A framework for improving reliability of truck

turn times in FMCG transport networks

Abstract

Truck turn times in FMCG supply chain are unreliable. The Dutch

‘Speed Docking’ project in 2011 and 2012 gave clear evidence to

this. This leads to additional trucks and drivers in transport

networks. Improving reliability of truck turn time will lead to

less trucks and drivers in the transport network and improved

productivity at distribution centres.

The research is focused on Fast Moving Consumer Goods (FMCG)

transport networks. A reduction of transport is high on the

agenda’s of FMCG companies. The objectives in the FMCG industry

are: less CO2, increased reliability, secure transport capacity

and ultimately a better service to consumers.

The FMCG industry, together with logistics service providers,

can realise improvements with the use of mobile communication,

on board computers, connected navigation and intelligent

transport systems, process mining based on big data to benefit

better tactical and operational planning, geo-fencing, agent

based dynamic planning and time slot allocation and the use of

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community platforms were data is shared with all chain partners

in FMCG.

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Purpose – The purpose of this paper is to understand the

reliability of truck turn times in Fast Moving Consumer Goods

transport networks and to investigate measures to improve

reliability. Improving the reliability of truck turn times can

improve capacity planning and scheduling and time slot

allocation.

Design/methodology/approach – Research literature in the field

of truck turn times, transport planning and pipeline

management. Analyse truck turn time data of more than 3.000

shipments (of Fast Moving Consumer Goods manufacturers Mars and

Heinz). Workshops with industry leaders to investigate

potential measures for improving reliability.

Findings – Truck turn times in FMCG transport networks are

unreliable. This leads to additional trucks and drivers in

transport networks (slack). Improving reliability of truck turn

time will lead to less trucks and drivers in the transport

network and improved productivity at distribution centres.

Further research is necessary on the actual truck turn times as

being used by transport planners, new planning, scheduling and

priority rules, the impact on internal warehousing operations

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(goods receipt and put away) and on developing a framework

(cause-effect) for improving understand the reliability of

truck turn times in FMCG transport networks.

Research limitations/implications – First, this study was only

focused on the transport flows of two FMCG companies (Mars and

Heinz) to distribution centres of retail and wholesale. Further

research needs to been done on all incoming shipments to a

distribution centre, in order to develop new planning,

scheduling and priority rules. Second, this study did not look

at the impact of reliability of truck turn times on warehousing

operations. Further research is currently going on to integrate

internal warehousing operations in the framework.

Originality/value – A lot of research has been done on how to

work with (unpredictable) truck turn times in transport

planning. This research focuses on understanding the reasons

for unreliable truck turn times and the measure to improve

reliability.

Keywords – truck turns, transport planning, FMCG supply chain

management, freight, big data, time slot planning

Paper type – academic paper

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1. Introduction

With a more efficient use of transport capacity, avoiding

unnecessary supply chain activities, vertical and horizontal

collaboration in the FMCG industry and integration of

processes, the logistics sector will need to accomplish a

strong increase in productivity in transport and distribution.

This paper presents research on the impact of reliability of

truck turn times in Fast Moving Consumer Goods transport

networks. This paper will present: the scientific and practical

relevance of this subject, the findings of our research based

on a data analysis performed in 2012, a framework for improving

reliability, conclusions, discussion and relevant questions for

further research.

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1. Introduction & problem statement

2. Transport planning and truck turn times

3. Data analysis:

Speed Docking Project

4. Framework for improving reliability of truck turn

times

5. Conclusion & 6.

Discussion

Figure 1: Approach

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2. Backgrounds

Across Europe the working population is getting older, also in

Eastern European countries. There will be a shortage of drivers

in the transport industry. Over the next 10 years, about 1 in 5

employees in the European transport industry will retire. Young

employees do not want to work in the transport industry.

Youngsters want acknowledgement for their work and that is what

they don’t perceive to get in the transport sector. Shortage of

transportation capacity could result in empty shelves and

stalled production if the productivity is not improved

drastically (Connect, 2011).

Fast Moving Consumer Goods

This research is focused on Fast Moving Consumer Goods (FMCG)

transport networks. A reduction of transport is on top of the

agenda’s of FMCG companies (CapGemini, 2010). Unilever launched

a EU Marco Polo project to reduce the mileage of its trucks in

Europe by 200 million kilometres per year, compared to 2010

(Unilever, 2010). The French Franprix chose to supply stores in

Paris by water. SCA and Hero bundle the supply of goods with

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logistics service Nabuurs. The objectives in the FMCG industry

are: less CO2, increased reliability, secure transport capacity

and ultimately a better service to consumers.

3. Truck turns time

Truck turn times have a big impact on the tactical and

operational planning of transport networks. Nearly half the

time trucks are not moving (Eurostat, 2011). It’s not that

trucks are actually waiting for the loading or unloading to

start, but the planners have scheduled this cycle time. In

logistics planning, it is not the average time that matters,

the longest cycle times do. If an average time is used,

schedules would have to be revised over and over again. The

larger the variance the more slack needs to be considered when

planning, with more trucks on the roads as a consequence

(Crainic, 1997).

Truck turn times

Amongst others Barrett (2001) studied truck turn times in

timber harvesting and wood delivery operations and Huynh e.a.

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(2005, 2007) and Boysen e.a. (2010a and 2010b) studied truck

turn times at sea container terminals. These studies focused on

a critical component of truck appointment systems: scheduling

rules. The goal of these studies was to gain an understanding

of how the various scheduling rules affect resource utilization

(both transport equipment and handling equipment) and truck

turn time in grounded operations and the use of truck

appointment systems. The objective of these studies was to

develop e.g. algorithms, simulations model and generalized

computer simulation model that can be used as a practical tool

to illustrate the impacts that truck turn times have on the

productivity and to improve planning and scheduling of

operations.

So far, no research has been done on how to actually improve

the reliability of truck turn times in transport networks. The

objective of this paper is to inventory potential measures to

improve the reliability of truck turn times. This is what the

Dutch project ‘Speed Docking’ in 2011 and 2012 was addressing.

Speed Docking project (2011-2012)

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Mars Netherlands organized the Dutch Speed Docking Championship

in 2012 for the second time, in 2012 together with HJ Heinz.

With this logistic competition, both companies want to

accomplish a quicker and more efficient inbound logistics

process at the distribution centres of retailers. The

competition revolves around the duration and time spent by

trucks at the distribution centres. For ten weeks the

distribution centres of retailers and wholesalers competed for

the greatest accomplishment: Who will save the most time when

unloading the trucks? This paper presents the findings of the

2012 Speed Docking project.

4. Data analysis

Mars and Heinz collected the entry and exit times via these

onboard computers, and share this information onwards with

retailers and wholesalers. Via ‘process mining’ (Maruster,

2002) using the actual times and trip data researchers

determined at what distribution centre a delay occurred. This

is an example of the use of Big Data in supply chains (Bughin

e.a., 2010, Manyika e.a., 2011). The transportation companies

involved (Kuehne Nagel and Nabuurs) found out the real truck

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turn times once they were able to register this in a reliable

way via the onboard computers.

Caroz More Insight, the company responsible for the collection

of the truck turn time data via the onboard computers of the

trucks and the analysis of the Speed Docking data, analyzed

3.247 shipments in 2012, spread over a period of 9 weeks. The

shipments went to 46 retail locations (1.556 shipments) and 116

wholesale locations (1.691 shipments).

Truck turn times

A large amount of the time spent by truck drivers is spent in

waiting for loading and unloading of their cargo. For

wholesales truck turn times are between 10 and 30 minutes, for

retail distribution centres truck turn times are between 30 and

90 minutes (see figure 2). However 15 to 20 percent of the

truck turn times are longer than the modal class. For resilient

tactical and operational planning, scheduling and time slot

planning, planning with average truck turn times is not good

enough. Planners need to plan slack time, and thus additional

capacity, in the transport network (Crainic, 1997).

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During the study researchers found that truck turn times for

trucks with 5, 10 or 26 pallets for one single delivery address

are relatively the same times. Transport networks for wholesale

are typically a milk run. The impact of individual truck turn

times, and the reliability of the truck turn times, might have

a big impact on overall efficiency and reliability of these

networks.

Therefore LTL truck turn times were translated to FLT truck

turn times to eventually get an indication on the ‘unloading’

time per pallet. The average truck turn time based on full

trucks was 4 hours and 11 minutes (LTL truck turn times

translated to FLT truck turn times).

For retail this was 2 hours and 32 minutes and for wholesale

with smaller shipments 5 hours and 37 minutes. So the waste of

time is twice as big at wholesale distribution locations as

opposed to retail distribution locations.

The distribution centre or retailer Jumbo in Veghel

(Netherlands) completed the goods receiving process in 50

minutes average and was announced winner of the Speed Docking

contest, and this even during the busy days around Pentecost

and Easter.

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Figure 2: truck turn times (Speed Docking data 2012)

Dropsizes between retail and wholesale are different (see

figure 3); 80% of shipments to wholesale is less than 10

pallets per drop, 80% of shipments to retail are more than 10

pallets per drop.

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Figure 3: Dropsizes (Speed Docking data 2012)

Observations

Process mining gives management a strong insight in processes

and bottlenecks. Analyzing transactional data from the onboard

computers has shown three practical take-aways for the

companies participating in the Speed Docking project.

Ordering full trucks, and bundling of volumes by shippers to

full truckloads, is more efficient and speeds up the goods

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receipt process enormously. In addition, a better flow provides

a distribution centre that is more at ease. Bigger shipments

score better in terms of ‘docking efficiency’.

Reliability is more important than quick. Loading and unloading

outside the agreed time window disrupts the dock planning, the

downstream processing in the distribution centre and causes an

unnecessary use of trucks because of the safety margins that is

use for tactical and operational planning.

The mindset of the staff turns out also to be of importance.

After the official contest there has been an additional ‘blind’

measurement of the truck turn times for two weeks, without the

staff knowing. Jumbo was again the fastest retail distribution

centre.

5. Framework for improving reliability of truck turns

Based on the findings of the Speed Docking project in 2011 and

2012 Mars and Heinz developed a qualitative framework in

workshops and round tables to improve the reliability of truck

turn times and, in near future, realising shorter truck turn

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times. Further quantitative research is necessary on the impact

of these measures.

The ‘Speed Docking’ project bundles innovative and sometimes

complex concepts like scanning of special pallet labels (SSCC),

precise advanced delivery notifications via EDI, data

synchronization, reduction of truck movements by bundling of

orders, even with other suppliers and full trucks with ‘Green

Order’.

Green order

Speed Docking uses an obvious order of events. Companies

started measuring the ‘Green Order’; how full is a truck

actually? The smaller the number of trucks going to a

distribution centre, the smaller the delay. The ‘Green Order’

indicates how sustainable the orders are, based on CO2

emission. This ‘Green Order’ concept is part of the Lean and

Green Award program by Connekt.

Paperless processes: ‘Scan & Go’

The second stage is ‘Scan & Go’, that consists of scanning all

the pallets upon receipt, based on the standard pallet labels

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(SSCC), advanced delivery notifications and supply chain data

alignment (Vermeer, 2010). The standards for electronic data

interchange and data alignment are developed by GS1.

Geo Docking

The third stage is the linking of ‘geo fencing’ (Reclus e.a.,

2009) with ‘dynamic dock planning’ (Veloso, 1998); geo docking.

The receiving distribution centre will assign the unloading

dock only when the truck is in the immediate area. This can be

determined by geo fencing technology that allows the definition

of one or more areas of relevance for each truck. Once a truck

enters or leaves a certain area, a message is sent via e-mail

or SMS to the planner. The on board computer in the truck

announces itself from e.g. 20 kilometres away and triggers

certain software agents to start the negotiation about which

unloading dock to use based on availability. Following this,

the driver is notified via the onboard computer of the exact

unloading dock to use. This can be linked to time slot planning

and booking (Koolstra, 2005).

Dock-and-Roll

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The final stage is Dock-and-Roll. The truck is equipped with a

rolling floor, drives up to the loading back and is

automatically emptied when cargo is moved over the track.

6. Conclusions

With a more efficient use of means of transport, avoiding

unnecessary supply chain activities and vertical and horizontal

collaboration in the FMCG industry and integration of

processes, the transport industries needs to accomplish an

annual increase in productivity of 2% year-by-year to overcome

future shortage in truck drivers.

Using existing technologies the transport industry serving the

FMCG industry can realise improvements with the use of mobile

communication, on board computers, connected navigation and

intelligent transport systems, process mining based on big data

to benefit better tactical and operational planning, geo-

fencing, agent based dynamic planning and time slot allocation

and the use of community platforms were data is shared with all

supply chain partners in FMCG.

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Speed Docking translates technological innovations to common

understandable concepts in such a way that the employees, the

truck drivers and the warehouse staff are enthusiastic

contributors to the improvements. The results for 2011 were

already challenging, but the 2012 results show that there are

10 to 20% too many trucks in the transport network. That’s not

a very sustainable outlook. By bundling innovations, the

transport industry might improve future productivity in FMCG

transport networks.

7. Discussion and suggestion for further research

Further quantitative research is necessary on the impact of the

measures brought forward by the companies participating in this

study; what will be the impact on transport capacity and cost

of the measures?

This study was focused on the transport flows of two companies

(Mars and Heinz) to distribution centres. Further research

needs to been done on all incoming shipments to a distribution

centre, in order to develop new planning, scheduling and

priority rules.

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This study did not look at the impact of reliability of truck

turn times on warehousing operations. Further research is

currently going on to integrate internal warehousing operations

in the framework.

In 2013 the ‘Speed docking’ project will be continued with 20

participants for the FMCG industry as part of the Lean and

Green program of Connekt.

References

Bock, S. 2010, “Real-time control

of freight forwarder transportation networks by integrating

multimodal transportchains”. European Journal of Operational Research.

2010 – Elsevier

CapGemini and Consumer Goods Forum 2010. Future supply chain

2016. Utrecht: CapGemini

CapGemini and Consumer Goods Forum 2011. Future value chain

2020: building strategies for the new decade. Utrecht:

CapGemini

21

Caris, A., Macharis, C., & Janssens, G. K. 2008. “Planning

problems in intermodal freight transport: Accomplishments and

prospects”. Transportation Planning and Technology, 31(3), 277-302

Connekt/SPL 2011. Human Capital Agenda Topsector Logistiek.

Delft: Connekt

Crainic, T.G., and G. Laporte 1997. “Planning models for

freight transportation”. European Journal of Operational Research 97,

409-438 (1997)

Crainic, T.G. 2000. Service network design in freight

transportation. European Journal of Operational Research. 2000 -

Elsevier

Morlok, E. K., & Riddle, S. P. 1999. Estimating the capacity of

freight transportation systems: a model and its application in

transport planning and logistics. Transportation Research Record:

Journal of the Transportation Research Board, 1653(-1), 1-8.

Barrett S. M. 2001. A computer simulation model for predicting

the impacts of log truck turn-time on timber harvesting system

productivity. Diss. Virginia Polytechnic Institute and State

University, 2001

22

Boysen, N. 2010a. “Truck scheduling at zero-inventory cross

docking terminals”. Computers & Operations Research, 37(1), 32-41.

Boysen, N., Fliedner, M., & Scholl, A. 2010b. “Scheduling

inbound and outbound trucks at cross docking terminals”. OR

spectrum, 32(1), 135-161

Bughin, J., M. Chui, and J. Manyika 2010. "Clouds, big data,

and smart assets: Ten tech-enabled business trends to

watch." McKinsey Quarterly 56 (2010)

Eurostat/European Union 2011. EU transport in figures.

EU/Eurostat Brussels

Huynh, N.H. 2005, Methodologies for Reducing Truck Turn Time at

Marine Container Terminals. Ph.D. Dissertation University of

Texas, Austin

Huynh, N., & Walton, C. M. 2007. “Evaluating truck turn time in

grounded operations using simulation”. World Review of Intermodal

Transportation Research, 1(4), 357-386.

Koolstra, K. 2005. Transport infrastructure slot allocation,

Thesis Trail Delft

Lam, S. F., J. Park, and C. Pruitt 2007. “An Accurate

Monitoring of Truck Waiting and Flow Times at a Terminal in the23

Los Angeles/Long Beach Ports. No. METRANS AR 05-01. METRANS,

2007.

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R.,

Roxburgh, C., et al. (2011). Big data: The next frontier for

innovation, competition, and productivity. McKinsey Global

Institute

Maruster, Laura, et al 2002. "Process mining: Discovering

direct successors in process logs." Discovery Science. Springer

Berlin/Heidelberg, 2002

Ploos van Amstel, M.J., D. Farmer 1990. “Controlling the

Logistics Pipeline”. International Journal of Logistics Management, The,

Vol. 1 Iss: 1, pp.19 – 27

Reclus, F., and K. Drouard 2009. "Geofencing for fleet &

freight management." Intelligent Transport Systems Telecommunications,

(ITST), 2009 9th International Conference on. IEEE, 2009

Sathasivan, K., Ng, M., & Waller, S. T. 2011. A Robust

Heuristic for Scheduling the Loading and Unloading of Trucks.

In Transportation Research Board 90th Annual Meeting (No. 11-2537).

24

Shu, J., & Zhang, J. 2011. Simulation and Optimization of

Loading and Unloading Operation System in Port Logistics Park

Based on Arena. In ICTE 2011 (pp. 476-481). ASCE

Veloso, M., M. E. Pollack, and M. T. Cox 1998. "Rationale-based

monitoring for planning in dynamic environments." Proceedings of

the 4th International Conference on AI Planning System. 1998.

Vermeer, B. H. P. J. 2001. "Data Quality and Data Alignment in

E-Business." Technische Universiteit Eindhoven, Eindhoven

Wang, X. and A.C. Regan 2002. “Local truckload pickup and

delivery with hard time window constraints”. Transportation

Research Part B 36, 97-112 (2002)

Walker, W.T. 1992. “Network economics of scale in short haul

truckload operations”. Journal of Transport Economics and Policy XXVI

(1), 3-17

Yu, W., & Egbelu, P. J. 2008. “Scheduling of inbound and

outbound trucks in cross docking systems with temporary

storage”. European Journal of Operational Research, 184(1), 377-396

Zhao, W., & Goodchild, A. V. 2010. “The impact of truck arrival

information on container terminal rehandling”. Transportation

Research Part E: Logistics and Transportation Review, 46(3), 327-343.

25

Websites

Unilever (2012),

http://www.unilever.nl/media/persberichten/2012/Unileverstimule

ertduurzamegroeimetnieuwEuropeeslogistiekproject.aspx - EU

Marco Polo project

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