contribute significantly to the storage of goods from the time of production until the goods are supplied to consumers on demand. In the highly competitive business environment of today, a warehouse is not just a place to store inventory but also to manage and operate value-added services (Kolinski and Sliwczynski, 2015).
Warehouses are constantly under pressure to increase productivity and accuracy while reducing costs and improving customer service. The measurement of productivity is the most significant dimension for warehousing to monitor the output from the input provided in warehouse operations. This study was prompted to revise the existing literature on warehouse productivity in view of several issues that have been raised regarding the measurement of warehouse productivity. The Asian Productivity Organization (2016) stated that instead of relying on their financial statements alone, small and medium enterprises should consider implementing a productivity measurement system (with value-added and logistics productivity indicators). According to Jacyna-Gołda (2015), the indicators that measure the efficient use of resources must be associated with the performance of tasks and functions to maximize the workload while minimizing the working cost. Johnson and McGinnis (2010) addressed the need to develop a valid model to account for resource inputs and service outputs, which will be useful in the conceptual design of new warehouses. In this context, the ratio method was introduced to measure the productivity of warehouse performance by using all the important warehouse resource inputs (capital and labour) and multiple service outputs (resulting from warehouse operations). Conclusively, productivity performance assessments in warehousing are significant to identify areas of improvement by using the resource inputs.
Hence, this study was aimed at revising the existing 10 warehouse productivity indicators (Staudt et al., 2015) that express the tasks within the work area into one model of warehousing productivity measurement indicators. Apparently, the performances of key metrics can be varied in many companies, but this study generalized the engagement of the results of significant resource inputs and outputs in every basic warehouse activity that can be easily accessible by any warehouse. By using an appropriate productivity performance measurement, together with the adoption of the latest technologies and innovations, a company can review and reward the productivity performance of its employees.
2. Literature review
2.1 Definition of warehouse
The terms “warehouse” and “distribution centre” are often used interchangeably, but there are a number of differences between the two, as illustrated by other studies. Dawe (1995) stated that a warehouse handles most products in four cycles, namely, receive, store, pick and ship; while, a distribution centre handles most products in two cycles, namely, receive and ship (De Koster et al., 2007). A warehouse is more than just a place where inventory is stored. The role of a warehouse in the supply chain network is essential because it provides temporary storage before the products reach the customers.
In fact, warehousing is integrally involved in four distinct supply chain processes, namely, sourcing/inbound logistics, processing/manufacturing, outbound distribution and reverse logistics (returns, recycling, etc.) Harrington (1998). Faber et al. (2013) defined warehouse management as a combination of planning, decision-making and controlling inbound, storage and outbound flows. Figure 1 illustrates the generic warehouse activities that are performed to some degree in all warehousing operations as it is possible that one or more of them do not exist in a given facility or are combined with other activities (Gourdin, 2006). Therefore, the word “warehousing” will be used throughout this study.
2.2 Previous studies on warehouse productivity indicators
Liviu et al. (2009) analyzed the performance of warehouses by adopting John M. Hill’s indicators, which are grouped into three categories, namely, “inventory management”, “warehouse performance” and “order fulfilment”. The respective authors also defined the measurement elements for the activities under each of these three category indicators. For instance, under “warehouse performance”, the indicators include four measurement elements, namely, orders per hour, items per hour, cost per order and cost as a percentage (per cent) of sales.
However, Kusrini et al. (2018) analyzed the performance of warehouses using the 25 key performance indicators (KPI) introduced by Frazelle (2002), which include productivity, financial, cycle time, quality and utilization, as shown in Table 1. In addition, Kusrini et al. (2018) found that the most important KPI for receiving is productivity, for putaway is cycle time, for storage is utilization, for order picking is cycle time and for shipping is productivity.
Warehouse key performance indicators
| Activity | Financial | Productivity | Utilization | Quality | Cycle time |
|---|---|---|---|---|---|
| Receiving | Receiving cost per line | Receipts per man-hour | % Dock door utilization | % Receipts processed accurately | Receipt processing time per receipt |
| Putaway | Putaway cost per line | Putaway per man-hour | % Utilization of putaway labour and equipment | % Perfect putaway | Putaway cycle time (per putaway) |
| Storage | Storage space cost per item | Inventory per square foot | % Locations and cube occupied | % Locations without inventory discrepancies | Inventory days on hand |
| Order picking | Picking cost per order line | Order lines picked per man-hour | % Utilization of picking labour and equipment | % Perfect picking lines | Order picking cycle time (per order) |
| Shipping | Shipping cost per customer order | Orders prepared for shipment per man-hour | % Utilization of shipping docks | % Perfect shipments | Warehouse order cycle time |
In addition, productivity is a subject that has been discussed for many years, and nowadays, there is a growing interest in productivity with regard to performance measurement. Usually, business entities are evaluated on their profitability and operational productivity. Warehouse productivity is measured by the ratio of multiple outputs relative to a specific input (Dowlatshahi, 2000; Kolinski and Sliwczynski, 2015). According to Stainer (1997), the term “productivity” is often ill-defined, but basically, its measurement is that of a prescribed output to the resources consumed and it can be divided into three main types:
Partial productivity is the ratio of output to a single input such as labour, materials or capital.
Total factor or value-added productivity is based on sales less bought-in goods, materials and services.
Total productivity is the ratio of total output to total input.
However, the National Council of Physical Distribution Management (1978) claims that not all the indicators commonly used in logistics to monitor productivity are strictly output and input measures as many relate more to utilization and efficiency. It also suggests that the measurements should be complete and more comparable, covering the use of all resources to be more valid in todays’ complex environment.
Warehouse resources consist of space, equipment and personnel (Pirttilä and Hautaniemi (1995); Hackman et al., 2001; Chow et al., 2006; Richards, 2011; Kłodawski et al., 2017). Operational productivity is usually measured as the number of units handled per person (De Vries et al., 2016). It is also inappropriate and inaccurate to call it labour productivity as this would be to neglect other resources, which can result in the performance output being seriously misleading. Therefore, Hackman et al. (2001) proposed a productivity benchmark, as shown in Figure 2, by considering all the warehouse resources and outputs from the warehouse operations.
Essentially, Staudt et al. (2015) provided a literature review from the dimension of 10 operational performance productivity indicators, namely, labour productivity throughput, shipping productivity, transport utilization, warehouse utilization, inventory space utilization, outbound space utilization, picking productivity, receiving productivity and turnover. Meanwhile, Frazelle (2002) defined five indicators under the productivity performance dimension (Table 1). In this context, a study conducted by Karim et al. (2018) adopted the 10 indicators of warehousing productivity introduced by Staudt et al. (2015) to rank the importance of the indicators affecting the failure of warehouse productivity operations. Besides, Laosirihongthong et al. (2018) found in their research that practitioners place more value in the performance resource utilization category on equipment utilization and labour productivity compared to space utilization. Conclusively, Table 2 illustrates the warehousing productivity measurement indicators proposed by Staudt et al. (2015), with modifications made by Karim and Abdul Rahman (2018).
Warehousing productivity measurement indicators
| Indicator | Definitions | Citation |
|---|---|---|
| Labour productivity | The ratio of the total number of items managed to the amount of item-handling working hours | Attar et al. (2012) |
| Throughput | Items/hour leaving the warehouse | Mentzer and Konrad (1991); Kiefer and Novack (1999); Altarazi and Ammouri (2018) |
| Shipping productivity | Total number of products shipped per time period | Mentzer and Konrad (1991); Kiefer and Novack (1999); Cagliano et al. (2014); Frazelle (2002) |
| Transport productivity | Vehicle fill rate | O’Neill et al. (2008); Matopoulos and Bourlakis (2010) |
| Warehouse utilization | The average amount of warehouse capacity used over a specific time | Rimiene (2008); Johnson and McGinnis (2010); Wang et al. (2010) |
| Inventory space utilization | The rate at which space is occupied for storage | Ramaa et al. (2011); Rizzi and Zamboni (1999); Frazelle (2002) |
| Outbound space utilization | Utilization of the area inside the warehouse used for retrieving, order picking, packing and shipping | Johnson et al. (2010) |
| Picking productivity | Total number of products picked per labour hour in picking activity | Kiefer and Novack (1999); Manikas and Terry (2009); Petersen et al. (2004); Frazelle (2002) |
| Receiving productivity | Number of vehicles unloaded per labour hour | Mentzer and Konrad (1991); Wang et al. (2010); Faber et al. (2002); Frazelle (2002) |
| Turnover | The ratio of the cost of goods sold to the average inventory | Johnson and McGinnis (2010); Yang and Chen (2012) |
Therefore, this study was conducted as an extension of the warehouse productivity measurement proposed by Staudt et al. (2015) and took into consideration other indicators within the work area. Two indicators were recognized in this study for the development of the warehouse productivity measurement model, namely, equipment utilization and putaway productivity. In addition, Staudt et al. (2015) stated that inbound indicators should be considered in future studies, as the inbound performance is as important as the outbound performance and will affect all the warehousing results. Putaway is very crucial as the decision in inbound operations is used to design the storage space (Lam et al., 2010) because of its importance with regard to the stock-keeping units and its direct impact on the picking process in outbound operations. Meanwhile, the equipment should also be considered in productivity performance, as all activities that are performed in the warehouse, especially the movement of goods within the warehouse, make use of internal transport (Govidaraj et al., 2000; Jacyna et al., 2015). The equipment used and the materials handled can be measured in various dimensions, including the cost incurred for advanced equipment and maintenance, the time consumed and the downtime (Staudt et al., 2015), as well as in terms of the efficiency (Pyza et al., 2011). Hence, this study also focussed on picking productivity and equipment utilization, which were considered for the productivity measurement indicators.
2.2.1 Equipment utilization.
Equipment can be categorized under vehicles, storage systems and conveyor systems (Hackman et al., 2001). Equipment utilization can be determined by the size of the equipment (material handling) required for a system. Equipment utilization has been extensively defined by several scholars as:
the average rate of utilization of mechanical equipment in logistics enterprises (He et al., 2017);
the given volume (or mass) of the load transported by given equipment or means of transport in a given unit of time (Pyza et al., 2011); and
the ratio of the capacity used to the maximum available capacity (Jothimani and Sarmah, 2014). Beamon (1998) summarized that the utilization value will be greater than one when the vehicle can carry multiple loads.
2.2.2 Putaway productivity.
Putaway, which is very crucial for the inbound process in warehouse operations, is a process that requires a strictly determined storage location (Karasek, 2013). The most important concern is the putaway strategy after the incoming items have been received. Therefore, Lam et al. (2010) stated that the putaway performance can be measured by the number of trips required and the travelling time for each trip after considering the potential labour resources and the means of internal transport used to move the items from the docks to the designated storage area. In addition, the putaway productivity can be measured as the putaway lines per man hour (Frazelle, 2002; and Lakmal and Wickramarachchi, 2011). Hence, the putaway activity is very crucial during the inbound process to provide a reference as to the availability of stock to customers.
Therefore, this study was aimed at revising the key performance metrics for improving warehousing productivity indicators in terms of ratio-based benchmarks for implementing a warehouse measurement system.
3. Methodology
Face-to-face and semi-structured interviews were conducted in this study. Both closed-ended questions and open-ended questions were used in the survey. For the closed-ended questions, the respondents were required to choose from an offered set of alternatives (Krosnick, 2018). As such, the proposed model for the ratio-based warehousing productivity performance indicators was determined by the degree of agreement on the classification of the resources, activities, indicators and definitions, respectively. By using closed-ended questions based on a five-point Likert scale ranging from “strongly disagree”, “disagree”, “neutral”, “agree” and “strongly agree”, data were collected from the respondents with regard to the proposed indicators (Joshi et al., 2015). This was followed by open-ended questions to provide opportunities for multiple answers (Sabilah and Manoy, 2018) and strategies for discovering new things without being influenced by the researchers (Reja et al., 2003). Thus, this research dealt with open-ended questions for comments and ideas for improving the proposed model.
3.1 Directed content analysis approach
Content analysis is a research technique for making replicable and valid inferences from texts or other meaningful matter within the context of their use to provide knowledge, representation of facts, new insights and a practical guide for action (Krippendorff, 1980). A content analysis is aimed at defining the characteristics of the contents of a document by considering who says what, to whom and with what effect (Bloor and Wood, 2006). According to Elo and Kyngas (2008), the purpose of a study determines the approach to be used for the content analysis that is whether the quantitative or qualitative data are to be used in an inductive or deductive way. In addition, the qualitative method typically involves the use of interviews and natural observations. Mayring (2014) mentioned that texts occur more often within social science, and the method involves analyzing the text materials themselves, including interview transcripts, focus groups, open questionnaires or observations and document analyzes. Meanwhile, there are three approaches to content analysis, namely, conventional, deductive and summative (Hsieh and Shannon, 2005; Hashemnezhad, 2015).
According to Hsieh and Shannon (2005), when an existing study is incomplete or would be of interest with a further explanation with the goal of validating or refining or extending a conceptually theoretical framework or theory, then a directed content analysis will be used. In a directed content analysis, as defined by Kyngas and Vanhanen (1999), the researcher uses a prior or existing theory research to develop the initial coding scheme before beginning to analyze the data. On top of that, Kaid (1989) stated that all approaches to a qualitative content analysis require a similar analytical process of seven steps, which are:
formulating the research questions to be answered;
selecting the sample to be analyzed;
defining the categories to be applied;
outlining the coding process and the coder training;
implementing the coding process;
determining the trustworthiness; and
analyzing the results of the coding.
Table 3 shows the differences between the three approaches to content analysis.
Major coding differences among three approaches to content analysis
| Type of content analysis | Study starts with | Timing of defining codes or keywords | Source of codes or keywords |
|---|---|---|---|
| Conventional content analysis | Observation | Codes are defined during data analysis | Codes are derived from data |
| Directed content analysis | Theory | Codes are defined before and during data analysis | Codes are derived from theory or relevant research findings |
| Summative content analysis | Keywords | Keywords are identified before and during data analysis | Keywords are derived from the interest of researchers or review of literature |
Therefore, this study used the deductive/directed content analysis approach for the proposed model development, which was verified from both the academic and industrial perspectives by using the level of agreement. However, when denoting the qualitative content analysis, the researcher was not allowed to conduct a statistical analysis to obtain the means of the data; therefore, other methods of analysis were required (Bengtsson, 2016). Hence, the data on the level of agreement was analyzed using the descriptive analysis approach. Then, the open-ended questions were analyzed using the directed content analysis approach to improve the model according to the participants’ comments and suggestions.
3.2 Expert survey
An expert survey is a well-known survey-based approach. According to Gemenis (2015), experts are asked to make judgements of party positions based on their knowledge, and these are extended with recommendations. The experts for this study were classified by filtration, as shown in Table 4. The experts who were chosen were professionals with more than 5 years of experience in the industry and academicians who had 10 or more international publications (Da Cruz et al., 2013).
Classification of experts
| Criteria | Industrial experts | Academic experts |
|---|---|---|
| Classification | Involved directly or indirectly in warehouse operations management | Background of expert studies in logistics, warehousing and supply chain |
| Experience | At least more than five years | At least more than five years |
| Positions/ranks | Operational/ tactical/ strategic management level | Senior Lecturer/Associate Professor/Professor |
3.3 Research framework
Figure 3 shows the research framework for this study. The study was conducted according to the steps and procedures of the developed research framework to revise the warehousing productivity performance indicators using ratio-based benchmarks.
3.3.1 Step 1: Identify the problem.
In this study, the problem was identified as the need to develop logistics (including warehousing) productivity performance indicators. Performance measurement issues are significant and successive to allow managers/researchers to ratify relevant performance measures. Conventional productivity benchmarking approaches use input and output ratios, where the ratio value corresponds to the “best in class” based on the specific ratio definition (Chen and McGinnis, 2007). In other words, the performance indicator refers to the ratio of an output to an input or an input to an output (Thanassoulis et al., 1996). Therefore, this study implemented the ratio-based productivity indicator measurement introduced by Staudt et al. (2015) because of the importance of allocating the warehouse resources efficiently and effectively to enrich productivity and to reduce the production cost of the warehouse (Richards, 2017; Poon et al., 2009; Rama et al., 2012). However, the introduced paradigm was incomplete, where some input resources and activity performances that were not included in the framework may result in an imbalanced and vulnerable productivity performance indicator benchmark. As such, these gaps raised the attention to investigate the necessary definitions of the ratio-based productivity indicators.
3.3.2 Step 2: Set up goal of study.
The goal of this study was to revise the warehouse productivity indicators by enhancing the existing paradigm of the ratio-based productivity indicator benchmarks. A ratio-based benchmark was chosen in this study as its significance was in line with current industry practices. Thus, a complete study was conducted to formulate the warehousing productivity indicator model with concrete ratio-based benchmarks that will be applicable universally.
3.3.3 Step 3: Recognize the scope of the study.
Warehouse performance is usually measured in terms of economic and technical efficiencies. This study focussed only on technical efficiency to measure the productivity performance according to ratio-based benchmarks. In addition, as discussed earlier, in the existing paradigm of warehouse productivity indicators introduced by Staudt et al. (2015), the definition of each productivity indicator of the ratio-based benchmark is described. However, the model produced by Staudt et al. (2015) does not seem to cover all the respective productivity elements, which are the warehouse input resources and the activities involved with warehousing. Hence, this indicated the significance of revising the existing paradigm by considering the measurement of all the input resources (labour, equipment and space) and outputs (movement, storage utilization and accumulation) in terms of the technical efficiency.
3.3.4 Step 4: Data collection process.
In this step, the data were collected from both primary and secondary sources. Initially, the data for the construction of the warehousing productivity performance indicators model were obtained from secondary sources (published journals, articles and books), as further explained in Step 5. Then, the constructed model (Table 5) was used to conduct a survey and discussions with experts to gather the first-hand data for analysis. The survey consisted of three parts, namely:
Information of the expert respondents
| Experts | Designation | Organization | Background/experience | Years of experience |
|---|---|---|---|---|
| Expert 1 | Senior Lecturer | Organization A | Logistics management and warehousing/logistics and warehouse | 16 years (practitioner) 13 years (academic in logistics and warehousing) |
| Expert 2 | Warehouse Executive (tactical level) | Organization B | Logistics and warehousing | 8 years |
| Expert 3 | Senior Logistic Manager (strategic level) | Organization B | Manufacturing, transportation and logistics | 19 years |
| Expert 4 | Warehouse Executive (tactical level) | Organization C | Warehouse | 22 years |
| Expert 5 | Warehouse Executive (tactical level) | Organization D | Warehouse | More than 10 years |
| Expert 6 | Associate Professor | Organization E | Logistics and supply chain | 25 years |
the respondents’ profile;
questions based on a five-point Likert scale; and
open-ended questions.
Meanwhile, the probability sampling technique was used to select a random sample of logistics experts from among both Malaysian academics and expert practitioners in the logistics, warehousing and supply chain fields. Table 5 illustrates the details of the selected experts, with their names and respective organizations written under pseudonyms to protect their anonymity and confidentiality.
3.3.5 Step 5: Model development – warehousing productivity performance indicators.
As briefly discussed earlier, the input, defined as the warehouse resources, includes labour, equipment, space and cost. However, cost and capital investment was not discussed in the productivity measurement (Frazelle, 2002; Staudt et al., 2015) and was referred to as another dimension of the warehouse cost performance measurement. Therefore, the model showed the ratio of the resources to the tasks within the work area. The output of the warehousing activities within the work area was classified as the movement and storage in response to the performance of the input resources.
As shown in Figure 4, the model was developed using the content analysis and expert survey methods. The input resources (labour, equipment and space) were outlined as the key codes for the construction of the model. Then, by refining the ratio-based benchmark of each indicator, as introduced by Staudt et al. (2015), the keyword to describe each indictor was categorized into the respective input resource group. For example, the “labour productivity” indicator was the ratio of the number of items managed to the number of items handled during working hours, where the working hours were referred to as the labour input resource.
Meanwhile, this study identified the equipment input resource to be considered in the model, and proposed an indicator referred to as “equipment utilization”. This was followed by the “putaway productivity” indicator, where the putaway activity that was performed during the inbound process was considered in the model. Thus, this model took into account the information in Table 2, namely, warehouse input resources (labour, space and equipment) and additional productivity indicators (putaway productivity and equipment utilization).
3.3.6 Step 6: Expert verification – academic and industrial perspectives.
The survey data were obtained through personal interviews and electronic interactive media. The experts were selected according to the criteria for respondents, as shown in Table 4. In total, 10 questionnaires were sent to both professional and academic experts. However, 6 out of the 10 questionnaires were returned. The data for this study, particularly those that were collected through semi-structured interviews with six experts, were analyzed as they surpassed the minimum number of five experts for the expert survey (Davis et al., 2009; Frehe et al., 2017).
Table 6 illustrates the summary of the experts’ comments, verification of the proposed model and the action that was to be taken. In this step, the gathered data were analyzed before the development of the warehousing productivity measurement indicators model was finalized.
Summary of data gathered from experts
| Expert | Comments | Verification | Action |
|---|---|---|---|
| Expert 1 | 1. To add two more items or activities, namely: 2. Layout (under the space) Computer usage or IT (under all the three inputs) to ensure the model is complete and the research is done significantly | Agreed to additional indicators | • The layout has been categorized as warehouse utilization (WU), inventory space utilization (ISU) and outbound space utilization (OSU) • To consider the Information System as an additional indicator |
| Expert 2 | 1. Unfair to measure LP as it depends on customer orders 2. To consider communication in the model | Agreed, with slight comments | • Analyzing the LP • To consider the Information System as an additional indicator |
| Expert 3 | 1. Unfair to measure labour productivity (LP) as an indicator 2. WU indicator to be amended to building/ facility utilization and to be measured as a percentage 3. ISU indicator to be amended to storage space utilization and measurement to vary 4. OSU indicator to be amended to staging/inbound and outbound space utilization and to be measured as item per square foot over time 5. Turnover defined as the ratio of sales revenue to average inventory level 6. Delivery activity to be amended to loading and measurement of TU to vary | Agreed to rename the indicators and amend the definitions | To consider and analyze the comments to finalize and amend the model development |
| Expert 4 | 1. Good to measure the throughput indicator in the production warehouse 2. TP is not under warehousing responsibility but acts as an advisor to the customer in filling the container planning 3. Communication through information system is the major means of ensuring the productivity of the warehouse | Agreed, with slight comments | • To analyze the comments • To consider the information system as an additional indicator |
| Expert 5 | 1. Those indicators and measurements are to be used in their company and should be remarked as neutral | Overall agreed | To analyze the indicators and measurements |
| Expert 6 | 1. Labour can be addressed as manpower (a term that is usually used in industries) 2 Technology shall be included in equipment usage 3. To consider WMS, inventory management system and RFID in the model | Strongly agreed with strong measurement model | To consider the Information System as an additional indicator |
3.3.7 Step 7: Descriptive data analysis process from experts’ verification.
The existing warehousing productivity indicators (Table 2) and proposed model development (Figure 4) were reviewed by the experts and their responses are recorded in Figure 5.
Figure 5 illustrates the analysis of the judgement options of the respondents such as strongly disagree, disagree, neutral, agree and strongly disagree. Overall, the respondents agreed with the proposed model (Figure 4), and their judgement options were based on the definition of each of the listed indicators. For instance, for labour productivity, it was recorded that one respondent disagreed, one was neutral, three agreed and one strongly agreed. Then, the experts preferred labour and equipment to be the main resources to be measured in both the inbound and outbound processes in warehousing operations. Meanwhile, under the space resource, the highest preference was for the “inventory space utilization” as this is very crucial, as every square foot available is to store or place goods, which will bring money to the company.
On top of that, all the experts were in favour of the proposed model, which consisted of 12 indicators, with some having been amended to rename the indicators and to clarify the definitions. Moreover, this study also considered the comments of the experts on the inclusion of additional indicators in the warehousing productivity measurement indicators model, and these will be presented as the final outcome of this study.
4. Findings
The final outcome, as illustrated in Table 7, was the revision of a set of ratio-based warehousing productivity measurement indicators. After the information had been gathered from the survey and discussed with the experts, all the suggestions of the experts were taken into consideration and the former indicators, as well as the ratio-based measurement for each indicator were revised. The resources remained as labour, equipment and space, with the inclusion of information system in the model, while the experts agreed that cost should not be incurred for measuring the productivity. Therefore, a comparison between the developed model (Figure 4) and the revised version of the model (Table 7) showed that some amendments were made, including the renaming of the indicators and the inclusion of additional resources, as well as warehouse activities. As such, the suggestions for equipment utilization’ and “putaway productivity”, that were made in the earlier stage of this study, were accepted. The experts had also suggested that the equipment resource should be expanded and that the productivity performance should be distinguished according to the basic warehousing activities. Meanwhile, the “putaway productivity” was extracted to measure the putaway operation performance in terms of labour and equipment resources, respectively. The labour and equipment resources were conclusively measured into the inbound (receiving and putaway) and outbound processes (picking and shipping).
The final revised model development – warehousing productivity measurement indicators
| Input (Resources) | Activities | Indicator | Definition |
|---|---|---|---|
| Labour | Receivinga | Receiving productivity | Number of vehicles unloaded per labour hour |
| Putawaya | Putaway productivity | Putaway per man hour | |
| Pickingb | Picking productivity | Total number of products picked per labour hour in picking activity | |
| Value-added servicesb | Manpower productivity | The ratio of the total number of items managed to the amount of item-handling working hours | |
| Shippingb | Shipping productivity | Total number of products shipped per labour hour | |
| Equipment | Receivinga | Receiving productivity | Number of vehicles unloaded per equipment |
| Putawaya | Putaway productivity | Putaway per equipment | |
| Pickingb | Picking productivity | Total number of products picked per equipment in picking activity | |
| Shippingb | Shipping productivity | Total number of products shipped per equipment | |
| Space | Layout | Building utilization | The ratio of the number of the square foot used per total square feet capacity of the building |
| Storage space utilization | The rate of space occupied by the storage as ϰγ ϰ = amount of capacity used by items (pallets, cartons, tons, square feet) γ = total amount of available capacity (racking, square feet) | ||
| Staging area utilizationa,b | The ratio of items per square feet over the amount of time | ||
| Storage | Turnover | The ratio between the number of outgoing items and average items in stock | |
| Throughput | Items per hour leaving the warehouse | ||
| Loading | Transport utilizationb | The ratio between weight/volume loaded over the total amount of weight/volume of container capacity | |
| Information system | Communication | Warehouse management system | Optimization of the warehouse as a whole Measurement of the assigned task per hour |
Notes:
aInbound process; boutbound process
Also, this model considered the categorization of “manpower productivity” with its definition as value-added services such as kitting, replenishment, packing and labelling, which depend on customer demand and warehouse availability to provide the value-added services that are usually performed by labour. This was to ensure that the value-added services provided by the warehouse were taken into consideration to magnify the effectiveness and efficiency of the current supply chain management. Meanwhile, the space resource remained the same, except for slight changes, where the indicators were renamed as “building utilization”, “storage space utilization” and “staging space utilization”, with its categorization as the layout of the warehouse. This was followed by storage group activity, which remained with the same indicators of turnover and throughput. The “delivery” activity was amended to “loading” activity, where the term loading was much more reliable and it was thought best that it be renamed as “transport utilization”, as its definition refers to the space capacity of the transport, where productivity is best defined for the movement performance.
After considering the comments and opinions of the experts, it was decided to include the “information system” element in the model as it is one of the most required resources for warehousing (Bartholdi and Hackman, 2014). As such, it was the information technology (IT) revolution that gave access to the specific mechanisms that appeared to increase productivity and profitability (King, 1998). Warehouse efficiency and firm performance are highly influenced by the positive relationship between warehouse operations and warehouse management information system (Mohd Saifudin et al., 2012; Ali and Haseeb, 2019). Some comprehensive integrated information systems in warehouse management systems influence the effective functions of the warehousing process (Klabusayova, 2013) and directly affect employee productivity, overall efficiency and even storage capacity (Ramaa et al., 2011). Therefore, this study proposed to measure the warehouse management system (WMS) productivity performance by the ratio of the assigned tasks per hour to improve the processing speed and time. For example, the assigned task can be referred to as the number of workers involved in the receiving activity per hour with the utilization of an information system in the process, resulting in an improved process and time saved per order. In addition, the information system will be used by the labour force, ranging from the operators to the upper management, to record, monitor and control the products using the available system. To summarize, the information system, which was generally addressed as the warehouse management system, was studied widely with various available inventory management systems to support the warehousing process as a form of effective communication throughout the process from the customer order, stock availability, stock leaving the warehouse and stock traceability.
For a better understanding of the revision process, Figure A1 ( Appendix) demonstrates the three different models that had been improved accordingly.
5. Conclusion
In conclusion, the objective of this study, which was to revise the ratio-based benchmarks for warehousing productivity performance indicators, was achieved as the model deviated from the previous findings by Staudt et al. (2015) with the additional inclusion of warehouse input resources that reflected every warehouse activity.
A comprehensive review of warehouse productivity performance indicators for the measurement of the input of major warehouse resources (labour, equipment, space and information system) within the work area to represent the movement and storage output performance was clearly presented in this paper. Apart from that, a major concern among warehouse managers is how to increase productivity and accuracy, reduce and control the cost of inventory and shipping, while providing good customer service (Richards, 2011). Therefore, the final model developed for warehousing productivity indicators can be adapted for worldwide applicability, as the basic warehouse operations have remained the same. Meanwhile, the model has improvised a variety of ratio-based measurements into packaged items (pallets/cartons/tons) for the movement and storage output performance. Still, researchers Frazelle (2002) and practitioners have cautioned against a high space utilization, which may reduce productivity. As such, this model can be of benefit to warehouse managers in highlighting the use of “turnover” and “throughput” indicators performance measurements to monitor the movement of products in the warehouse, as well as to avoid the congestion that will lead to reduced productivity performance. Productivity has been widely studied as one of the major dimensions in the measurement of warehouse performance that can assist managers in identifying any low productivity together with the adoption of the latest technology and innovations. The best thing a company can do to improve its productivity is to engage with the people as they are the heart of warehousing operations and to be ready to transform the business by revolutionizing its “smart” technology assets. Consequently, an improved internal warehouse operation performance will lead to increased customer satisfaction.
The contributions of this study are as follows: First and foremost, the developed ratio-based warehousing productivity performance indicators model is accessible to any warehouse. The model was constructed by considering significant resources (labour, equipment, space and information system) in every warehouse and the output performance of all the basic warehouse operations (receiving, putaway, storage, picking and shipping). As such, this model can be widely adopted regionally and globally as all warehouses are being run with the same basic warehouse operation activities and general warehouse resources. Secondly, this study also provides a general ratio-based performance measurement to evaluate the productivity performance by indicating the activity indicators and the input resources of the warehouse. Thus, it is important to distinguish between different outputs as different levels of resource commitments may be involved in handling different types of order lines (piece picks, case picks and pallet picks). Thirdly, the indicators of the productivity dimension are meant to support organizations, authorities and researchers in developing a warehouse performance measurement system, as well as to assist warehouse managers in measuring the KPIs of the warehouse. Finally, warehouse managers can re-examine the work area movement and storage output performance with the available input resources. For instance, the measurement of labour and equipment productivity can provide better performance analysis for warehouse management to increase or decrease the number of workers and/or investments for new equipment.
As such, this study suggested some details for extended studies that can be conducted in future:
to measure the ranking of importance among the warehousing productivity performance indicators, which will lead to better knowledge and guidance for future planning;
to conduct a productivity performance measurement assessment case study by using the revised ratio-based method among practitioners;
as the limitation of the study was that it only focussed on the productivity dimension, a revised study can be carried out on the other dimensions (time, cost and quality) of existing ratio-based warehouse operation performance indicators; and
to construct a general systematic warehouse operation performance indicators model that can be used by every type of warehouse both regionally and globally.
The authors would like to express gratitude to the anonymous Malaysian experts from DB Schenker Southern Region, Kontena Nasional Berhad, Universiti Utara Malaysia, Asahi Logistics Sdn Bhd and Universiti Teknologi MARA for their involvement and contribution in this study. The authors are also thankful to the Ministry of Education, Malaysia, and Universiti Malaysia Terengganu for giving the financial support under Fundamental Research Grant Scheme with vote number: 59510.
