Outside the Box Warehousing: When Thinking of Inputs as Outputs Makes Sense.
As the supply chain revolution continues, small and medium-sized enterprises and smaller facilities of large companies experience an increased pressure from their adjacent supply chain partners and their own senior management to improve their performance. This places higher demands on the accuracy of their performance measurement, particularly labor productivity in warehouses. Smaller facilities cannot afford to implement engineered labor standards (ELS) and rely on traditional metrics of output, such as piece count, which do not provide the reliable level of accuracy demanded today. Changing the traditional view of the inputs-outputs paradigm, this study suggests a unique labor productivity metric for nonELS facilities based on warehouse lift-truck utilization data. Empirical tests using a longitudinal data set from an automotive parts distribution center provide evidence that the new metric is more accurate than the traditional output metrics because it is not affected by the assignment contribution error due to workload smoothing. The new metric offers managers an opportunity to fine-tune labor productivity measurement in warehouses and other environments with extensive lift-truck utilization without investing in ELS projects.
Warehousing, performance measurement, labor productivity, lift trucks, small and medium-sized enterprises (SME)
Introduction: Understanding the Problem
Warehouses are known to be labor intensive and labor productivity measurement has long been an important performance metric in warehousing (Bowersox, CIoss, and Helferich 1986; Sanders and Ritzman 2004). It is one of the five warehouse performance measurements identified in a comprehensive study of logistics performance measures by Mentzer and Konrad (1991). Six of the thirteen operations metrics relate to labor productivity in a periodic survey of warehouses conducted by the Warehousing Education and Research Council (WERC) (Manrodt, Vitasek, and Tillman 2013). In supply chain relationships, such as between retailers and 3PL warehouses, the latter are viewed primarily as organizations supplying "labor capacity" (Newsome, Thompson, and Commander 2013), so it is unsurprising that the warehouse performance measures are so focused on labor productivity.
Challenges of Measuring Labor Productivity in a Warehouse
The warehousing industry, one of the fastest growing sectors of the economy, remains highly fragmented with the majority of the facilities relying on mechanized handling systems, that is, labor plus material handling equipment (MHE) (Burrow 2012, Bowersox et al. 2013). Measuring labor productivity is inherently difficult in such warehouses: a typical warehouse operation includes a variety of activities involving labor and MHE: receiving, putaway, storage, order picking, and shipping (Frazelle 2002).
Historically, labor productivity measures in warehouses were chosen primarily for their simplicity, looked more like quotas and were based on the best guess of the management rather than science (Ludwig and Goomas 2009). More recently, many warehouses with sufficient economies of scale introduced engineered labor standards (ELS) based on time and motion studies (Gray 1992; Ludwig and Goomas 2009; Newsome, Thompson, and Commander 2013). In these studies, all warehouse tasks are split into basic elements and fair times to perform them are determined as a basis for ELS (Ludwig and Goomas 2009). Labor productivity is then a comparison of the actual time spent on a task to the standard time allotted for the task (Ludwig and Goomas 2009).
When coupled with the latest IT technology, such as sophisticated warehouse management systems (WMS) and mobile computers (wearable or lift-truck mounted), ELS are believed to be particularly effective (Ludwig and Goomas 2009), but ELS-based labor productivity measurement has its share of problems. Introduction of ELS is often perceived by warehouse employees as "draconian" measures and is actively resisted by unions at every step of the way, often successfully (Newsome, Thompson, and Commander 2013). A WERC study reported that the majority of ELS programs fail within two years (Atkinson 2003).
The accuracy of performance measurement systems is often seen as a tradeoff with resources (Atkinson 2003; Mentzer and Konrad 1991; Morgan 2004). Small and medium-sized enterprises (SMEs) lack resources for advanced comprehensive performance measurement systems (Mentzer and Konrad 1991; Morgan 2004; Shepherd and Gunter 2006), largely rely on simple "routine" practical performance measures (Hudson, Smart, and Bourne 2001; Newsome, Thompson, and Commander 2013) and even consider performance measurement a "luxury" (Neely, Gregory, and Platts 1.995). For many smaller warehouses, it is prohibitively expensive to invest in automation and advanced information technologies or conduct time and motion studies needed for ELS (Gray 1992). As an alternative to ELS they use the traditional way of measuring labor productivity as a ratio of outputs to inputs, specifically labor (Chan and Qi 2003; Ludwig and Goomas 2009), and focus only on select labor-intensive and customer critical warehousing processes, such as order picking and shipping (Chiang, Lin, and Chen 2011; Frazelle 2002; Grosse et al. 2015) leaving some warehouse activities completely unmeasured.
In this article, we propose a metric of utilization of MHE, specifically lift trucks, as a way for warehouses without ELS to increase the accuracy of labor productivity measurement for those activities that are measured with traditional outputs and extend labor productivity measurement to lift truck-supported activities that have not been measured before. This metric can be viewed as an intermediate option between the two categories of metrics described above.
But first it is necessary to understand why improving the accuracy of labor productivity measurement is becoming critically important for smaller facilities without ELS.
The Effects of Being "Small" in the Modern Age of Supply Chain Development
The move to ELS is in line with the observation made by Neely, Gregory, and Platts (1995) that the focus of performance measurement started to shift from the results to the process and determinants of results. This presents a unique challenge to smaller warehouses.
On the one hand, in the age of increasing supply chain collaboration, smaller firms are more lured by an interorganizational relationship as they stand to gain more in a cooperative relationship than their large partners (Das, Sen, and Sengupta 1998). On the other, as a party's share of business with certain partners grows, it becomes more dependent on them and its business partners acquire more power over it (Emerson 1962). The retail industry provides particularly clear evidence of that. A shift of power from suppliers to retailers has been well noted in the retail supply chain (Bloom and Perry 2001; Gosman and Kohlbeck 2009; Kelly and Gosman 2000; Mottner and Smith 2009). Advances in retail and information technology legitimized the extension of operations monitoring and control beyond the retailer itself down to the supply chain (Newsome, Thompson, and Commander 2013). Retailers constantly demand new cost savings from suppliers and set up key performance indicators based directly on internal performance measures of their suppliers, forcing the suppliers to create more stringent performance measures such as hourly labor productivity in a supplier's distribution center (Newsome, Thompson, and Commander 2013). This creates enormous pressure on suppliers to upgrade their performance measurement systems.
The age of convenient product uniformity is quickly ending in other warehouse settings, too. From the situation of handling goods by a pallet load or full cases, distribution centers serving retail industry are switching to piece (broken case) picking as orders come more frequently but for smaller quantities of goods per order. The trend is already modus vivendi in fulfillment centers for e-commerce (Hinojosa 2003). Thus, the performance measurement problems that only select industries (furniture, automotive) have always had due to handling products highly diverse in weight, shape and size will become a headache for more and more warehouse managers in the future. This underscores the importance of this study.
Coping with Increasing Demands on Labor and Its Performance Measurement
With the evolution of supply chains, many changes occurred in warehouses, including considerable progress in IT systems and in automation to achieve higher productivity (Faber, de Koster, and van de Velde 2002). However, many warehouses prefer to remain "manual" to retain flexibility in dynamically changing operating environments (Grosse et al. 2015). To cope with the growing variability in demand for labor and to achieve consistent and more efficient labor utilization, many warehouses employ workload smoothing (buffering) techniques, such as spreading outbound shipments within a day or shifting them to other days of the week (Dubeauclard and McKinsey 8t Company 2008; Heskett 1971; Sanders and Ritzman 2004). Workforce flexibility can become a source of competitive advantage (Autry and Daugherty 2003; Sanders and Ritzman 2004), but it is not an even playing field. When smaller warehouses with unsophisticated WMS split a work assignment, such as picking a nonurgent order over two days as a workload smoothing technique, they experience a classic example of what Mentzer and Konrad (1991) termed a contribution assignment error: all outputs (lines, pieces, etc.) are credited by the WMS to the day when the picking order is confirmed (finished). This distorts the labor productivity, since the actual labor hours are recorded accurately each day. It may also tempt workers to adjust their labor productivity to a perceived "fair day's work" standard, a practice known as pegging (Gray 1992; Kriesberg and Hoecker 1954; Ludwig and Goomas 2009).
In other words, smaller warehouses find themselves in a paradoxical and precarious situation of having to choose between better labor utilization and measuring labor performance more accurately in a situation when they are under mounting pressure to do both. Is there a solution to this problem? Obviously, finding a better way to measure labor productivity would be highly desirable. This becomes the research question of this study. Potential solutions using a theoretical framework are explored in the next section. It is followed by testing empirically one proposed solution. The article concludes with a summary of findings, intended contributions to research and practice, and a brief discussion of the study limitations and directions of future research.
Theoretical Framework: Looking for a Solution
The Input-Output Framework
One possible view of warehousing operations is that of inputs and outputs (Hackman et al. 2001). Then labor productivity can be defined as the ratio of physical input to physical output (Bowersox, CIoss, and Helferich 1986). An alternative approach is to use the ratio of output per input (Kriesberg and Hoecker 1954). Regardless of the approach, both inputs and outputs need to be measured.
Hackman et al. (2001) proposed a general warehouse input-output model consisting of labor, space, and equipment as inputs and movement, storage, and accumulation as outputs. More specific measures (proxies) of inputs and outputs are necessary for practical purposes, such as labor productivity measurement. Commonly, labor input is measured in labor hours (Mentzer and Konrad 1991). Measuring and using this kind of inputs for the labor productivity ratio does not present a substantial difficulty in most warehouse settings.
Unfortunately, outputs for labor productivity are a different story; they are much harder to decide on (Mentzer and Konrad 1991). Two particular problems with selection of measures are that they do not take all aspects of operations into account and systemically underestimate the actual inputs and outputs, particularly when products differ in dimensions relevant to handling, such as size, weight, and bulkiness (Mentzer and Konrad 1991). For example, the cases per hour measure does not take into account such factors as travel distance, weight, size, or shape of the product (Ludwig and Goomas 2009). However, measuring all possible nuances of inputs and outputs may be impractical since the marginal cost of information may be higher than the marginal benefit (Mentzer and Konrad 1991).
Two of the three categories of output (accumulation and storage) in the Hackman et al. (2001) model can be immediately ruled out. Accumulation (conveyors and sortation systems) is rare in smaller warehouses, and storage (cost efficiency of space utilization) is not directly related to operations (Hackman et al. 2001). This leaves the movement output.
This output is measured in a variety of units, such as order lines, cases, or pieces (Gray 1992). Automotive spare-parts distribution centers serving dealerships are a typical example; they often have to deal with parts that defy any traditional unitization means, such as vehicle body panels. The existing practice then (reflected in WERC industry surveys) is to use several measures of output, such as order lines, pieces, and "cube" (a measure of volume, e.g., cubic feet), and consequently several measures of labor productivity based on them. The situation resembles the famous story of a group of blind men touching different parts of an elephant to understand what the animal looks like. Unlike in the story where all parts touched appear to be unrelated, different measures of output in automotive distribution centers have reasonable statistical correlations as shown later in this study.
Introducing the New Metric
Regardless of the chosen output metric, the problem of contribution assignment error remains since all data for the discussed movement outputs comes from the WMS that distorts them due to workload smoothing as previously explained.
The potential solution proposed by this study requires thinking outside the box. We have rejected all typical output metrics and now need to look for insights to the other side of the input-output model. After all, inputs are transformed in a warehouse into outputs (Hackman et al. 2001).
Since labor is already used in the labor productivity ratio, the remaining two possibilities are space and equipment. The amount of space is not a discretionary input in the short term (Hackman et al. 2001) and is thus useless for a dynamic operational measure like labor productivity. However, Hackman et al. (2001) note that warehouse managers have a reasonable degree of control over equipment (lift trucks) and the need to visit a location is often the main driver of time and costs in warehouse operations.
The specific metric to quantify the use of lift trucks (an input in the Hackman et al. 2001 model) suggested here as a proxy for output in the labor productivity ratio is machine hours. Warehouse machines here are understood as various human-operated lift trucks equipped with hour gauges. These are roughly equivalent to odometers in automobiles. They show how much the machine has been used. These hour meters in contemporary lift trucks can be adjusted or are already set as default by the manufacturer to record only the time when the machine is moving as a whole or any of its parts (such as the mast) is in motion. Simply turning the power on will not cause the clock "to start ticking." Thus, machine hours represent the true time the machine has worked.
Understanding the New Metric
There have been precedents of substituting a measure of input for an output. In an academic paper on benchmarking warehouse efficiency, Hackman et al. (2001) chose a warehouse input efficiency measure over the corresponding output efficiency measure to use in their analysis because it was more meaningful.
The idea of using machines in this sense is not unique either. Mentzer and Konrad (1991) note that an accounting orientation is often reflected in the selection of measures in logistics by analogy with accounting, similar to direct cost allocation in manufacturing. As in the manufacturing environment, where products are produced by people using manual labor and machines, handling of products in the warehouse with the use of manual labor and machines (lift trucks) results in physical output. For example, Patrick (1961) compared the choice of labor hours versus machine hours in this accounting cost-allocation sense for manufacturing and concluded that machine hours are a superior measure in cost accounting, providing an easier and more consistent contribution allocation method whenever operations are complex and either tasks or equipment are heterogeneous, a remarkable analogy of a present-day warehouse.
Machine hours as an output metric are not tied to physical attributes of products and may be hard to accept psychologically. But warehouses already use popular metrics of this kind, such as order lines, so this disadvantage of psychological resentment may be just a matter of time and should be weighed against the advantages.
There are several reasons why the new metric may be promising. It eliminates or alleviates two of the problems highlighted by Mentzer and Konrad (1991): the measurement effort and the measurement error. It is clear that in a smaller warehouse daily recording of machine hours is easy. It is typically performed as part of good housekeeping practices. The current hour meter reading is often recorded in the machine log as part of the walk-around "ritual" of passing the machine from shift to shift. Machine hours are also tracked for lift-truck regular maintenance purposes and optimum equipment utilization (Keller and Keller 2014).
So these data are readily available and objective. Human errors, if any, recording these data are easy to uncover and correct. These data are hard to manipulate, and their use addresses the human factor aspect of the measurement error problem. Cases of opportunism when a worker will aimlessly drive a lift truck just to mark the time are not considered here since they are rare and managers of such warehouses definitely have higher priorities of work discipline and organization to address before they are ready to fine-tune their labor productivity measurement with the method described in this article.
Testing the New Measure
In the previous section, it was argued that machine hours are an objective warehouse input that can be thought of as an output. Finding evidence that lift-truck hours offer an improvement in accuracy over the traditional measures of output such as lines or cubic meters for the purposes of labor productivity measurement in warehouses without ELS becomes the goal for the empirical part of this study.
The data set for this study came from a small (65,000 SF) parts distribution center of a major automaker with global presence. The demand on labor was very uneven, and the management used a workload smoothing technique, "artificially" spreading picking of large non-urgent stock orders over two days to allow the flexibility of switching the workforce between urgent and regular (stock) orders depending on the actual urgent order demand.
When the picking of an order split over several days was completed, the whole order was confirmed in the WMS and all output, measured in picking lines or cubic meters, was allocated to the confirmation transaction date. Labor, however, was measured on the actual basis in human-hours: number of workers on hand times the number of hours worked. Labor productivity was tracked on a daily basis for the purposes of workload planning, personnel scheduling, including overtime, and performance-based pay for the personnel. The warehouse management was aware of the discrepancies in output measurement due to incomplete stock orders carried forward to the next day but was unable to identify a reasonable solution.
To answer the research question, we have to answer two specific questions represented by two hypotheses. We need to establish, first, if machine hours can reasonably be used as a measure of output and, second, if machine hours provide an accuracy advantage over the traditional measures when used as a measure of output.
There is no universal output measure to benchmark against, but we can test the strength of the statistical relationship between machine hours and the traditional forms of output. Since they all measure the same construct, the amount of work performed in a warehouse, they should all reasonably correlate.
Hypothesis 1. The daily total number of machine hours has a strong positive correlation with daily total hours worked and cubic meters processed.
If hypothesis 1 is supported, this would mean that machine hours can be used as a measure of output. However, we are interested in this metric only if it provides an advantage over traditional metrics affected by the contribution assignment error. Since machine hours are recorded daily and are never carried forward, we would expect that workload smoothing with orders split over several days will have no effect on daily labor productivity when machine hours are used as output, whereas there may be a difference in daily labor productivity due to the error introduced by workload smoothing when the other measures of output are used. Formally, this can be expressed as a two-part hypothesis:
Hypothesis 2A. There is no significant difference in daily labor productivity on days with workload smoothing and without smoothing when machine hours are used as a measure of output.
Hypothesis 2B. There is a difference in daily labor productivity on days with workload smoothing and without smoothing when lines or cubic meters are used as a measure of output.
Data and Method
One year worth of data was available with 257 observations (days with picking activity) in one calendar year. Fifty-four days were identified when workload smoothing definitely did not occur (all picking completed; no picking lines left to transfer to the next day). Smoothing may have occurred on any and all remaining 203 days.
Hypothesis 1 was tested with statistical correlation analysis using a statistical software package. The analysis of variance (ANOVA) was performed to compare means of the two groups (with and without smoothing) for each of the three variables (labor productivity with output measured in machine hours, lines and cubic meters) to test hypothesis 2.
Results of Data Analysis and Discussion
Pearson correlations for hypothesis 1 are given in table 1. As seen from the table, machine hours have a strong positive correlation with the other three variables. Therefore, hypothesis 1 is supported.
While a high correlation of machine hours and labor hours is intuitively expected, it is interesting to examine the correlations between the two forms of output tracked in the warehouse (lines and volume) and between either and machine hours. The correlation between lines and volume is .491, which translates to [R.sup.2] = .24. This is not as strong as one would intuitively expect of the two metrics measuring the same thing, the daily movement output of the warehouse. It underscores the inherent difficulty of measuring output in distribution centers that have to deal with extremely heterogeneous loose goods.
It is worth noting that the relationship of lines and volume with the variable of primary interest, the machine hours, is just slightly less strong with r = .465 for lines and r = .473 for cubic meters. This suggests that machine hours can be used as a proxy measure of output in place of either of the two.
To test hypothesis 2, compliance of the data with the standard ANOVA assumptions was verified, including homogeneity of variances (insignificant Levene's test). A one-factor two-group ANOVA was performed for all the three variables of interest.
For labor productivity expressed as machine hours per labor hour, F(1,256) < 1, insignificant. The analysis did not establish a difference in productivity between days with workload smoothing and without. Therefore, hypothesis 2A cannot be rejected. We can continue to assume that machine hours are a consistent measure when used as output in the labor-productivity ratio.
However, when output was measured in lines or in cubic meters, there was a significant difference in the means of the groups for days with and without smoothing: [F.sub.lines](1,256) = 13.59, P < .001 and [F.sub.cu m] (1,256) = 9.01, p = .003. This supports part B of hypothesis 2: workload smoothing had an effect on labor productivity expressed as lines per labor hour or as cubic meters per labor hour. In the context of this warehouse, this can be interpreted as a lack of reliability of the labor productivity data based on lines or volume and a more reliable measurement of output provided by machine hours.
Logistics and supply chain performance metrics researchers noted a lack of empirical and case studies (Gunasekaran, Patel, and McGaughey 2004; Shepherd and Gunter 2006). This study attempts to help bridge the gap. Addressing the lack or inaccuracy of existing output metrics in warehouses that are increasingly coming under pressure to improve labor productivity but have limited resources to do so, this study proposes an option to measure work performed in a warehouse by the number of machine hours generated by the total movement of lift trucks.
While machine hours are positively correlated to such measures of output as lines and cubic meters, the relationship is not strong enough to proclaim it a universal accurate solution. This is the main limitation of the metric. Yet, the warehouse management may use it as a way to check specific cases when smoothing is suspected to cause a deviation from normal daily operational parameters or as part of a weighted index.
The use of specific operational metrics is always a tradeoff between the accuracy of measurement and the cost of measuring (Mentzer and Konrad 1991). In this respect, the proposed metric has a huge advantage of requiring no investment, change in operations, or lead time to implement. Lift-truck hours are normally measured anyway and the historical data are already available.
In the warehousing setting described above, it is also the most accurate metric that can be applied to analysis of individual days. Of potential future interest is using this metric to analyze effects of human behavior on operations. Behavior is part of the performance construct (Rogers, Daugherty, and Alexander 1996). It was argued that most workers are guided by a "fair day's work" concept (Kriesberg and Hoecker 1954) and may resort to pegging (working faster or slower to match a certain productivity standard) (Gray 1992). If any specific patterns of pegging are suspected on certain days (e.g., Mondays versus Fridays), it might be possible to apply the new metric in looking for the differences.
In summary, three potential uses are proposed for this metric. First, machine hours, or more generally, the equipment utilization metric, provides an enhancement of the traditional metrics of picking labor productivity. It can be particularly useful in settings where traditional metrics lag in accuracy, such as handling of pieces with diverse physical characteristics or operations with workload smoothing.
Second, it can be applied to any warehouse activity that relies on a substantial use of lift trucks. Without any financial investment or change in existing operations it will allow warehouse managers to measure some of the activities that were not previously measured at all.
Third, applications beyond traditional warehousing are possible where lift trucks are used extensively, for example, in manufacturing facilities or in LTL terminals.
One intended contribution of this article is to encourage creativity in performance metrics design and a fresh, outside-the-box look at traditional perspectives and frameworks. Hopefully, along the way, a more universal warehouse output metric whose absence was lamented by Hackman et al. (2001) will be introduced to replace the fragmented metrics of today. For starters, a mere replication of the study in a similar or dissimilar setting would be of interest.
In the meantime, the use of machine hours will allow managers of warehouses and other material handling facilities to fine-tune their measurement of labor productivity and better respond to the challenges of supply chain management of today.
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Western Illinois University
Table 1/Pearson Correlations of Measures of Output Metric Daily total labor hours Daily total lines Daily total lines .442 * Daily total cubic .373 * .491 * meters Daily total machine .645 * .465 * hours Metric Daily total cubic meters Daily total lines Daily total cubic meters Daily total machine .473 * hours * Significant (p < 001)
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|Date:||Mar 22, 2018|
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