What are the logistics implications for PressCo to ensure high delivery reliability to customers WestCo and EastCo?
Measuring schedule variability
A problem that is all too familiar to suppliers in the automotive industry is that of schedule variability. A vehicle assembler issues delivery schedules to specify how many parts of each type are required each day for the following month. And each day a
‘call-off’ quantity is issued, which specifies how many the vehicle assembler actually wants. The two sets of figures are not necessarily the same, although usually they add up to the same cumulative numbers for the month as a whole. In other words, the
total scheduled quantities and the total call-off quantities are the same. So what is the problem?
The problem is that the supplier has to cope with the variability of call-off quantities that create huge problems for the supplier’s process. Let S = Scheduled demand, and A = Actual call-off quantity. Then the difference D between schedule and actual is given by D = S – A.
If the supplier produces to schedule, there are two possible scenarios that follow:
• S > A, so the supplier will over-produce the part and end up with excess stock; or
• S < A, so there is a shortfall (S – A) of parts from the supplier, unless the supplier holds
a stock of the parts, in which case it is an opportunity to reduce them.
The two conditions (S > A and S < A) therefore have different logistics implications.
Figure 1.8 shows that actual demand, totalled across four different parts at PressCo
(a supplier of pressed metal components), may be up to 1,600 units above schedule,
or 2,200 below schedule when supplying the vehicle assembler WestCo. This range has
been divided up into intervals of 100 units. The mode (0-99) indicates that S = A for a
frequency of 18 per cent of the observations.
Figure 1.8 Distribution of differences between scheduled and actual demand for
Assuming that the distribution is roughly normal, the standard deviation (SD) is 573,
which is characteristic of the flat, wide spread of data. Figure 1.9 shows the distribution
of S – A for four similar parts from the same supplier, PressCo, but for a different vehicle
assembler, EastCo. This time, the SD for the distribution is 95, representing a much narrower spread of differences than for the assembler, WestCo.
Figure 1.9 Distribution of differences between scheduled and actual demand for
1 What are the logistics implications for PressCo to ensure high delivery reliability to customers WestCo and EastCo?
2 What steps will the supplier need to take in order to satisfy call-off orders from WestCo?
3 If separate parts of the PressCo factory were dedicated to production for WestCo and for EastCo, which would be the more efficient in terms of labour costs and inventory
Quality is not just about meeting target pick accuracy or target defect levels. It is
also about controlling variability. The same argument can be made about costs. The
implication of dependability for logistics is that supply chain processes need to be
robust and predictable. In Chapter 6 we develop the case for dependability in supply
chains under the themes of planning and control and just-in-time pull scheduling.
Dealing with uncertainty: the agility advantage
Dealing with uncertainty means responding rapidly to changes that affect logistics processes. These changes may be unanticipated, or anticipated but not with
a high degree of accuracy. Sometimes, problems can be foreseen – even if their
timing cannot. Toyota UK manages inbound deliveries of parts from suppliers in
southern Europe by a process called chain logistics. Trailers of parts are moved in
four-hour cycles, after which they are exchanged for the returning empty trailer
on its way back from the UK. One hitch in this highly orchestrated process means
that incoming parts do not arrive just-in-time at the assembly plant. Toyota demands that its suppliers, and logistics partner, plan countermeasures. This means
that alternative routes for suppliers to deliver to its Burnaston assembly plant in
the UK have been planned in advance to deal, for example, with a French channel
ferry strike at Calais. The weather is also a cause of uncertainty in logistics – for
example, it may mean that Tesco has to switch between salads and soups as the