The impact of real time transport information on manufacturing scheduling
Riccardo Mogre*
r.mogre@hull.ac.uk
Chandra S. Lalwani
c.s.lalwani@hull.ac.uk
Hull University Business School
Kingston upon Hull, HU6 7RX, UK
*Contact author. Phone number: +44 (0) 1482 34-7557
Keywords: Assembly systems, Vehicle tracking, supply chain synchronisation, simulation model
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Research Problem
This paper considers a two-stage supply chain in which components are shipped from multiple
suppliers to one manufacturer. Suppliers can be identified at a fabrication stage, wherein the
manufacturer represents a multi-product assembly stage in which all components are incorporated
into a final product. It is reasonable to assume that all the suppliers contribute to every final product
by shipping at least one component.
Moreover, we consider the manufacturer and the suppliers operating in a Just in Time (JIT)
setting, leading to further assumptions. Firstly, we consider a final assembler using flexible
machines with low setup times and costs. The manufacturer is therefore able to produce a variety of
products in small lots to better match the supply with the demand profile (mixed modelling
principle). For the sake of simplicity, we assume that the final assembler manufactures the entire
range of products every day. Moreover we consider suppliers operating according to a level
delivery schedules principle, i.e. they dispatch each component in a separate shipment and make
sure that the components arrive in time to meet the production schedules of the final product for
which they are required.
The suppliers’ demand is deterministic and is computed from the daily production schedule
of the final product. On the other hand, we take into account stochastic dispatching lead times and
processing lead times. The variability of lead times considerably affects the production schedule
and the delivery plan.
This paper aims to assess how much real time information about the expected delivery times
of delivery of components can contribute to the efficient production scheduling in a JIT setting.
Recent technology enhancements, such as vehicle tracking, which enable accurate and real time
measurement of transport operations, do in fact generate much higher levels of operational control
than before (Lalwani et al., 2004). With better levels of control, the possibility of effectively
integrating transport with other supply chain functions may develop (Closs et al., 1997; Kärkkäinen
et al., 2004). For instance, the knowledge that certain materials will arrive earlier than those
scheduled first for production allow operators to dynamically reschedule the plans on the basis of a
First Come First Served (FCFS) policy (first best tactics). Without this vital information, the
operators of the plant will prefer to process materials in the order originally defined by the
scheduled production plan, unaware of shipment delays and whether other shipments will arrive
first (second best tactics). Therefore the purpose of this paper is to assess the difference in
performance between first best and second best tactics. The performance measure used to compare
the two tactics will be ‘hours of no production’ representing the hours of plant inactivity when
delays in transport operations of components prevent the production from starting.
Assembly systems such as the one described in this paper have been studied in previous
literature so as to determine optimal lot size. Blackburn and Millen (1982) proposed approximate
techniques to solve the lot-sizing problem in assembly systems while Afentakis et al. (1984) solved
exactly the lot-sizing problem for a specific configuration of the system. Nevertheless these studies
do not take into consideration the variability of lead times. Song and Zipkin (2009) recently studied
the order policies of an inventory system with multiple supply sources and stochastic demand and
lead times. Nevertheless their analysis is mainly focused on serial multi-stage inventory systems
and, in the knowledge of the authors, their approach has not been extended yet to assembly systems.
Potter et al. (2007) and Haughton (2008) have studied the synchronisation of transport operations
with other supply chain activities. Both studies analyse a complex system similar to the one
described in the present paper and they use simulation techniques to investigate the performance of
the systems. In accordance with the approach used by these two latter studies, in the present paper
we propose to assess the impact of real time transport information on manufacturing scheduling
through simulation techniques. Further studies might extend the present discussion by using the
analytical approach (based on stochastic process modelling and queuing theory) presented by the
aforementioned paper of Song and Zipkin (2009).
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Key Methodology and Assumptions
This paper employs spreadsheet simulation modelling to describe the model with a single supply
source (scenario A) and then extend the model to multiple supply sources (scenario B).
In the first scenario (scenario A), each material delivered by the supplier is the sole
component that the final assembler needs to manufacture the final product. This case is uncommon
in the real world and it is only used to introduce the rationale used in the simulation engine. Each
shipment of components directed to the final assembler is defined by the following parameters: type
of material (every shipment carries a single component at a time), quantity of component carried,
scheduled time of arrival to the assembler’s plant and scheduled time when the production process
associated to the delivery should be completed. The simulation engine on the basis of a normal
distribution randomly generates the scheduled delivery plan. Nevertheless, the scheduled delivery
plan is not made at random; rather takes into account some constraints (e.g. the last shipment must
arrive during the hours of operation of the plant, the daily plan of deliveries should warrant a
sufficient number of daily working hours for the plant, etc.). This scheduled delivery plan would be
deterministic if there was no variability in the lead times.
The variability elements considered in the model are the following: a normal factor –
modelling the intrinsic random disturbance affecting the delivery process – and a Poisson factor,
modelling the impact on delivery lead time of the occurrence of rare but critical major events (e.g.
snow, accidents, engine failure). The use of the Poisson and normal factors has been done in
accordance to what is suggested by statistics textbooks (e.g. Grinstead and Snell, 1997). Also basic
queuing theory applications, which model the arrivals of unforeseen events, are usually based on a
Poisson inputs (Hillier and Liebermann, 2010). A second normal factor, which models the intrinsic
random variability of the production lead-time has also been considered in the model (through a
variability of the quantity of the component, which impacts on the working time).
Through an appropriate Design of Experiment (DOE) the first best tactics (which include the
possibility of reschedule the production with the knowledge of the actual expected arrival time of
the components) and the second best tactics (which process the components strictly on the basis of
the scheduled delivery plan) have been compared.
In the second scenario (scenario B) we introduce multiple independent supply sources. In
this second scenario, every component (which corresponds to a delivery) is associated to multiple
products and also every product is associated to multiple components. A bill of materials
determines the relationship between every final product and the components provided by the
suppliers. The aforementioned description of variability elements and design of experiments is also
valid for scenario B.
Summary of major results and significance
In order to identify the significant difference between the hours of no production obtained applying
the first best tactics and the ones obtained applying the second best tactics, a one-way ANOVA
(Analysis of Variance) – including F-test – has been used. The results of the model show that there
is a significant difference, as far as the hours of no production are concerned, between adopting the
first best tactics – enabled by a real-time tracking solution of the shipment – and the second best
tactics, where a real-time shipment tracking solution is not present. Moreover, switching from a
second best to first best tactics lead to a 34% reduction of the hours of no production in the case of a
single supply source (scenario A) and a 14% reduction of the hours of no production in the case of
multiple supply sources (scenario B with 10 supply sources). In scenario B, the reduction is less
than in scenario A because the presence of the bill of material makes re-scheduling production plans
less flexible.
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The model rationale and implications are extremely significant for those companies
operating with low level of stocks of components. The model is extremely simple and can be easily
extended to more complex cases of multi-stage manufacturing and assembly systems.
The authors are currently working on applying the basic concepts underlying the simulation
model to a more analytical setting, which will be based on the approach suggested by Song and
Zipkin (2009) and will provide a richer interpretation of the problem studied.
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