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Specialization: Transportation Engineering and Logistics Report number: 2017.TEL.8143
Title: Operational Efficiency in Warehousing
Author: T.D. Hogenboom
Title (in Dutch) Operationele efficiëntie van magazijnen en distributiecentra
Assignment: literature Confidential: no
Supervisor: dr.ir. Y. Pang
T
U
Delft
M A T E R I A L S E N G I N E E R I N GDelft University of Technology Department of Marine and Transport Technology
Mekelweg 2 2628 CD Delft the Netherlands Phone + 3 1 (0)15-2782889 Fax + 3 1 (0)15-2781397 www.mtt.tudelft.nl Student: T.D. Hogenboom Supervisor: Dr. ir. Y.Pang Specialization: TEL Creditpoints (EC): 10
Assignment type: Literature Assignment Report number: 2017.TEL.8143 Confidential: No
Subiect: Operational Efficiency in W a r e h o u s i n g
In the field of warehousing, Operational Efficiency (OE) can be considered to be the ability to run the operations while using a minimum level of resources. These resources can be the formalities of time, money, personnel, energy, etc. Inefficiency in warehousing can be caused by un-knowing how to be efficient and by the lack of methods/tools/approaches to perform operations efficiently. Various features, methodologies and technologies can be used to improve the operational efficiency of warehouses.
This literature assignment is to survey the state of the art development of OE in warehousing. The main tasks of this assignment cover the following:
• to describe OE and the relative definitions • to describe the field of warehouse operations • to study how OE is measured in warehousing
• to indicate the features that affect the OE of warehouses
• to investigate the concepts, methodologies and technologies to increase the OE of warehouses
This report should be arranged in such a way that all data is structurally presented in graphs, tables, and lists with belonging descriptions and explanations in text.
The report should comply with the guidelines of the section. Details can be found on the website.
The mentor.
Warehouses play a major role in virtually any supply network and continue to do so within all foreseeable future. Improving the operational/technical efficiency of warehouses has substan-tial economic potensubstan-tial and is key in obtaining high profitability. As Johnson & Mcginnis [1] state; "lacking a general understanding of warehouse technical efficiency and the associated causal factors limits industry’s ability to identify the best opportunities for improving ware-house performance". A clear overview of the state-of-the-art developments in assessing and improving the operational efficiency of warehouses is however lacking.
This research surveys the state-of-the-art developments in assessing and improving the oper-ational efficiency of warehouses. In this way, researchers and practitioners are provided the basic knowledge they need to identify fruitful opportunities for improvement.
Operational efficiency is considered to be a synonym for technical efficiency. A warehouse is considered to be technically efficient if it is operating on its production function [2]. Assessing the operational efficiency of warehouses is mainly done by using a set of key performance indicators (KPIs) or through benchmarking. Data envelopment analysis (DEA) is mostly used for benchmarking warehouses. Because warehouse benchmarking studies are not abundant nor consistent, it is difficult to present firm conclusions about the exact impact that certain features have on the total operational efficiency. It would be helpful if future benchmarking studies validate more of the work done by colleagues. Anyways, five parent-categories have been identified over which the drivers of operational efficiency in warehousing can be divided:
• Inventory management
Proper inventory management may reduce inventory levels (diversity as well as quantity) and forecasting errors. Positive effects may be expected from this due to the parameters significantly correlated with operational efficiency found by Johnson & Mcginnis [1] and Li et al. [3].
• Design and control of the material and information flow
High quality design and control of the material and information flow is a consistent factor in driving high productivity [4]. This also holds for reducing the work content by eliminating handling steps and minimizing travel time [4].
• Design and maintenance of warehouse facilities
Mainly the correlations of proper facility management [5] and warehouse size (smaller is better) [4, 6–8] with operational efficiency illustrate the importance of proper design and maintenance of warehouse facilities.
• Level and use of automation
Warehouse automation can have many advantages such as eliminating worker travel-ing, continuity and improved tracking & control [9]. Inappropriate system selection, a lack of adequate maintenance and difficulties in reconfiguring for changing business requirements (lack of flexibility) may however offset any improvements brought by tech-nology [4].
• Human performance
The quality of labor [1], manpower management [5] and management’s attentiveness to operations [4] are found to have a very high impact on the total operational efficiency.
High potential is seen in improving the put-away, storage and order-picking activities of warehouses. These activities account for the majority of the operating costs and directly impact the customer service level [9–12]. Options, considerations and directions are given to improve put-away, storage and order-picking with respect to:
Warehouse design
Decisions about the facility design are usually hard or expensive to change and thus, con-sidering the operational performance during the design phase is very important [13]. The general design of a warehouse consists of [13]: the overall structure, department layout, sizing and dimensioning, operational strategy selection and equipment selection. Determining the layouts of the facility (e.g. a U-flow or through flow concept [9, 10]) and the storage/pick-ing area is considered to be essential. Determinstorage/pick-ing the storage/pickstorage/pick-ing area layout consists of the block stacking pattern, aisle configuration and (optionally) the design of fast-picking areas [12, 13]. Innovative layouts such as the fishbone design are found to have significant benefits in unit-load operations [14].
Operational strategies and coordination
The storage location assignment strategy (e.g. random, dedicated, class-based, family group-ing) has a great impact on travel times and the utilization of storage locations [9, 12, 15]. Goods-to-picker order-picking strategies have a number of advantages opposed to picker-to-goods strategies [9]. They are however more capital intensive than the labor intensive picker-to-goods strategies [12,16]. Strategies such as batch-, zone- and wave picking, methods for routing (i.e. optimal, heuristics, sequencing) and stock counting are also discussed. Effects of the layout of the storage/picking area, storage location assignment strategy, order-picking strategy and methods for routing all interact to a great extend. These interactions provide an opportunity for future research (mainly in picker-to-goods systems).
Leveraging technology in manual storage and retrieval
Different types of storage and material handling equipment (e.g. gravity flow racks, tur-ret trucks etc.) are described that can be used to streamline manual storage and tur-retrieval operations. A number of paperless picking technologies (e.g. barcode scanners, voice pick-ing and pick-to-light) are found to reduce the number of order-pickpick-ing errors and increase productivity [17].
Automation in storage and retrieval
Technologies treated are: carousels, vertical lift modules (VLMs), A-frame dispensers, au-tomated storage and retrieval systems (AS/RS), autonomous vehicle storage and retrieval systems (AVS/RS), automated guided vehicle (AGV) systems and finally, automated item handling. A trend is spotted towards more advanced robotics such as AVS/RS, AGV systems and automated item handling. These technologies have great potential in improving the op-erational efficiency while maintaining much needed flexibility. Future research on automation in storage and retrieval should be directed towards these technologies.
Topics that didn’t receive much attention in this work, but do have a significant impact on the total operation efficiency of warehouses include inventory management and human performance. These topics also present interesting opportunities for future research.
Summary iii
1 Introduction 1
2 Defining Operational Efficiency 3
2.1 Productivity . . . 4
2.2 Efficiency . . . 4
2.3 Effectiveness . . . 5
2.4 Performance . . . 5
2.5 Operational Efficiency . . . 6
3 The Field of Warehousing 9 3.1 The role of the warehouse . . . 9
3.2 Warehouse operations . . . 11 3.2.1 Inbound operations . . . 13 3.2.2 Storage . . . 14 3.2.3 Outbound operations . . . 14 3.2.4 Cross-docking . . . 15 3.2.5 Value-adding services . . . 16
4 Assessing Warehouse Efficiency 17 4.1 Introduction . . . 17
4.2 Key Performance Indicators . . . 17
4.3 Benchmarking studies . . . 24
4.3.1 Benchmarking studies in practice . . . 24
4.3.2 Driving features of operational efficiency in warehousing . . . 29
5 Improving operational efficiency in warehousing 33 5.1 Introduction . . . 33
5.2 Operational efficiency by design . . . 34
5.2.1 General design . . . 34
5.2.2 Storage area layout . . . 37
5.3 Operational strategies and coordination . . . 40
5.3.2 Order picking strategies . . . 42
5.3.3 Routing and sequencing . . . 44
5.3.4 Stock counting strategies . . . 45
5.4 Leveraging technology in manual storage and retrieval . . . 46
5.4.1 Storage equipment . . . 46
5.4.2 Material Handling Equipment . . . 48
5.4.3 Paperless order picking . . . 50
5.5 Automation in storage and retrieval . . . 52
5.5.1 Carousels and vertical lift modules (VLMs) . . . 52
5.5.2 A-frame dispensers . . . 54
5.5.3 Automated Storage and Retrieval systems (AS/RS) . . . 55
5.5.4 AGV systems . . . 59
5.5.5 Automated item handling . . . 60
6 Conclusions 63 7 Recommendations for future research 65 Bibliography 67 Glossary 73 List of Acronyms . . . 73
List of Symbols . . . 74
A Warehouse assessment background 75 A.1 Benchmarking information . . . 75
2.1 General process view . . . 3
2.2 Production function and efficiency estimation [18] . . . 7
3.1 General overview of warehouse operations . . . 11
3.2 Typical subdivision of activity costs as a percentage of total costs [9] . . . 12
3.3 Typical warehouse major cost elements [11] . . . 12
3.4 Labor hours per activity as percentage of total labor hours [11] . . . 12
3.5 Typical order picking time consumption [10] . . . 14
3.6 Principle of cross docking operations [9] . . . 15
4.1 Initial input-output model of Johnson & McGinnis [1] . . . 25
4.2 Input-output model of Hackman et al. [4] . . . 26
5.1 Warehouse design problems and publication frequency [13] . . . 34
5.2 Concept of a U-flow warehouse (courtesy of University of Huddersfield) [9] . . . 35
5.3 Concept of a through-flow warehouse (courtesy of University of Huddersfield) [9] 35 5.4 Floor stacking: a) block stacking and b) line stacking [19] . . . 38
5.5 Conventional storage layout [20] . . . 39
5.6 Non-conventional storage layouts (L: flying-V, M: fishbone, R: chevron) [14] . . . 39
5.7 Example of routing methods for a single block warehouse [12] . . . 45
5.8 Gravity flow rack [21] . . . 47
5.9 Picking from conventional racks vs. picking from a flow rack [9] . . . 47
5.10 Movable aisle rack [19] . . . 48
5.11 Drive-in rack [19] . . . 48
5.12 Turret truck [22] . . . 49
5.13 Reach truck [www.raymondhandling.com] . . . 49
5.14 Order picking truck [22] . . . 50
5.15 Order picking cart [22] . . . 50
5.16 Vertical carousel [23] . . . 53
5.17 Horizontal carousel [22] . . . 53
5.19 Working principle of a VLM [25] . . . 54
5.20 Concept of an A-frame dispenser [26] . . . 55
5.21 Concept of AS/RS (Courtesy of Stöcklin Logistik AG) [22] . . . 56
5.22 Classification of AS/RS options [27] . . . 57
5.23 Main components of an AVS/RS [28] . . . 58
5.24 AVS/RS by KNAPP [29] . . . 58
5.25 AVS/RS by Swisslog [30] . . . 58
5.26 Autostore AVS/RS [31] . . . 59
5.27 Kiva Systems AGVs [32] . . . 60
5.28 Totes with candidate picking items illustrate difficulties in automated item picking [33] 60 5.29 Grasping with suction cups [33] . . . 61
5.30 Grasping with an under-actuated robotic hand [33] . . . 61
5.31 Robo-Pick item picking cell [www.ssi-schaefer.us] . . . 62
4.1 Time related indicators definition [16] . . . 20
4.2 Productivity related indicators definition [16] . . . 21
4.3 Cost related indicators definition [16] . . . 21
4.4 Quality related indicators definition [16] . . . 22
4.5 Direct indicators classified [16] . . . 23
4.6 Indirect indicator themes [16] . . . 23
4.7 Practice or attribute factors higly correlated with efficiency [1] . . . 26
5.1 Typical division of warehouse space [7] . . . 36
A.1 List of warehouse practices and attributes investigated in the second stage of cor-relation by Johnson & McGinnis [1] . . . 75
Introduction
A warehouse or Distribution Center (DC)1 can be considered to be a facility that acts as a node within a supply chain/network where goods are accumulated, temporarily stored and distributed from. The flow of goods from supplier to customer thus pauses for a period of time in a warehouse node [10]. In Europe alone, the logistic capital volume of warehousing was estimated around 242 billion euro for the year 2010 [34]. This was around 26% of the total logistics capital volume of Europe for that time [34]. By definition, it is impossible to maxi-mize the economic efficiency (and thus profitability) of any operation without maximizing the operational/technical efficiency [2]. Improving the operational efficiency of warehouses thus has substantial economic potential and is key in achieving high profitability.
As Johnson & McGinnis [1] state; "lacking a general understanding of warehouse technical efficiency and the associated causal factors limits industry’s ability to identify the best oppor-tunities for improving warehouse performance". Understanding the features that are essential to the operational efficiency of the best-in-class performers is thus of great importance for im-proving the warehouses that operate inefficiently. Imim-proving the efficiency of the best-in-class performers requires an even more thorough understanding of the own operations, combined with strong innovation capabilities. It has been noted that a clear overview of the state-of-the-art development in assessing and improving the operational efficiency of warehouses is lacking. Scientific work on the subject is often focused on very specific theoretical problem areas while a strong impact of this work on industry practice seems missing [13].
The main goal of this research is to survey the state-of-the-art developments in assessing and improving the operational efficiency of warehouses. In this way, researchers and practitioners are provided the basic knowledge they need to identify fruitful opportunities for improvement. To achieve this goal, the following research questions need to be answered:
• How can operational efficiency and the related concepts of productivity, effectiveness and performance be defined?
• What is the role of warehousing in supply networks and what operations are executed? • How is the total operational efficiency of warehouses measured?
• Which warehousing features2 are found to have a significant impact on the total
oper-ational efficiency?
• How can the operational efficiency of warehouses be improved?
1
In literature sometimes a distinction is made between warehouses and DCs. A DC is then considered as a special type of warehouse that has multiple suppliers and/or customers and has a high throughput rate and/or limited storage/fast moving goods. For this report this distinction is only considered when the term DC is explicitly used.
2
"Features" in this research encompasses concepts, methodologies, technologies, decisions, characteristics etc.
Due to the academic nature of this work, the focus of investigation will be on scientific literature that addresses the total operational efficiency of warehouses in the operational phase. This excludes many analytic and simulation studies performed on specific focus areas. These studies often only use theoretical cases, lack validation with industry practice and the work usually only considers limited (very specific) measures on performance. Findings on the operational efficiency of specific topics, such as order picking or the storage location assignment problem, are being treated to an appropriate level. Deeper investigation of these specific topics/focus areas is considered as material for follow-up studies. Directions for improvement of the operational efficiency are focused towards those features that are found to have a significant impact on the total operational efficiency.
This report is structured as follows. The next chapter will elaborate the definition of opera-tional efficiency and the adjacent terms. The definitions are considered generic for the use in analyzing/improving industrial systems. Chapter three will introduce the field of warehous-ing. The role of warehouses in supply networks will be discussed as well as the operations that take place within a warehouse. The fourth chapter will look into the methods and find-ings of studies that measure the total operational efficiency of warehouses. Findfind-ings that indicate the specific impact of features on the total operational efficiency are shown where this was investigated. In this way it becomes clear which features are known to drive the total operational efficiency. Chapter five will present options, considerations and directions for improving the operational efficiency based on the findings from the previous chapters. Finally, the conclusions will be presented and some recommendations will be given for future research.
Defining Operational Efficiency
When studying the topic of operational efficiency, one comes across many definitions and a plurality of terms that are used interchangeable and/or without a proper definition. In the literature studied for this research, this encompasses the following five terms:
• Productivity • Efficiency • Effectiveness • Performance
• Operational Efficiency
Despite that all of these terms are related, the interchangeable use is not always justified and the lack of proper definition leads to a lack of clarity. This chapter has the purpose of promoting the mutual understanding of those involved on the topic of operational efficiency. Clear definitions are given for the five terms presented and their mutual relations are shown. The terms are defined for the purpose of analyzing and/or improving industrial systems. Fig-ure 2.1 shows the concept of a black-box approach that is used in analyzing and/or improving processes within such a system.
Figure 2.1: General process view
In this black-box approach, a process can be seen as "a series of transformations that occur during throughput which result in a change of the input elements in place, position, form, size, function, property or any other characteristic" [35]. The output (or result) can, in its broadest sense, be anything that is produced by the process. Often it refers to the finalized product or service. However, byproducts, waste etc. can also be seen as process outputs. Inputs can be defined as anything (effort, (monetary) cost, resources etc.) that is used by the process to produce the output.
2.1
Productivity
Productivity (P ) is commonly recognized as the ratio between the outputs and inputs of a process [2, 35–37]. Output is in this context often implied as the intended, or useful output that has passed the minimum output quality threshold. This means that unwanted byprod-ucts, defect prodbyprod-ucts, general waste etc. are often not considered as outputs in determining productivity.
P roductivity = Outputs
Inputs (2.1)
This definition of productivity implies that there are only five situations in which the pro-ductivity can be improved [37]:
1. Output increases faster than input; the increase in input is proportionately less than the increase in output
2. More output from the same input 3. More output with a reduction in input 4. Same output with fewer inputs
5. Output decreases, but input decreases more; the decrease in input is proportionately greater than the decrease in input
Computing the productivity of a process/system is quite straightforward if there is only one input and one output. Real systems however tend to have multiple inputs and/or outputs, making productivity calculations significantly more challenging. Considering all inputs and outputs in the determination of productivity is referred to as total productivity [36] or Total Factor Productivity (TFP) [2, 37]. Only considering a subset of inputs and/or outputs is referred to as partial productivity [2, 36, 37].
An important thing to recognize is that measurements of productivity are always taken with a certain overall quality level of the in- and outputs. If one is comparing the productivity of a process with similar processes, or with itself over time, differences in productivity might be explained by differences in the in- or output quality [2]. The quality of labor is a good example of how the quality of an input can affect productivity. If productivity is for example measured in output per man-hour, high expertise levels of the workforce may lead to a higher output with the same number of man-hours invested (i.e. a higher productivity) [2].
2.2
Efficiency
Efficiency is considered to be a measure for how well the inputs are used in a system to produce a certain output. It can be defined as "the minimum resource level that is theoretically required to run the desired operations in a given system compared to how much resources that are actually used" [36]. Two parts can be identified in this definition.
The first is "the minimum resource level that is theoretically required to run the desired operations". This is in fact the theoretical maximization of the ratio between the in- and outputs (i.e. the maximum productivity that can theoretically be achieved). This will be denoted by Pmax.
The second part is "the actual usage of resources to run the desired operations". This can be viewed upon as the actual inputs used to produce a certain output, which is in fact a measure for the actual productivity (Pactual).
Efficiency is considered to be the ratio between these two productivity numbers [18]:
Ef f iciency = Pactual
Pmax (2.2)
As a result, efficiency always ranges from zero to a maximum of one. Where an efficiency of one implies that maximum productivity has been achieved. In practice it is often quite difficult, if not impossible, to find the true Pmax. Terms like "expected" [36] or "standard" [35]
are often used instead to indicate the currently known best practices. The equation for calculating efficiency then becomes:
Ef f iciency = Pactual Pexpected = Outputactual Inputactual Outputexpected Inputexpected (2.3)
Using the best-in-class results at a certain moment in time as the Pmax baseline can result in efficiency scores higher than one if those best-in-class results are surpassed. If this is the case, the baseline is updated to reflect the new best-in-class results such that the efficiency drops back to one or below [35].
Often in literature, efficiency is stated as the ratio between the expected input and the actual input only [35, 36]. As Eq. (2.3) indicates, this only holds when the actual output is equal to the expected output (which is also acknowledged by Veeke et al. [35]).
2.3
Effectiveness
Effectiveness is a term that is often used interchangeable with efficiency and is therefore shortly discussed here. Effectiveness is considered to be a measure for how well a system is aligning with its (strategic) goals [35,36]. To view this as an equation, it can be displayed as a ratio between the actual output/result and the intended (or expected) output/result. Despite the simplicity of the equation, depending on the goals, effectiveness can be rather hard to quantify [36]. Using the definition presented, effectiveness is a different and much wider concept than efficiency. The interchangeable use with efficiency may however be justified in only one specific case. If one is setting goals in terms of achieving a certain level of productivity, the effectiveness ratio in Eq. (2.4) becomes equal to the general definition of efficiency described with Eq. (2.2).
Ef f ectiveness = Outputactual Outputintended
(2.4)
2.4
Performance
The last term that one often comes across in studying the topic of operational efficiency is performance. According to Coellie et al. [2], performance is a relative concept. It describes how well a system is scoring relative to either itself at a different moment in time or relative to
different systems that are comparable. Performance thus always provides a value judgement over some measure. Performance indicators can be identified at all system levels and can be used to benchmark virtually anything. This means that performance measures can include scores for productivity, efficiency, effectiveness and profitability but are not limited to those [36, 37]. This is why performance is also referenced to as an "umbrella" term [36].
2.5
Operational Efficiency
In the previous sections, definitions are stated for the terms of productivity, efficiency, effec-tiveness and performance. Also their mutual relations are shown. The general concept of efficiency in industrial systems is thus clarified by those sections. But, then what exactly is
operational efficiency?
Obtaining a precise definition of operational efficiency from literature has proven to be quite difficult. Lee and Johnson [18] shortly define operational efficiency as "the ability to deliver products and services cost effectively without sacrificing quality". This is however not a very comprehensive definition that distinguishes operational efficiency from the general concept of efficiency. After their short definition, Lee and Johnson use operational efficiency as a synonym for technical efficiency. This is found to be done quite regularly in literature (see also for example [38]) and will also be adopted in this report.
Technical efficiency is commonly defined in the field of (production) economics. A system (or company) is considered to be technically efficient if it is producing on its production function (also known as the production frontier) [2]. The production function represents the maximum attainable quantities of output given quantities of input. Figure 2.2 provides a visualization of a production function. Firm B here operates on the production function and thus is con-sidered to be technically efficient. Firm A operates underneath the production function and thus is considered to be technically inefficient. Two firms can both be technically efficient (operating on the production function) while they operate at different productivity levels. This means that scale economies apply such that it can be possible to operate more efficient at a different output rate [2].
In (production) economics, technical efficiency is distinguished from allocative efficiency which, multiplied together, form the economic efficiency (Eq. 2.5) [2]. Using this defini-tion, operational/technical efficiency mainly refers to measures of productivity using non-monetary quantities of in- and output. Allocative efficiency incorporates price levels of the in- and outputs in order to determine the optimal mix of in- and outputs for the purpose of profit maximization [2]. As a result, this means that the technical efficiency of a system can be at its maximum without having a maximized economic efficiency (and thus optimal prof-itability). It is however not possible to maximize the economic efficiency without achieving technical efficiency.
Economic Ef f iciency = T echnical Ef f iciency × Allocative Ef f iciency (2.5)
To summarize: operational efficiency is considered as a synonym for technical efficiency
(which is well defined in economics). The preposition of "operational" thus mainly refers to measures that state non-monetary quantities of in- and outputs.
The Field of Warehousing
This chapter gives an introduction to the field of warehousing. The role of warehouses in supply networks is discussed and the operations that are being performed within warehouses are described. With this chapter, the basic knowledge of warehousing is acquired before pro-gressing towards the assessment and improvement of the operational efficiency of warehouses.
3.1
The role of the warehouse
Holding inventory and performing (extra) material handling steps are inevitably associated with costs. Because warehouse core-activities can be considered as non-value-adding1, often the question is asked why we should have warehouses at all? This question can be answered by looking at the functions that warehouses fulfill in a supply chain/network. At its core, a warehouse fulfills one or two major functions: the buffer function and/or the consolidating function [10].
Buffer function
Holding inventory is inevetably associated with costs. Storage facilities, insurance, loss of products, energy, labor, etc. all have their price. It is thus obviously desired to keep inventory levels (and therewith the inventory costs) in supply chains as low as possible. In modern days (and all foreseeable future), it is however still a utopia for virtually any supply chain to get rid of all inventory. Erratic and unpredictable supply and demand rates, together with limitations in (production) capacity and order fulfillment speed, make that buffers are still indispensable in a variety of (production) stages and (geographical) locations [9].
Despite the costs associated with holding inventory, holding a buffer might in some cases lower the total costs or even create value. Discounts in bulk buying and lower unit costs due to advantages in shipping large quantities might make up for the extra holding costs [9]. Some products, the so called investment stocks, can even increase in value over time (like wine for example) [9]. Making it sometimes an economically viable decision to store more inventory than strictly necessary.
To summarize: the buffer function is a necessity to overcome constraints of location and
time and can possibly also provide cost advantages due to economies of scale and/or time-dependent product value.
Consolidating function
In the past, warehouse operations were mainly focused around the storage of goods (the buffer
1Activities are considered to be value-adding when they add value to the product/service itself and thus
con-vince customers to acquire those products/services [39]. Core-activities of a warehouse include the (un)loading of carriers, put-away, storage and order picking. These activities do not alter the products itself, they merely transport/relocate them within the boundaries of the warehouse. These activities thus do not increase the customer value but do add additional costs to the product.
function) [9]. In the course of years however, warehouse operations have become significantly more complex and are now an integral part of the supply chain. Down a supply chain there are different requirements imposed on the shipments. Large shipments for example might be broken down into smaller portions further downstream. Different goods from different suppliers might be combined to form composite orders and transshipment might be necessary to change mode of transport or vehicle type. These type of operations together form the consolidating function. The consolidating function is thus necessary in order to facilitate the changes in shipping requirements down the supply chain. It encompasses all activities that are performed in the warehouse to make the incoming goods ready for shipment according to customer demands. Value-adding activities are also covered within the consolidating function by using this definition. These activities are however still not considered as core-activities of a warehouse.
Types of warehouses
Warehouses can be typified by lots of characteristics. Some of the most well-known ways of classification are by the type of goods that are being handled and/or by the specific role/place they take in the supply chain. Handling perishables, raw materials or finished consumer goods for example all impose different requirements on the facilities, equipment and operations. Also different characteristics can be expected from operations that are close to a manufacturer opposed to operations that are close to the final consumer. Some common types of warehouses found in literature are [9, 10]:
• Raw materials storage facility
Storage and distribution of raw materials, often early in the supply chain and in large quantities.
• Intermediate, postponement, customization or sub-assembly facilities
Temporary storage of intermediate products to facilitate the production process. Often extra services such as repacking, relabelling or even some assembly steps are performed. • Finished goods storage facility
These facilities store ready-to-sell goods mainly to provide a buffer/safety stock. • Retail distribution center
Regularly supply multiple retail stores. Demand can often be predicted with reasonable accuracy but the amount of goods handled is typically huge.
• Catalog fulfillment or e-commerce center
These facilities often process large amounts of small orders consisting of only a few products. After the order is received, a very quick response is expected.
• Service parts distribution center
These centers store and distribute spare parts. There is often a wide variety of products stored while the demand for each product is very erratic and unpredictable. If an incoming order is an emergency order (i.e. vital components have been broken down), response must be extremely fast.
• Perishables handling center
These facilities distribute products such as food, flowers or vaccines. They can require cooling, making storage space very expensive. These products typically have a dwell time in the warehouse of only a couple of hours.
• Sorting center
Can mainly be found in the postal service industry. Letters or parcels with a variety of destinations are collected, sorted, consolidated and shipped.
• Reversed logistics center for returned goods or waste
In today’s market, products do not only flow from producer to consumer anymore. Also the reverse is possible as goods can be returned from customer to the producer/ven-dor. Also products and byproducts (such as packaging) can be discarded and handled in waste streams. Warehouses play major roles in buffering and consolidating these reversed streams.
3.2
Warehouse operations
In the previous section it is shown that there are lots of different types of warehouses, each with its own specific purpose. The operational activities of these different types of warehouses will also differ when looking into detail. In general however, many similar activities take place. Figure 3.1 gives an overview of these general warehouse operations and the interrelations between them. In practice it might be less easy to distinguish the different activities as clearly as in this section because intermixing or merging of tasks can occur.
Figure 3.1: General overview of warehouse operations
In figure 3.2, a typical subdivision is given of how the total warehousing costs are divided over the different warehouse operations. It can be seen that order picking is by far the most expensive activity, accounting for around 35% of the total costs. Both Bartholdi & Hackman [10] and de Koster et al. [12] state an even higher number of around 55% of all operating expenses.
Hamdan & Rogers [11] give an overview of the typical major cost elements of a warehouse (figure 3.3). Labor is by far the most expensive cost element. This is also being supported by Richards [9] who states that labor accounts for around 48 to 60 percent of all warehouse costs.
Hamdan & Rogers [11] also show how labor is typically divided over the different warehouse activities (figure 3.4). It must be mentioned that all of the figures shown are for the typical warehouses. Figures for highly automated warehouses or special types of warehouses such as pure cross-docking facilities or refrigerated warehouses might differ significantly.
Figure 3.2: Typical subdivision of activity costs as a percentage of total costs [9]
Figure 3.3: Typical warehouse major cost elements [11]
The following subsections will elaborate the different warehouse operations in more detail. Much of the work in these subsections is based on the work of Richards [9] and that of Bartholdi & Hackman [10].
3.2.1 Inbound operations
Receiving
Warehouse operations start with the receipt of goods. Usually notification is made of the shipment upon arrival for scheduling purposes. After the carrier arrives at the warehouse it is unloaded. How the carrier is unloaded depends in great deal on how the load arrives (pallets, cartons, loose items etc.), the equipment available and the level of automation. The goods can be unloaded into a staging area nearby the carrier or can be processed directly. Using a staging area requires extra material handling steps inside the warehouse but may increase the availability of the carrier for transport as unloading might be faster (depending on the equipment available and the process in detail).
Gu et al. [15] state that three scenarios can be distinguished for the availability of knowledge about incoming/outgoing shipments:
• No knowledge about the incoming/outgoing shipments.
• Partial statistical knowledge of arriving and departing processes, such as the average level of material flow from an incoming carrier to an outgoing carrier.
• Perfect knowledge of the content of each arriving carrier and each departing carrier. The availability of this knowledge determines the options one has in organizing the receiving and dispatch processes.
Inspection/quality control
Incoming goods are often subject to inspection/quality control prior to further processing. This is done to ensure that the right goods have arrived in the right quantity and condition. Richards [9] however suggests that when deficiencies in this stage are found, it is in fact already too late and costs will be made inevitably. Instead of checking all incoming goods, sample checking or total Good Faith Receiving (GFR) are sometimes used to reduce the inspection cost.
Preparation
Some extra activities might be necessary to make the goods suitable for further processing. Determining the size and weight of incoming goods might for example be necessary to deter-mine the storage location. Unpacking/repacking can be necessary if the goods are handled or stored in different packing than they arrived.
Put-away
All different products that need to be stored inside the warehouse are called stock keeping units (SKUs). After receiving, all SKUs are transferred to their storage location. This storage location is usually dictated by the warehouse manager or retrieved from the warehouse management system (WMS). The transfer to the storage location can be done by employees, using various types of material handling equipment. As indicated in figure 3.4, put-away is quite a labor-intensive activity as it involves lots of traveling within the facility. There are also more automated solutions for put-away (e.g. automated storage and retrieval systems (AS/RS)).
3.2.2 Storage
After the SKUs have been put into their storage location, they stay there until they are needed again for order picking. The time that this takes can be a matter of hours with very fast-moving SKUs but can reach up to multiple years with very slow-moving SKUs (some investment stocks for example). Warehouses need to accommodate different storage condition requirements for different goods. Perishables for example might need a cooled environment while other goods need to be stored at room temperature within acceptable humidity levels.
3.2.3 Outbound operations
Order picking
After a customer order is received (and the items are in stock), retrieving all the ordered items from their storage locations is the next thing to do. Order picking can impact the customer service level to a great extent as it lies at the heart of the order fulfillment cycle. If, for example, the wrong items or quantities are picked and shipped, the customer does not get what he/she ordered. Customer orders are divided into a number of picking lines that tell order pickers which items to pick, where-from and in what quantities [10]. Each picking line thus consists of a number of items that correspond with one specific SKU that is stored somewhere inside the warehouse. The two-dimensional interface between the order picker and the storage location from where the SKU is picked from is called the pick face. Different types of material handling can be seen in order picking. With pallet picking, the items are handled per pallet. Layer picking involves handling pallet layers. In full-case/carton picking, items are handled per case/carton. And with piece/unit/item/broken-case picking, items are handled as individual pieces. Combinations of these material handling types may occur within the same order.
As indicated in the beginning of section 3.2, order picking is usually by far the most expensive warehouse activity. Extensive labor is required as there are is lots of material handling involved (including lots of traveling through the facility). Figure 3.5 shows the major time-consuming elements of manual order picking. Just like with put-away, automated solutions are also available for order picking. The AS/RS systems mentioned previously for example can take care of entire put-away and order picking operations.
Figure 3.5: Typical order picking time consumption [10] Aggregating and preparing for shipment
Aggregating the different items of an order can take place during order picking (sort-while-pick) or after order picking (sort-after-(sort-while-pick). If the aggregating takes place after order picking, this is often done in a dedicated sorting area inside the warehouse.
Orders that are gathered completely must be packed and labeled. Other preparation before shipment (such as pallet-building) also takes place during this phase. Some final inspections or checks might also be performed before the goods can be designated as ready for shipment.
Dispatch
When goods are ready for shipment, they are transferred to the dispatch area and loaded into the carrier. Depending on the availability of the carrier and the need to rearrange the load in a different order (trucks for example are usually offloaded last-in first-out (LIFO)), a staging area near the dispatch location might be used. Just like with receiving, using a staging area for dispatch can also increase the number of material handling steps and therewith the operating costs.
3.2.4 Cross-docking
With cross-docking operations, goods are transferred almost directly from the incoming carrier to the outgoing carrier. Figure 3.6 shows the basic idea of this kind of operation. Any consolidation with the goods from other carriers or SKUs from the storage area needs to be done in-line with the transshipment between the carriers. Storage of goods in cross-docking is basically not present, or limited to a very short period of time in a staging area.
Cross-docking has a couple of advantages in comparison to regular warehouse operations. Because cross-docking goods are not stored inside the facility, no costs are made in put-away, storage and order picking of those goods. The highly complex operations of cross-docking need however a high degree of coordination. Customers and suppliers need to actively participate in the process. They need to ensure the right ways of presenting products, punctuality in arrival times and provide all other types of support that are necessary for smooth operation. The warehouse operator is also challenged with the task of assigning carriers to docks such that the internal transportation of goods is optimized in terms of travel distance [10]. And, although the facility doesn’t need dedicated storage space (apart from the staging areas), it needs to be large enough and well managed to avoid congestion of transfer equipment [9, 10].
3.2.5 Value-adding services
Besides the core-activities of the warehouse, value-adding services can be provided. These value-adding services (VAS) are also known as value-adding logistics (VAL) and blur the boundary between manufacturing and warehousing. The warehouse is the location of choice for these activities because items are touched there anyway. In this way it becomes possible for manufacturers to push product differentiation further downstream in order to serve markets better to their local needs [10].
De Koster et al [40], divide VAS over high-end and low-end VAS. High-end VAS deliver great value to customers. Examples of this are sub- or final-assembly, (re)configuration, installation, repair/refurbishment and sterilization. Low-end VAS only deliver minor value to the customer. Examples of this are (re)labelling, pricing, (re)packing, kitting and adding manuals.
Assessing Warehouse Efficiency
4.1
Introduction
Performance measurement helps managers to evaluate operations and to take appropriate actions in accordance [16]. This chapter will explain how operational efficiency/performance is measured in warehouses and will elaborate the main findings of publications found on this subject. In this way it becomes clear which measuring techniques can be applied and which warehouse features are found to have a significant impact on the total operational efficiency. In general there are three different approaches for performance evaluation: benchmarking (and measuring KPIs in practice), analytic models, and simulations [13]. In this research the focus is on efficiency/performance evaluation of actual warehouse operations. Analytic models are usually design-oriented; they are used to test different design possibilities quickly but don’t necessarily reflect all system details sufficiently [13]. Altough simulation models are analysis-oriented [13], their usage is also more towards design/operational improvement than towards evaluation of the current state. Because of these reasons, the focus of this chapter lies on the measuring of KPIs and the benchmarking of warehouse operations.
The work in this chapter is not purely dedicated towards operational efficiency. There is looked into operational performance in a broader sense. This is done because in literature, the distinction between operational efficiency and performance is not made very clearly and an overlap of concepts is found. Classification and definition of indicators can be done in multiple ways and there is not much consistency found for this. It must also be mentioned that solely focusing on productivity/efficiency might not be very effective as there is an interaction with other performance indicators such as quality (see chapter 2).
4.2
Key Performance Indicators
Measuring performance by a set of Key Performance Indicators (KPIs) is probably the most common practice. Measuring KPIs is often quite easy as many of them are expressed as simple ratios. Bartholdi & Hackman [10] state that ideally, all KPIs should be unbiased, customer-focused and consistent with corporate goals. In practice however, most KPIs won’t fit these criteria perfectly [10]. The main purpose of this section is to identify important and/or commonly used indicators as well as their definitions. There is no intention of developing an extensive list of all KPIs that can be used in practice. Such a list would be of little value to this research as it doesn’t present directions for improvement. Neither does it reveal where current research is focused on.
Staudt et al. [16] have recently conducted an extensive literature review on warehouse KPIs that is in line with the scope of this research. They analyzed 43 journal publications (out of an initial search result of 1500 articles) in which they focused on the operational performance
indicators that can be used in short-term warehouse management. Work on strategical and tactical decision making (e.g. warehouse location, design) has not been evaluated. The remainder of this section summarizes the main findings of the study performed by Staudt et al.
Staudt et al. have split the performance indicators into direct and indirect indicators. Direct indicators (’hard’ metrics) are those that are expressed by simple equations and can be mea-sured directly (e.g. order cycle time). Indirect indicators (’soft’ metrics) are more complex concepts that are difficult to measure with simple expressions like ratios (e.g. customer sat-isfaction). Advanced measurement tools such as regression analysis, fuzzy logic or DEA are necessary to compute these indirect indicators.
Direct indicators
Direct indicators can be characterized as a measure of time, quality, cost, or productivity. Flexibility is sometimes also used but is more often found to be measured indirectly. Staudt et al. have merged very specific direct indicators into their parent category. The example is given of ’time in quality control in receiving’, which is included in the parent indicator ’receiving time’. In this way it becomes clearer where research is focused on.
Time related indicators
Table 4.1 gives an overview of the time related indicators. Most of the warehouse activities are covered with dedicated indicators (e.g. receiving time, putaway time) except for those more related to inventory control (e.g. the total time a product has spend in inventory). The vast amount of publications that address the order (and delivery) lead time might suggest that there is a strong customer-focus in delivery speed.
productivity related indicators
The indicators related to productivity are shown in table 4.2. The strong focus of literature on labor productivity is not very surprising given the share that labor has in the total warehousing costs (section 3.2). Productivity in the major cost component of space is also fairly represented (e.g. warehouse utilization). It is however notable that productivity indicators are poorly represented in the cost components of technology and material handling equipment. It is not clear if this is due to the the search methods of Staudt et al. or because of a total absence in literature.
Cost related indicators
In table 4.3 the cost related KPIs are shown. Cost indicators don’t seem to be used fre-quently on the operational level. As stated in chapter 2, operational/technical efficiency is usually measured using non-monetary measures. Financial measures are often lagging and cannot capture the fluctuating operational characteristics with enough detail [2]. No indi-vidual warehouse activities are represented by cost indicators. This is not very surprising as activity-based costing is complicated and time consuming and so managers prefer to use cost-related metrics for global processes only [16].
The focus in literature for the cost related indicators is on the inventory cost. Following the definitions from the table, this is however an incomplete way of measuring it [16]. All expenses associated with holding inventory should be considered [16]. This includes for ex-ample the holding cost, depreciation and insurance. Other cost indicators can also present a misleading image. The "cost as a percentage of sales" for example is not focused entirely on the warehouse operations alone as it can also fluctuate by the amount of sales. In this way it
doesn’t meet the "unbiased" criteria presented by Bartholdi & Hackman [10]. The result can for example be offset by sales prices or marketing.
Quality related indicators
The quality related indicators are shown in table 4.4. The emphasis here is on on-time delivery and customer satisfaction. The focus on on-time delivery is a bit strange as "perfect orders" reflects the delivery performance in a more comprehensive way [41]. The perfect orders indicator doesn’t seem to be used as widely.
In table 4.5 the direct indicators are put in the total spectrum of warehouse operations. The table shows that the number of indicators for outbound processes is much higher than for inbound processes. This indicates that outbound processes are considered more critical than inbound processes and are therefore studied more extensively [16]. Gu et al. [15] also concluded earlier that the research on receiving is limited. This indicates that there has not been a large shift of focus in the meantime. It is important to note for table 4.5 that empty cells do not mean that an indicator doesn’t exist. It means that the literature considered in the work of Staudt et al. hasn’t stated them.
Indirect indicators
Indirect indicators, like the customer perception for example, are a lot harder to measure than direct indicators. Staudt et al. concluded that there is no consensus in literature about the definitions and the ways of measuring the indirect indicators. Therefore they have opted for a different approach than in analyzing the direct indicators. The indirect indicators found in literature have been assigned to themes and some details are given on them individually. Table 4.6 gives an overview of the themes and indicates the focus of previous research.
• Labor
As can be seen in table 4.6, labor is by far the most prominent theme. This is again not very surprising as it was shown in section 3.2 that labor is the major warehouse cost element. Another reason for the importance of labor indicators is that employee perfor-mance can directly affect the customer service level [16]. Examples of indirect indicators belonging to this theme are: supervisory coaching of employees (which is positively cor-related to employee satisfaction and performance) [42]; front-line employee performance and interdepartmental customer orientation [43]; trends in employee performance [44]. • Value-adding activities
Staudt et al. do not state any real indicators for the value-adding activities except for the amount and complexity of those performed. It speaks for itself however that the performance of each value-adding activity can be assessed individually.
• Inventory management
Although inventory management is often not considered as a warehouse function, Staudt et al. state that the relations between inventory management and warehouse automation are getting closer together. Examples of indicators for this theme are: the accuracy of logistics information and emergent order handling.
• Warehouse automation
The degree of warehouse automation can for example be measured by the level of tech-nology used or by the number of available information systems. Staudt et al. [16] quote that "high levels of information system utilization in order selection activity should have positive influences on delivery".
• Customer perception
Customer relationship and customer satisfaction are by Lu & Yang [45] identified as the most satisfactory performance variable by managers1. They considered the degree of customer response, consisting of pre- and post-sale service and responsiveness to the customer.
• Maintenance
Sohn et al. [5] used structured equations modeling to construct a warehouse logistic index. They showed that after manpower management, facility management has the highest impact on warehouse capability. Facility management is measured through the following variables in the study of Sohn et al.: level of warehouse deterioration, suit-ability of protection against fire, degree of inefficiency in warehouse space, suitsuit-ability of warehouse design and finally the level of cooperation with a facility-related department.
Table 4.1: Time related indicators definition [16]
Indicator Definition Np
Order lead time Lead time from customer order to customer
accep-tance 9
Lead time from order placement to shipment
Receiving time Unloading time 5
Order picking time Lead time to pick an order line 4 Delivery lead time Lead times from the warehouse to customers 3 Putaway time Lead time since a product(s) has been unloaded to
when it is stored in its designated place
2
Queuing time Time that products wait on hold to be handled 2 Shipping time Lead time to load a truck per total orders loaded 2 Dock to stock time Lead time from supply arrival until product is
avail-able for order picking 2
The amount of time it takes to get shipments from the dock to inventory floor without inspection
Equipment downtime Period in which an equipment is not functional, down-time incurred for repairs
1
Np: Number of publications that address indicator
1
Its worth mentioning that Lu & Yang [45] present three critical logistics service capabilities: innovation capability, the flexible operations capability and the customer response capability.
Table 4.2: Productivity related indicators definition [16]
Indicator Definition Np
Labor productivity Ratio of the total number of items managed to the amount of item-handling working hours
11
Throughput Items/hour leaving the warehouse 10
items/m2 per day
Shipping productivity Total number of products shipped per time period 7
Transport utilisation Vehicle fill rate 5
Warehouse utilisation The average amount of warehouse capacity used over a specific amount of time
4
Inventory space utilisation Rate of space occupied by storage 3 Outbound space utilisation Utilisation of the area inside the warehouse used for
retrieving, order picking, packing and shipping
3
Picking productivity Total number of products picked per labor hours in picking activity
3
Receiving productivity Number of vehicles unloaded per labor hour 2 Turnover Ration between the cost of goods sold and the average
inventory
2
Np: Number of publications that address indicator
Table 4.3: Cost related indicators definition [16]
Indicator Definition Np
Inventory cost Total storage costs/unit 7
Inventory level (measured monetarily)
Order processing cost Total processing cost of all orders per number of orders 3 Labour cost Cost of personnel involved in warehouse operations 2 Distribution cost The mean number of vehicles and total travel distance
per day provide measures of distribution costs
2
Cost as a percentage of sales
Total warehousing cost as a percent of total company sales
3
Maintenance cost Costs of building maintenance 2
Equipment maintenance
Table 4.4: Quality related indicators definition [16]
Group Indicator Definition Np
A On-time delivery Number of orders received by customer on
or before committed date 10
Orders shipped on time
Number of orders shipped on time per to-talorders shipped
B Order fill rate Orders filled completely on the first ship-ment
5
C
Physical inventory accuracy
Measures the accuracy (by location and units) of the physical inventory compared to the reported inventory
5
Picking accuracy Accuracy of the orders picking process where errors may be caught prior to ship-ment such as during packaging
3
Storage accuracy Storing products in proper locations 4 Shipping accuracy Number of errors free orders shipped 2 Delivery accuracy Number of orders distributed without
in-cidents
2
D
Stock-out rate Number of stock products out of order 4 Scrap rate Rate of product loss and damage 2 Cargo damage rate Number of orders damaged during
deliv-ery activity
1
E Perfect orders Orders delivered on time, without damage and with accurate documentation
2
Customer satisfac-tion
Number of customer complaints/number of orders delivered
8
Group Definition: A Punctuality; B Completeness; C Correctness; D Breakage; E -Customer Satisfaction.
Table 4.5: Direct indicators classified [16]
Table 4.6: Indirect indicator themes [16]
Indicator theme Np
Labour 7
Value-added logistics activities 4 Inventory management 4 Warehouse automation 4 Customer perception 3 Flexibility 3 Maintenance 1 Np: Number of publications
4.3
Benchmarking studies
While a set of KPIs can be useful for managers to use in the day-to-day operations and in the evaluation of the performance of an individual warehouse over time; it presents some serious challenges when one wants to compare the relative performance of a number of warehouses. Different warehouses may not be comparable by their KPIs because of differences in opera-tional characteristics and/or the way in which KPIs are measured. Measuring performance by a set of KPIs can also bring forth other challenges. For example: when half of the KPIs have improved with time while the other half have deteriorated, has the overall performance become better, worse or has it stayed the same? Benchmarking studies are used to overcome these challenges and to measure the performance in a more systematic way. Gu et al. [13] define warehouse benchmarking as "the process of systematically assessing the performance of a warehouse, identifying inefficiencies, and proposing improvements". Hackman et al. [4] state that "benchmarking is the process of gathering and sharing assessments of performance of some aspect of an organization, and may include developing an improvement plan of action based on the assessment". Benchmarking studies usually have a way of aggregating all kinds of different process inputs and outputs to determine an overall performance score. The TFP mentioned in section 2.1 is an example of a performance measure that can only be determined using benchmarking. In fact, many of the indirect indicators stated in section 4.2 can only be computed through benchmarking.
Data envelopment analysis (DEA) is found to be the most used method for benchmarking warehouses. With DEA, an efficient frontier (such as described in chapter 2) for a set of decision making units (DMUs) is estimated using linear programming techniques [2]. The (in)efficiency of each DMU is then calculated relatively to the efficient frontier [2]. DEA thus always assumes that one or more best-in-class warehouses are operating efficiently and subsequently scales the relative (in)efficiency of the rest to those best-in-class performers. The next subsection will summarize the main results of studies that performed warehouse benchmarking in practice. After that a reflection will be given on these research efforts and findings. In this way it becomes clear which warehousing features are the main drivers of operational efficiency.
4.3.1 Benchmarking studies in practice
Johnson & McGinnis [1] use DEA to assess the operational efficiency of warehouses. They applied their method to a large sample of warehouses2 that have self-reported their data through internet. Because of this self-reporting, data accuracy could not be checked. There-fore Johnson and McGinnis have relied on outlier detection to assure the quality of results. A two-stage method was used to identify the correlation between efficiency and three types of properties: (i) operational policies; (ii) design characteristics; and (iii) attributes of the warehouse.
In the first stage, the efficiency was estimated using an input-oriented DEA approach based on an input-output model. The initial model (fig. 4.1) was reduced to a model with labor, space and investment as inputs and with broken case lines, full case lines and pallet lines as outputs. Value Added Services were deleted from model because they blur the line between warehousing and manufacturing. Returns processing was deleted because of a lack of data (only 20% of warehouses dealt with returns). The results indicated that 23% of the ware-houses included operate efficiently. The average efficiency over the entire sample was 66%,
2
with a standard deviation of 0.27. Johnson & McGinnis state that "this indicates either sub-stantial room for improvement, the existence of attributes that limit performance compared to peers, or both".
Figure 4.1: Initial input-output model of Johnson & McGinnis [1]
In the second stage of their method, Johnson & McGinnis search for practices and attribute that have a significant effect on the operational efficiency. The 33 practices and attributes investigated are listed in table A.1 in the appendices. Table 4.7 shows the practices and attributes found by Johnson & McGinnis that are highly correlated with efficiency. These highly correlated practices are elaborated below [1]:
• Seasonality (volume in peak month/average volume per month)
Fluctuating demand rates present challenges in the use of space and equipment. Tem-porary labor that is used in high demand periods can complicate things even further. • SKU churn (percentage of SKUs that change from year to year)
A higher SKU churn means that more effort needs to be put into ancillary matters as reorganizing storage locations. This has a negative effect on the efficiency.
• SKU span (total SKUs stored in the warehous annually)
The more SKUs a warehouse has, the harder it becomes to locate a particular SKU and to specialize in operations.
• Inventory ($) (average inventory level in dollars)
More efficient warehouses have better reordering practices such that they can fill orders with minimum inventory.
• Total replenishment (includes the replenishment transactions and is the anual total number or replenishments for all SKUs)
Highly correlates with SKU span and with lower efficiency levels. Although the average inventory levels can be lower with higher levels of replenishment, the SKU span effect dominates this.
• Temporary labor (measured as annual hours of temporary labor employed)
Correlated most negatively of all factors. Temporary workers are less familiar with operations and are therefore less efficient.
• Inventory turns (ratio of a warehouse’s annual shipment to its inventory measured in dollars)
Is correlated most positively with efficiency. Rapid turnover needs less storage. This reduces the amount of storage space and equipment while travel/searching times are shorter.
• Cross docking (does a warehouse perform cross docking y/n?)
Cross docking eliminates the buffer function altogether. The benefits from this seem to outweigh the organizational complexity of cross docking operations.
Table 4.7: Practice or attribute factors higly correlated with efficiency [1]
Observations Correlation coeffcients Significance level Seasonality 40 -0.268 ** SKU churn 41 -0.193 * SKU Span 29 -0.233 * Inventory ($) 46 -0.26 * Total replenishment 43 -0.253 * Temporary labor 33 -0.413 *** Inventory turns 36 0.342 ** Cross docking 44 0.246 *
*Significant at the 90% confidence interval **Significant at the 95% confidence interval ***Significant at the 99% confidence interval
Hackman et al. [4] performed an analysis of the operational efficiency of 57 warehouses. This study is the predecessor of that of Johnson & McGinnis [1] and also uses DEA on an input-output model (fig. 4.2). Hackman et. al. answer three main questions: Do larger warehouses perform more efficiently? Do capital-intensive warehouses perform more efficiently? Do non-union facilities outperform their non-union counterparts?
Figure 4.2: Input-output model of Hackman et al. [4] The following conclusions were drawn:
• Smaller warehouses tend to be more efficient than larger warehouses.
• Warehouses using lower levels of automation tend to be more efficient. This association is more pronounced in small firms.
• Unionization is not negatively associated with efficiency and in fact may actually con-tribute to higher efficiency.
Although the data collected dates back from 1992 to 1996, these conclusions may still be valid in today’s industry because of the underlying principles Hackman et al present.
In larger warehouses, travel distances increase, workflow visibility decreases and difficulties in communication and supervision arise. Hackman et al. state that these principles offset the benefits of economies of scale. A side note is made however that the losses in operational