Improving Road Transport Operations through Lean Thinking: a case Study

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Improving Road Transport Operations through Lean Thinking: A Case Study

1st Author

Prof. Dr. Bernardo Villarreal

Departamento de Ingeniería, Universidad de Monterrey, I. Morones Prieto 4500 Pte., San Pedro Garza Garcia, NL 66238, Mexico


2nd Author and Corresponding

Dr. Jose Arturo Garza-Reyes*

Centre for Supply Chain Improvement

The University of Derby

Kedleston Road Campus, Derby, UK, DE22 1GB


Tel. +44(0)1332593281

3rd Author

Dr. Vikas Kumar

Bristol Business School

University of the West of England

Coldharbour Ln, Bristol, UK, BS16 1QY


Tel. +44(0)1173283466

4th Author

Prof. Ming K. Lim

Centre for Supply Chain Improvement

The University of Derby

Kedleston Road Campus, Derby, UK, DE22 1GB


Tel. +44(0)1332591770

* Corresponding Author

Article word count: 9,585

Improving Road Transport Operations through Lean Thinking: A Case Study

Traditionally, logistics and transportation problems have been addressed through mathematical modelling, operations research, and simulation methods. This paper documents a case study where the road transport operations of a leading Mexican brewery organisation have been improved through lean thinking and waste reduction. Two lean-based principles and tools were combined; the Seven Transportation Extended Wastes (STEWs) and Transportation Value Stream Mapping (TVSM), and three systematic steps were followed to facilitate the improvement project. Feasibility studies suggested that lean thinking is an effective alternative for the improvement of road transport operations. Logistics and transport managers can use this paper as a guide to improve the road transport operations of their organisations. This paper also contributes by expanding the limited evidence of the application of lean thinking in road transport logistics and highlighting the research areas where the application of lean has been concentrated in this sector.
Keywords: Lean, road transportation, transportation efficiency, value stream mapping, waste elimination.

1. Introduction

The improvement of transport operations has been traditionally approached with the use of mathematical modelling, operations research, and simulation methods (Sternberg et al., 2013). Under these, several classical transportation problems that include the vehicle scheduling problem (e.g. Zhang et al., 2014; Eliiyi et al., 2009), the delivery problem (e.g. Urban, 2006; Mitrovic-Minic et al., 2004), the transportation problem (e.g. Yu et al., 2015; Faulin, 2003; Zhang and Yun, 2009), the vehicle routing problem (e.g. Yu et al., 2013; Lam and Mittenthal, 2013; Kumar et al. 2011; Mishra et al. 2011; Marinakis and Marinaki, 2008; Zhong et al., 2007), among others, have been addressed. Using mathematical modelling, operations research, and simulation methods, the main approach used by researchers to improve transport operations has been primarily based on minimising cost, time or distance, and optimising resource utilisation, routes, and transportation/delivery schedules. Although, these methods and approaches have contributed to tackle the concern that industrialists and researchers have had for the improvement of transport operations since the mid-1990s (Dantzig and Ramser, 1959), criticism has emerged about their effectiveness to actually address real-life transportation problems (Berhan et al., 2014). According to Ak and Erera (2007), one of the most compelling reasons for this is that the large majority of these approaches, and in particular vehicle routing models, are oversimplified by treating parameters such as demand, time, distance, and others, as deterministic, when in real life scenarios they are stochastic in nature. In addition, the improvement of actual road transport operations and activities to gain efficiency is rarely considered under the mathematical modelling, operations research, and simulation methods (Fugate et al., 2009).

Since significant waste and unnecessary costs are normally present in most transportation networks (McKinnon et al., 2003), the application of lean thinking, alongside its principles and tools, has emerged as an opportunity to complement the traditional mathematical modelling, operations research, and simulation methods. This may contribute in overcoming some of their limitations when addressing the improvement of road transportation. In line with the traditional lean’s philosophy of waste elimination, the focus of the so called “lean road transportation movement” lies on identifying and eliminating non-value adding activities, specifically relevant to transport operations, in order to improve the overall productivity and efficiency of a company’s logistics operations. Generally, the focus of research within the lean field has been on production activities related to quality improvement and the quest for increased efficiency. Although a research stream has also studied the application of lean thinking in supply chains (e.g. Mohammaddust et al., 2015; Qrunfleh and Tarafdar, 2013; Liu et al., 2013), specific research on the utilisation of lean in the road transportation sector is scarce and in early stages (Villarreal et al., 2009). In this context, only a handful of articles have proposed methods or reported a case where transport operations have been improved through both the elimination of non-value added activities and the application of lean concepts and tools (e.g. Villarreal et al., 2013; Villarreal, 2012; Villarreal et al., 2012; Hines and Taylor, 2000). The relatively narrow research on lean road transportation is particularly evident when compared with the vast amount of research on lean’s application in other industries such as manufacturing (Taj, 2008), processes (Lyons et al., 2013), and services (Sternberg et al., 2013).

To complement and support the limited body of knowledge on lean road transportation, this paper presents a case study where two lean-based principles and tools have been combined to improve the road transport operations of a leading Mexican brewing organisation. These lean-based principles and tools include the Seven Transportation Extended Wastes (STEWs) (Sternberg et al., 2013) and Transportation Value Stream Mapping (TVSM) (Villarreal, 2012). Besides providing a guiding reference for supply chain and logistics managers when undertaking similar improvement projects as well as expanding the rather limited body of knowledge on lean road transportation, this paper also intends to contribute by stimulating researchers to broaden the study of this under-researched area.

The rest of the paper is organised as follows: Section 2 provides a brief review of the primary research areas that have been derived from the application of lean thinking in road transportation; a description of some of the concepts and the methodology followed in this paper are outlined in Section 3; the case study application of lean thinking in road transport operations is undertaken in Section 4; and Section 5 presents the conclusions.

2. Literature Review on Lean Road Transportation

Within the limited research undertaken in the field of lean road transportation, three research areas (RAs) can be identified. Figure 1 illustrates the three main RAs identified in the academic literature in lean road transportation and the specific papers that correspond to every one of these RAs. These areas are discussed in the following sub-sections.

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2.1 RA 1 - From production to transportation wastes

The origins of lean can be traced back to the 1930s when Henry Ford revolutionised car manufacturing with the introduction of mass production techniques. However, the biggest contribution to the development of lean thinking principles and tools, over the last 50 years, has come from the Japanese automotive manufacturer ‘Toyota’. The central objective of the lean philosophy, as “preached” by Toyota, is the elimination of non-value added activities (Pettersen, 2009), which consequently contributes to the reduction of costs (e.g. Monden, 1998) and increased value for customers (e.g. Dennis, 2002; Bicheno, 2004). Toyota identified seven major types of non-value adding waste in production or business processes (Liker, 2004), namely: production of goods not yet ordered, waiting, rectification of mistakes, excess processing, excess movement, excess transport, and excess stock (Ohno, 1988). In this context, Sternberg et al. (2013), Sutherland and Bennett (2007), and Guan et al. (2003) realised the potential of studying the Toyota’s seven common forms of production and business waste within the context of road transport operations, and adapting them for their improvement. In this case, Sternberg et al. (2013) concluded that only five of the Toyota’s wastes applied to motor carrier operations excluding waste due to excess conveyance and excess inventory. Sternberg et al. (2013) included resource utilisation and uncovered assignments as part of the transportation wastes derived from Toyota’s original production and business wastes. They called them the “Seven Transportation Extended Wastes” (STEWs).

Similarly, Sutherland and Bennett (2007) defined overproduction, delay/wait, excess transport/conveyance, motion, inventory, space, and errors as the “Seven Deadly Wastes of Logistics”. According to Sutherland and Bennett (2007), these wastes keep the management of supply chains away from achieving their full business potential. Finally, Guan et al. (2003) identified five transport wastes, these being: driver breaks, excess load time, fill losses, speed losses, and quality delays. Sternberg’s et al. (2013) STEWs were developed and validated through multiple case studies and carrying out in-depth interviews with experts from carrier operations, the lean field, carrier technology providers and carrier service buyers. Hence, these wastes identified by Sternberg et al. (2013) were used as the basis to improve the road transport operations of the Mexican organisation studied. Figure 2 presents a description of the STEWs.

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2.2 RA 2 - Lean performance measures for road transportation

Measurement on a continuous basis is crucial to improve operations and supply chains (Dey and Cheffi, 2013; Cabral et al., 2012). Thus, the application of lean practices in transportation needs to be supported by adequate metrics to measure the performance of lean systems as a basis for continuous improvement. In this line, Taylor and Martinchenko (2006) proposed four lean transportation laws (i.e. transportation waste, daily event management, transportation strategy, and transportation performance), which explain how and where transportation processes may be sub-optimal, and how the application of lean can positively impact these processes and overall organisational performance. Similarly, Mason et al. (2001) and Simons et al. (2004) adapted and extended the Overall Equipment Effectiveness (OEE) (Nakajima, 1988) metric, used by the lean’s Total Productive Maintenance (TPM) (Nakajima, 1988) approach to measure equipment effectiveness, and developed a new metric called Overall Vehicle Effectiveness (OVE). This metric was used by Simons et al. (2004) for measuring and improving the performance of truck transportations.

Later, to reflect the efficiency of a route, Guan et al. (2003) modified OVE by dividing the performance factor into two components: route and time efficiencies. Finally, Villarreal (2012) proposed a modified version of the OVE measure called Transportation Overall Vehicle Effectiveness (TOVE). Unlike OVE, TOVE considers total calendar time instead of loading time. This is due to the fact that waste identification and elimination is related to the transportation vehicles utilised to move products. Since vehicles represent a high investment, it is important to keep them in operation at all times. A comparative illustration of the structures of the OVE and TOVE measures is presented in Figure 3. In summary, although both measures broadly classify transportation wastes into three mutually exclusive elements (i.e. availability, performance and quality losses), TOVE adds the administrative availability element. Hence, it divides the availability component into administrative and operating availability. The concept of vehicle administrative availability is important as it has a significant impact on the overall vehicle utilisation and efficiency. It is mainly the result of administrative policies and strategies related to capacity or maintenance decisions. TOVE is obtained from the product of administrative availability, operating availability, performance and quality; whereas OVE is obtained by multiplying the availability, performance and quality components, see Figure 3.

Insert Figure 3 here

2.3 RA 3 – Improvement of road transport operations

It is well known that transportation is an activity classified by the lean movement as waste that should be, if possible, eliminated (Womack and Jones, 2003; Ohno, 1988). However, in the current globalised market, transportation is a necessary activity to deliver goods to customers. In fact, although transportation is also recognised as a tertiary economic activity (Chase and Apte, 2007), Villarreal et al. (2009) suggest that efficient transport operations can nowadays be considered a differentiating factor that adds service value to customers. Thus, when mapping a supply chain, unnecessary transportation becomes an important waste to identify, measure, and eliminate. According to Fugate et al. (2009) and McKinnon et al. (1999), unnecessary transportation problems and waste can be addressed by increasing the efficiency of transport operations. In this context, Hines and Taylor (2000) proposed a methodology, consisting of four stages, to increase efficiency and eliminate waste in transport operations. Villarreal et al. (2009) reported the application of this methodology in the logistics operations of a Mexican firm, leader in the production and distribution of frozen and refrigerated products. As a result, the organisation saved over half a million British pounds of capital investment and one million British pounds per year of operations cost by improving the capacity utilisation and availability of its distribution vehicles (Villarreal et al., 2009).

Villarreal et al. (2012) also developed a methodology to reduce transport waste by integrating the Just-in-Time approach of milk runs with the traditional operations research approach of developing algorithms to optimise vehicle routing. Additionally, Villarreal (2012) adapted the lean’s Value Stream Mapping (VSM) (Rother and Shook, 2003) tool to support efficiency improvement programmes in transport operations. He called this adapted tool Transportation Value Stream Mapping (TVSM). Finally, Villarreal et al. (2013) developed a scheme for increasing transportation efficiency. The scheme was proposed around a modified version of the OEE index used in TPM. This index was adapted as the primary performance measure for transport operations and integrated with the VSM tool to identify and eliminate availability, performance and quality related transportation wastes.
2.4 Other approaches for waste reduction and efficiency increase in road transport operations

Lean relies on practices such as Just-in-Time (JIT), Total Quality Management (TQM), Total Productive Maintenance (TPM), pull, flow, and others, to reduce waste and increase efficiency (Yang et al., 2011). Forrester et al. (2010) considers lean as the most influential new paradigm in manufacturing however, the reduction of waste and improvement of efficiency in the road transport sector has been addressed through different perspectives and using other approaches which are not necessarily based on lean practices or the use of lean tools. For example, Sanchez Rodrigues et al. (2014) suggest that avoiding extra travel in road freight operations is vital as these operations are characterised by low-profit margins. For this reason, they proposed a measure, called “Extra Distance”, which intends to reduce the additional operational costs associated with transport disruptions. Similarly, Sanchez Rodrigues et al. (2008) proposed a model to improve the efficiency of freight transport through a better management of supply chains’ uncertainty. Furthermore, Bottani et al. (2015) proposed an integrated approach to increase sustainability and efficiency of logistics activities. Similar additional works have been conducted, for example, by Cruijssen et al. (2010), McKinnon and Ge (2004), Abbas and Aly (2004) and Davies et al., (2007).

In general, other non-lean based approaches reported in the academic literature devised to reduce waste/cost and improve efficiency of road transport operations include the outsourcing of logistics operations (Atkas et al. 2011) and integration of supply, production and distribution/transport activities (Van der Vorst et al., 2009; Mason and Lalwani, 2006). Other approaches, as previously discussed, have included the development of advanced tools or models based on mathematical modelling, operations research or simulation methods to improve efficiency through the optimisation of cost, time, distance, resource utilisation, routes, or transportation/delivery schedules. Examples of recent works in these areas include Yu et al. (2015), Zhang et al. (2014), Jemai et al. (2013), Eliiyi et al. (2009), among others.
We consider important to highlight the abovementioned studies and research streams, as future research may more closely look at the synergies of the approaches proposed in these studies and lean thinking. This may provide an opportunity to formulate more inclusive and enhanced methods and strategies for the improvement of road transport operations.

3. Concepts and Methodology

The improvement of the road transport operations of the Mexican organisation studied was addressed by following a sequential three stages approach that consisted of the following steps: (1) Analysing the value stream of the road transport operations, (2) Identifying the STEWs inherent in the road transport operations, and (3) Defining and implementing a strategy for the elimination of the STEWs.

To analyse the value stream of the road transport operations of the Mexican organisation studied, a TVSM (Villarreal, 2012) study was carried out. The use of VSM to document, visualise, quantify and comprehend the material and information flows of value streams in manufacturing operations (e.g. Singh and Sharma, 2009; Seth and Gupta, 2005), healthcare operations (e.g. Lumus et al., 2006; Teichgräber and de Bucourt, 2012), environmental-based operations (Kurdve et al., 2011), and service operations (e.g. Barber and Tietje, 2008) has been widely documented in the academic literature. However, only a small number of researchers have considered the use of VSM to support the analysis and improvement of the value stream of logistics and transport operations (Villarreal et al., 2013; Villarreal, 2012; Villarreal et al., 2012; Hines et al., 1999; Jones et al., 1997). For this reason, besides documenting the application of lean thinking in road transport operations to expand the limited body of knowledge in this area, this paper also contributes by presenting a further case study of the application of VSM in the road transport industry. In this case, the TVSM concentrated on uncovering waste related to transport efficiency (Villarreal et al., 2012) through the entire distribution cycle, from loading product orders to the transportation vehicles to unloading product returns from the market and closing administratively the route or shipment.

The TVSM analysis consisted of two main facets; one that included activities pre and post transport and serving clients, known as “Not-In-Transit (NIT)” activities, and another that contemplated activities related to the actual physical distribution of the product, known as “In-Transit (IT)” activities. Villarreal (2012) suggests that vehicle drivers should focus on performing IT activities only, whereas the execution of NIT activities should be carried out by warehouse operators. This advice was taken into consideration when proposing the improvement initiatives, and after having analysed the value stream of the transport operations studied as well as identified and quantified the STEWs inherent in it. The data collected for NIT activities to conduct the TVSM study included some of the basic metrics of performance reflected in the traditional VSM as established by Rother and Shook (2003). These were: cycle time, value added time, uptime and setup time. Additionally, NIT activities were aligned to the takt time required to load customer orders to trucks and deliver them on time. On the other hand, data related to average time between clients, truck capacity utilisation level, average distance travelled per client, distance travelled in excess per route, and the percentage of waiting time in transit was collected for IT activities and the TVSM study. Finally, for serving clients; the average number of clients per route, cycle time, value added time, the percentage of product returns, and the percentage of clients not served was the key data gathered for the TVSM analysis.

The TVSM analysis contributed to the identification and quantification of the STEWs (Sternberg et al., 2013) inherited in the value stream of the transport operations studied. Once the STEWs were identified and quantified, improvement initiatives based on the semi-dynamic design of routes, automation of product loading and inspection, redesign of customer serving procedures, among others, were formulated and implemented(see Section 4), to reduce the STEWs and hence improve the road transport operations of the organisation studied.

4. Case Study

This section presents the case study documented in this paper to complement and support the limited body of knowledge on lean road transportation. Voss et al. (2002) emphasises the importance of conducting and publishing case study-based research as they suggest that it is particularly suitable for the testing and development of new theory, especially in the field of operations management (McCutcheon and Meredith, 1993). This is evident from the high volume of recent researches published in this field using a single case study research method approach (e.g. Bouzon, 2015; Bevilacqua et al., 2015; Tuli and Shankar, 2015; Diehl and Spinler, 2013; Taylor, 2009; etc.). Serrano Lasa et al. (2008) therefore comments that many of the breakthrough concepts and theories in operations management, from manufacturing strategy to lean production, have been developed through field case research. Similarly, Zander et al. (2015), Cameron and Price (2009) and Eisenhardt (1989) consider a single detailed case study a valid research methodology to study and understand specific phenomenon within specific contexts, such as in this case the application of lean thinking in road transportation. In this study, and as suggested by Voss et al. (2002), the case study research approach proved to be a valuable source to document and report the experiences of the authors while conducting the improvement of road transport operations using lean thinking-based principles and tools. Thus, the case study was an ideal research strategy that contributed in enriching the limited body of knowledge on the application of lean thinking in the road transport industry.

The Mexican organisation used as the basis for this study processes and distributes bottled beverage. This organisation is a large and leading national company with major operations in the north of Mexico. This study focused on the distribution of products from regional distribution centres (RDCs) to retailing points that included convenience store chains, independent retailers, and supermarket chains. The company has a number of RDCs across Mexico, but considering the complexity and difficulties of conducting a large scale improvement initiative and the constraints in terms of budget, time, and personnel that organisations normally face when carrying out improvement projects (Marriot et al., 2013), this study focused only on the RDC located in the city of Monterrey, Mexico. Concentrating efforts in only one RDC helped, as suggested by Antony and Banuelas (2002) and Pyzdek (2003), in validating the application and results of lean thinking as an effective and suitable approach to drive the improvement of road transport operations, before proceeding into a future full scale deployment in the rest of the company’s RDCs.

In particular, the RDC studied counts with a fleet of about 90 distribution trucks (see Figure 4 for an illustration) to serve about 6,000 customers that include selling points (e.g. convenience stores) and consumption points such as pubs, restaurants, etc.

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Most selling points associated to established chains state morning time windows of three hours. However, some important chains were open 24/7. This offered the opportunity to supply them through evening and night shifts. The rest could be serviced any time of the day except from 2 to 4 P.M. Consumption points such as restaurants were supplied during the morning and pubs and entertaining businesses during late afternoon. The distribution of beverage was made daily through 76 fixed day routes. Labour cost per night or day shift was the same. However, night shifts presented contradictory situations; in one hand, driving conditions made night shifts more attractive due to significantly lower traffic congestion; but on the other hand, operating risk was higher due to the current climate of insecurity. It was common operational practice to have a two-person driving crew per route.

The foremost strategic concern of the company referred to cost reduction. In order to address this situation, the firm established a company-wide strategy for reducing cost. Since routing cost had become an important component of the total cost structure in recent years, it was necessary to consider it for improvement. Labour cost represented an important cost concept. The routing operations of interest presented a consistent level of overtime cost. Labour overtime was paid double for every hour after a day shift of eight hours until a maximum of nine hours per week per worker. Routing cost per hectolitre was about $86 Mexican pesos (i.e. about £3.40 British Pounds). This cost included labour (25%), truck maintenance (30%) and gas (45%).
4.1 Analysis of the value stream

As indicated previously, the first step in addressing the improvement of the road transport operations of the organisation studied consisted of analysing the value stream of such operations. This was done by developing the TVSM for the current routing operations as shown in Figure 5. The data used to construct the TVSM was collected from both company’s records and field data. For the first, data was gathered from an administrative database directly fed by the vehicle drivers’ handhelds and the truck’s GPS. For the second, a team of researchers collected detailed data by accompanying the truck driving crews. This was done by sampling 30% of the routes. The transport operations mapped consisted of the following activities:

  • Preparation of orders. This was considered a NIT activity that was comprised of “sub” or “micro” activities such as inspecting the orders’ load, the truck, and reviewing the route;

  • Distribution of products (i.e. transporting products, serving customers and collecting spoiled product). This was considered an IT activity;

  • Returning back to the RDC was also considered as an IT activity;

  • Closing routes. This NIT activity included “sub” or “micro” activities such as settling payments from customers with the cashiers and unloading/returning spoiled product and the truck.

In general terms, the TVSM study indicated that the overall average daily journey time for the distribution of goods from the Monterrey RDC to its corresponding retailing stores was 11.5 hrs, see Figure 5. Since an 8 hrs shift was the working standard for the studied organisation, the 3.5 hrs excess contributed to significantly increasing the operational cost, which was associated to overtime. All the activities included in the distribution operations, from preparing the routes, serving the stores until closing every route were executed as part of the journey. Internal NIT activities took 2.5 hrs on average (i.e. 22% of the journey’s time), whereas IT activities took an average of 9 hrs (i.e. 78% of the journey’s time), see Figure 5. The average number of stores served by a route was 15.

From Figure 5 it is clear that the TVSM shows the various important detail activities of the Monterrey RDC. According to Seth and Gupta (2005), a holistic visualisation of this type offers an actual trigger and a challenge for improvement. Therefore, the next steps in the improvement project consisted of identifying the STEWs (Section 4.2) and defining and implementing a strategy for their elimination (Section 4.3).

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4.2 Identification of the STEWs inherent in the road transport operations

The second step in the road transportation improvement project involved the identification of the relevant STEWs (Sternberg et al., 2013). Table 1 presents a summary of the most relevant STEWs identified through the TVSM, the operation activities where they were located, and their effect on the transport operation.

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