Figure 5 illustrates the micro context of the TVSM previously presented in Figure 4. The most relevant information is also summarised in Table 2. The average number of stores served by a route was 45. Therefore, the organisation studied utilised its fleet only about 49% of the available daytime. Additionally, 17% of this time, both truck and its driving crew were in the DC performing NIT activities.
Before trucks left the DC, the driving crew had to prepare the route. This included activities such as loading and truck inspection, a quick meeting, and route sequence definition. As shown in the Figure 5, the crew was idle 50% of the average time taken for route preparation. After distributing the product to customers, the driving crew had to participate in closing their routes. This NIT activity consisted of settling the payments collected from customers with the cashiers, returning both spoiled product and the truck. In particular, the original procedure carried out by the cashiers was manual, sequential, and with different cycle times, which resulted in an important total queueing time per route of 17 minutes. Total waste identified in the activity of closing routes was estimated to be 21 minutes (35%).
IT activities consisted of transporting product and serving customers. Average transport time between clients was estimated to be 2.5 minutes. It was identified that on average, only 27% of truck capacity was utilised per route. In addition, each route travelled 32 kilometres in excess. Serving customers took an average of 9.4 minutes per stop, of which 31% was identified as non-value added.
As indicated in Table 2 and Kaizen burst 1 in Figure 5, incorrect processing and resource utilisation wastes were found, in this case, mainly during the transportation of products to customers. These occurred because of inefficiencies in the design of routes (i.e. customer assignment to trucks and visit sequencing). Route design was a shared responsibility between the route dispatcher and the truck drivers. All the routes were fixed and established four years ago. Assigning additional customers and customer sequencing was determined based on the experience of each driver. Customer time windows were not considered, resulting in several visits to customers per route. As a consequence, 73% of transport capacity was under-utilised and 32 kilometres of distance per route were travelled in excess. In addition, these wastes caused longer journey durations and hence an important number of programmed customers were not visited because of the lack of time. On average, a route did not visit 13% of the programmed customers.
Unnecessary movements and waiting time were found in the processes of serving customers and NIT activities as indicated in Table 2 and Kaizen burst 2 in Figure 5. These occurred due to inefficient procedures that contained non-value added activities. Customer service time included the time taken to perform activities that did not add value or were not simplified, for example, inspecting products, verifying with the store leader whether the order was complete, and getting and loading product returns. Serving clients was an activity with 31% of its time categorised as waste. There was also the need to consider the time taken to obtain the payment of the order from the customer. In principle, NIT activities must not be the responsibility of the driving crew. However, if these have to be done, the objective would be to perform them efficiently. In this case, NIT activities took about 2 hrs. This accounted for 17% of total journey’s time. Even though there were no bottlenecks present in the warehousing activities, 50% of the time for preparing routes was found to be waste. Also, 35% of the time taken to unload and close routes was found to be non-value added.
4.2.3 Uncovered assignments and defect waste
Defect waste in this case included the percentage of spoiled product that was returned to the company. This waste occurred during serving customers. For this case, it was estimated that 12% of the product demand was returned because it became spoiled. The main cause of this waste was the low product distribution frequency, for example, each customer was visited twice per week.
Uncovered assignment waste consisted of the percentage of customers not visited per route. The cause of this waste was the amount of time misused on inefficient procedures, waiting and unnecessary movements. Thus, any initiative directed to reduce this wasted time would positively impact on decreasing the number of customers missed per route.
4.2.4 Analysis of value added time
Additional relevant information about the routing operations concerns the level of value added time (VAT) per route. As shown in Figure 5, about 75% of the total journey time was VAT. This was equivalent to 3 hrs. After considering driver breaks, the remaining 8.3 hrs associated with VAT was used for transporting and serving customers. However, it yet remains to be seen if this time is used properly. That is, trucks should be loaded at full capacity without travelling distance in excess. It also assumes that all customers are served satisfying 100% of their demand.
However, as shown in Figure 5, there was a truck capacity utilisation of 73% and a distance travelled in excess of 32 kms per route. This was equivalent to 6.3 hrs of non-value added time (NVAT). Finally, there was also 13% of customers not visited and 12% of the product was returned. This would result in an additional equivalent time waste of 0.5 hrs. Thus, in total, an additional equivalent time of 6.8 hrs of NVAT was identified. Therefore, for this case, only 1.5 equivalent hrs the truck would be moving fully loaded travelling zero distance in excess, and satisfying 100% of customer demand. Table 3 illustrates the impact of each STEW on NVAT. The incorrect processing and resource utilisation wastes were considered the most relevant.
Table 3. Summary of impact of STEWs on NVAT
Non Value Added Time (hrs)
Incorrect processing and resource utilisation
Unnecessary movements and waiting
Uncovered assignments and defect waste
4.2.5 Impact of STEWs on efficiency factors
As previously described in Section 2.2, the determination of the TOVE index metric requires the identification of several wastes associated with its different components: administrative and operating availability efficiencies, performance efficiency and quality efficiency, see Figure 1. It would be of interest to determine the inter-relationships between both waste classification streams: STEW´s and efficiency wastes. Considering Figure 1 and Table 1 as a basis, the following points can be concluded:
STEW’s waiting is similar to the efficiency waste of waiting;
STEW’s resource (i.e. truck, operator, etc.) utilisation includes the efficiency waste (truck) fill loss;
STEW’s overproduction, waiting and unnecessary movements can cause efficiency wastes related to activities performed with time in excess (e.g. loading, unloading, inspection and customer serving);
STEW’s defect includes efficiency wastes product defective and corrective maintenance;
STEW’s incorrect processing, uncovered assignments and resource utilisation can cause efficiency wastes (truck) fill loss and/or distance travelled in excess;
STEW’s resource utilisation can cause efficiency wastes time not planned for trucks and/or internal NIT activities;
STEW uncovered assignments can cause efficiency waste demand not satisfied.
In general, there is a strong relationship between both waste schemes. It seems that the identification of certain STEWs increases the probability of occurrence of certain efficiency wastes. This aspect can be used to delineate an overall waste identification scheme. Two basic types of inter-relationships are identified in this case, namely: the STEW causes an efficiency waste (cause & effect), and an efficiency waste is included, or is a component, of a STEW.
The previous findings can be used to design more effective transportation waste elimination schemes. A new hybrid scheme could use performance measures (TOVE, availability efficiency, etc.) as references for goal setting improvement purposes. The identification of wastes would be enriched by the consideration of the two waste streams: STEWs and efficiency wastes. Further discussion on this potential scheme is left for future works.
Kaizen Burst 1
Kaizen Burst 3
Kaizen Burst 2
Journey time = 11.8 hrs
Figure 5. TVSM micro analysis for the routing operations from the Escobedo Distribution Centre
4.3 Stage 3. Definition of waste elimination strategy
As previously discussed, different strategies have been proposed by, for example, Villarreal et al. (2009), La Londe and Masters (1994), Burns et al. (1985), Cooper (1983), among others, to improve transport operations. In this case, the strategy established to decrease the main STEWs (Sternberg et al., 2013) identified was originally aimed at eliminating two sets of wastes. The first set consisted of the elimination of incorrect processing and resource utilisation. The second set included unnecessary movements and waiting time. Both sets of wastes had an important impact on the level of uncovered assignments waste (see Table 2). The waste elimination strategy formulated to tackle the STEWs is briefly described in Table 4. Particularly, this project was focused on the deployment of improvement strategies based on the design of semi-dynamic routes and the improvement of procedures. Hence, other improvement strategies such as increasing the frequency of customers’ visits, redesigning the basket size of transport vehicles, and using smaller trucks could be considered as part of a second wave of future improvement strategies.
Table 4. Description of improvement strategies
Incorrect processing and resource utilisation
Sub-optimal routes defined by drivers
Sub-optimal client sequencing
Customers are visited several times per route
Semi-dynamic route design
Unnecessary movements and waiting
Procedures for serving customers, preparing and closing routes have non-value activities.
4.3.1 Semi-dynamic routing design
This initiative started with the definition of a new route redesign review period. At the time of the development of the project, there was no determined review period. Four years had passed and the market dynamics had changed significantly in terms of the quantity, location and demand of the clients. After analysing the market demand growth and considering that each customer was visited twice per week, it was decided that the company would carry out a weekly route redesign when additional new clients appeared. The weekly customer growth rate per route was a maximum of two new customers. The solution used before the redesign consisted in including the new customers to the closest route and sequenced between the two closest customers. The company had the option of using specialised software programmes such as Roadnet Transportation Suite Routing and Scheduling Systems (UPS Logistic Group, 2004), which they already owned, and Map-Info (MapInfo Corporation, 2015). In particular, MapInfo software could be used to perform a map and geocode analysis while Roadnet Transportation Suite would enable the company to create optimised routes and load plans (Alagöz and Kocasoy, 2008).
4.3.2 Simplifying procedures
The simplification of procedures in three stages of the routing operations was undertaken, namely: (1) during route preparation before trucks left for distribution, (2) during serving clients, and (3) at closing routes. Route preparation before leaving to distribute products was a lengthy activity. Driving crews were idle at least 50% of the time. So, they could have about 30 additional minutes for routing and distributing products.
Serving clients consisted of unloading and inspecting each customer order. Then, they would put the product in the customer’s receiving area and obtain their payment. Finally, product returns were identified, counted, and packed to be transported back to the company’s DC. The last stage requiring procedure simplification was closing routes at the DC. This stage included the activities of settling customer payments and product returns. Hence, long queues occurred because of the inefficient work of two cashiers. Each cashier performed different activities in series, and were idle 36% of the time. A new procedure in which both cashiers performed all the tasks in parallel to each other was designed. This reduced idle time to 15% and decreased total time required for this activity by about 22%.
It is estimated that the benefits that can be derived from implementing the semi-dynamic route design and simplification of procedures improvement strategies are significant. Table 5 illustrates a summary of these benefits. For instance, if the semi-dynamic route optimisation strategy is implemented, the impact would be limited to the elimination of incorrect processing and resource utilisation wastes. It is estimated that customer service level would be fully satisfied with this implementation. Also, total distance travelled by all the routes would decrease by 16% and the number of routes would be reduced by 10%.
Table 5. Summary of the positive effect of optimising routes and NIT and serving activities
Optimising NIT & Serving Activities
Number of routes
Clients per route
Total distance (km)
Number of clients not served per route
Service time per client (min)
NIT activities time per route (min)
Implementing the improvements and standardising projects of NIT and customer serving activities would also yield important benefits. For instance, total distance would decrease another 19%, and the number of routes would be reduced by 18%, see Table 5. This further improvement effort would have a significant positive impact on distribution costs. In this context, it is estimated that a minimum cost reduction of 27% will be achieved when all the initiatives are implemented. Hence the importance of not only proposing the improvement strategies but also deploying them as indicated by the proposed systematic lean method.
4.4 Stage 4. Implementation of STEWs elimination strategy
The implementation of improvement strategies is more effective when they are first supported by a pilot test to validate their effectiveness (Nousala et al., 2008). Thus, the implementation of the strategy to eliminate the STEWs included an initial pilot test. The two initiatives that required careful attention were the semi-dynamic route redesign and simplification of procedures.
4.4.1 Redesign of routes
A sample of 30% of the routes was redefined. This task was carried out with the support of the specialised software programs Roadnet Transportation Suite Routing and Scheduling Systems (UPS Logistic Group, 2004) and Map-Info (MapInfo Corporation, 2015). Here, both the assignment of clients and the visiting sequence were optimised. As an initial step, it was decided to do a pilot test with ten routes during two weeks. This had the purpose of building confidence, and making the necessary adjustments for a successful implementation. The results from the pilot run showed a reduction on the average number of clients not served per route from six to zero. However, average journey time did not changed significantly.
The implementation of the previously described strategy is currently under way. This has been divided into two fronts: the first front is concentrated on improving warehousing (NIT) and the procedure for serving clients. The main initiative for NIT activities consists of improving the tasks performed by the cashiers. In particular, the original procedure to settle cash payments from the customers was modified and automated. Now, both cashiers perform the full job from start to end. These projects have already been fully implemented.
The second front is concerned with route design. The initial step in this front consisted of the pilot test explained earlier. The second step, which has already started, is the redesigning of all 90 routes. After applying the optimisation software, the number of routes has been reduced to 66 (see Table 5), without compromising the customer service level. The average number of clients to be served by each route has increased by about 40%, and the distance travelled reduced by 32%. It is estimated that this effort will be completely implemented and stabilized during the first quarter of 2016. Finally, this initiative will be applied to the rest of the routing operations during the second quarter of 2016.
5. Discussion The systematic lean thinking-based method proposed in this paper contributes to expand the very limited application of lean principles and tools in the logistics and transport sector as highlighted by Villarreal et al. (2009). First, unlike other approaches such as mathematical modelling, operations research-based methods and simulation, which have been traditionally used to improve road transport operations through the optimisation of resource utilisation (e.g. Chiu et al., 2006; Zhong et al., 2007; Eliiyi et al., 2009), routes (e.g. Lau et al., 2009; Jemai et al., 2013), cost (e.g. Boudia et al., 2008; Eliiyi et al., 2009; Yu et al., 2015), time (e.g. Chiu et al., 2006; Zhong et al., 2007; Zhang et al., 2014; Yu et al., 2015) and distance (e.g. Zhang et al., 2014), the proposed approach is based on the improvement of transport operations by the elimination of waste (i.e. non-value added activities), and hence improving the efficiency of the actual road transportation operations. This presents an opportunity for logistics and transport companies to reduce operational costs (Monden, 1998; Ohno, 1988) and increase value for their customers (Bicheno, 2004; Dennis, 2002) similar to companies in other sectors such as manufacturing (Taj, 2008), processes (Lyons et al., 2013) and services (Sternberg et al., 2013). The method proposed in this paper thus provides companies in the logistics and transport industry with the opportunity to also benefit from the lean philosophy. The outcome of its application in the studied company echoes the positive results that organisations from other sectors have already experienced with the application of lean thinking. The results also supports earlier findings by researchers such as Villarreal et al. (2009), Sternberg et al. (2013), and Villarreal et al. (2013), and thus emphasise that lean thinking can be an effective approach that both researchers and industrialists can further explore to improve road transport operations.
Second, the results of the case study suggest that VSM, a lean tool that has successfully been applied to study the value streams of manufacturing (e.g. Seth and Gupta, 2005; Singh and Sharma, 2009), service (e.g. Barber and Tietje, 2008), healthcare (e.g. Teichgräber and de Bucourt, 2012; Lumus et al., 2006) and environmental (Kurdve et al., 2011) processes, can also be effective in identifying wastes in logistic and transport operations. Due to the limited evidence in the academic literature in this respect (Villarreal et al., 2013; Villarreal, 2012; Villarreal et al., 2012; Hines et al., 1999; Jones et al., 1997), the present paper adds to the existing scant literature by providing further evidence of the application of VSM in the logistics and transport sector.
Third, although improvements in road transport operations can be conducted in an ad hoc basis, a systematic improvement approach underpinned by lean principles and tools provides a more effective and efficient approach. This is evidenced by the effectiveness of other systematic approaches to problem solving and improvement such as PDCA (Adebanjo et al., 2015; Deming, 1993) and DMAIC (Ghosh and Maiti, 2014; Garza-Reyes et al., 2014). The importance of following a structured and integrated approach to operations improvement has been widely discussed in the academic literature (e.g. Garza-Reyes et al., 2014; Mauri et al., 2010; Vanneste and Van Wassenhove, 1995). In this research, the proposed systematic method helped the studied organisation to establish a standardised routine to improve its transport operations. Therefore, its application provides organisations with a platform to achieve this.