Logistic sensitivity indices

4.1.5.1 General

The concept of a logistic sensitivity index (LSI) is to quantify the sensitivity of a manufacturing location in terms of the logistics costs associated with manufacturing as a function of net added costs.

4.1.5.2 Data analysis

As previously noted, the aims of the LSI data analysis were to identify:

• The logistics costs associated with product manufacture. These costs include

transport costs of both raw materials and finished products, plus any additional costs resulting from handling / storage.

• The value added by the manufacturing process. This has been taken as the net difference between the price at which goods are sold to customers and the purchase price of the raw materials from which the products are manufactured.

For each product for each market destination, the LSI value is the ratio of logistics costs divided by the value added. The key steps of the procedure, carried out were:

! Value of products produced

! Value of raw materials

! Raw material transport costs

! Raw material handling costs

! Transport of manufactured products

The LSI values were calculated from a formula derived by Pak-Poy & Kneebone as:

) /(

)

( _i^j _i^j _i^j _i^j _i^j

j

i C E D A B

LS = + + −

where:

j

LSi = Logistics Sensitivity Index for product line i for destination market j.

j i j

i E

C , = Transport costs respectively for raw materials and manufactured products for product line i , destination j.

j

Di = Materials handling charges for product line i and destination j.

j i j

i B

A , = Sales income and raw materials costs respectively for product line i and destination j.

Table 4.1 summarises LSI values derived via the analysis procedure outlined above. Detailed costs are not reported as these are of commercial value to competitors.

LSI values increase as the cost of freight to destinations increases. Products sold domestically are less transport cost sensitive than those sold in Europe. The LSI values confirm that lightweight products with higher value added are significantly less sensitive to transport and other logistics costs than heavier, lower value added products.

Table 4.1 Calculated logistic sensitivity indices

SKS Ltd Freudenberg SH Ltd Direct Customer Delivery Domestic 5.2 1.5

Export 8.8 2.3

Delivery to Distribution Centre Domestic 3.3 1.1 Export not applicable 1.7

4.1.5.3 Summary

The key aspects of business operations as observed in this chapter were:

! The market sector in which the business operates – either commodity or manufactured products. Businesses operating in the former can be subject to significant fluctuations in raw material prices and hence in levels of demand for their products. Conventional product manufacturers, whilst being affected by fluctuations in product demand, can usually plan their manufacturing strategies with much greater degrees of certainty, particularly with respect to profit margins.

! The relative proportions of products sold domestically or exported. If most products are sold domestically, then there will be a locational sensitivity to domestic freight costs; for export oriented companies, freight costs for manufactured / processed products is only a minor logistical issue.

! Extent of value added in the manufacturing chain. Products with higher proportions of value added are generally less sensitive to transport and locational issues.

! The nature of the product – whether light or bulky, or heavier and more difficult to handle.

4.1.5.4 Suggestions for further work on relative sensitivity

Classical sensitivity as introduced by House (1998) is a normalised measure of the change in some desired quantity with respect to the change in some system parameter. If T is the desired quantity and a is the parameter, the sensitivity is given by:

T a

∆a

= ∆Τ

aT

S

This form of sensitivity is perfectly good, but if one is adamant about the desired value of T, it certainly gives a measure of the normalised deviation from this value.

Comparison sensitivity on the other hand, for which the deviation of some desired quantity, i.e. LSI, with respect to parameter changes (i.e. in raw material costs) is compared with the optimum values defined by the appropriate business requirements.

The unifying theme in what has been discussed above is the recurrence of the nominal quantity, which is always used as a basis for comparison. Because they are based on an absolute desired quantity, such sensitivities might be called absolute.

The relative sensitivity is a normalised measure of optimality. The optimal value of relative sensitivity is zero for every analysis regardless of the absolute value of the optimal performance index. Those parameters that provide values near their optimal over a wide range of controls remain near zero in relative sensitivity; they are relatively insensitive to the choice of control factors. When a control is to be found which in some sense provides optimality to, for instance, a logistical location analysis problem, it is good procedure to allow those parameters with small relative sensitivity to have a lesser affect on the decision. The optimisation of a logistical system decision-making analysis depends a great deal on the system requirements. Two reasonable decision-making criteria can be conjectured:

1 Minimise the maximum deviation from optimal values 2 Minimise the average deviation from optimal values

Either of these criteria might be applied when one is attempting to find a single controller for a number of similar examined systems.

In this chapter, due to lack of information from the examined companies (data confidentiality in most cases), a relative sensitivity analysis could not be conducted.

Having a nominal value for indices such as annual value of products produced, value of raw materials purchased, raw material transport costs, raw material handling costs, and transport of manufactured products would make it possible to evaluate the normalised measure of optimality.

4.2 Development of a Complete Micro-logistics Audit System in