The impact of supply chain performance measurement systems on dynamic behaviour in supply chains

The amplification of demand variation up a supply chain widely termed ‘the Bullwhip Effect’ is disruptive, costly and something that supply chain management generally seeks to minimise. Originally attributed to poor system design; deficiencies in policies, organisation structure and delays in material and information flow all lead to sub-optimal reorder point calculation. It has since been attributed to exogenous random factors such as: uncertainties in demand, supply and distribution lead time but these causes are not exclusive as academic and operational studies since have shown that orders and/or inventories can exhibit significant variability even if customer demand and lead time are deterministic. This increase in the range of possible causes of dynamic behaviour indicates that our understanding of the phenomenon is far from complete. One possible, yet previously unexplored, factor that may influence dynamic behaviour in supply chains is the application and operation of supply chain performance measures. Organisations monitoring and responding to their adopted key performance metrics will make operational changes and this action may influence the level of dynamics within the supply chain, possibly degrading the performance of the very system they were intended to measure. In order to explore this a plausible abstraction of the operational responses to the Supply Chain Council’s SCOR® (Supply Chain Operations Reference) model was incorporated into a classic Beer Game distribution representation, using the dynamic discrete event simulation software Simul8. During the simulation the five SCOR Supply Chain Performance Attributes: Reliability, Responsiveness, Flexibility, Cost and Utilisation were continuously monitored and compared to established targets. Operational adjustments to the; reorder point, transportation modes and production capacity (where appropriate) for three independent supply chain roles were made and the degree of dynamic behaviour in the Supply Chain measured, using the ratio of the standard deviation of upstream demand relative to the standard deviation of the downstream demand. Factors employed to build the detailed model include: variable retail demand, order transmission, transportation delays, production delays, capacity constraints demand multipliers and demand averaging periods. Five dimensions of supply chain performance were monitored independently in three autonomous supply chain roles and operational settings adjusted accordingly. Uniqueness of this research stems from the application of the five SCOR performance attributes with modelled operational responses in a dynamic discrete event simulation model. This project makes its primary contribution to knowledge by measuring the impact, on supply chain dynamics, of applying a representative performance measurement system.

Current capacity limits and activities within the EU project MODE-GAP to overcome them

In this presentation we will discuss the implications of the so called Nonlinear Shannon Limit. We will compare technologies including new fibres for long haul transmission and techniques to expand the capacity of existing standard single mode fibres.

Factors determining the growth curve of the foliose lichen Parmelia conspersa

Growth curves of the foliose lichen Parmelia conspersa (Ehrh. Ex Ach.)Ach. Were obtained by plotting radial growth (RGR, mm yr-1) of the fastest measured lobe, the slowest measured lobe, a randomly selected lobe, and by averaging a sample of lobes from each thallus against thallus diameter. Growth curves derived from the fastest-growing lobe and by averaging lobes were asymptotic and could be fitted by the growth model of Aplin and Hill. Mean lobe width increased with thallus size, reaching a maximum at approx. 4.5 cm thallus diameter. In four out of six thalli, radial growth of lobes over four months was positively correlated with initial lobe width or area. The RGR of isolated lobes was unaffected until the base of the lobe was removed to within 1-2 mm of the tip. The concentration (micrograms mg-1 biomass) of ribitol, arabitol and mannitol was greater in the marginal lobes of large than in small thalli. The results suggested that the growth curve of P. conspersa is determined by processes that occur within individual marginal lobes and can be explained by the Aplin and Hill model. Changes in lobe width and in the productive capacity of individual lobes with thallus size are likely to be more important factors than the degree of translocation within the lobe in determining the growth curve.

Investment efficiency among a cross-section of UK firms:implications for the debate on financing constraints

In this paper, we address this policy issue using a stylised methodology that relies on estimates of the cash flow sensitivity of firms’ investment, as well as a relatively new methodology that enables us to generate a (0, 1) bounded measure of investment efficiency of firms, i.e., the efficiency with which firms can convert their sales into investment, after controlling for unobserved year- and industry-specific effects. Higher investment efficiency is associated with lower financing constraint. Our results indicate that there is considerable heterogeneity in investment efficiency across firms, during a given year; the range being 0.57-0.82. However, the average investment efficiency measure is similar across years, regions and NACE 2-digit industries. We also do not find discernible patterns in the relationship between investment efficiency and firm size, both before and during the financial crisis. The results suggest that while some firms are clearly less efficient at translating their performance into investment, broad policies targeting firms of a certain size, or those within a particular industry or region, may not successfully address the problem of financing constraint in the United Kingdom. The targeting of firms with financing constraints may have to be considerably more refined, and look at not easily observable factors such as credit history/events and organisational capacity of the firms.

Financial constraints in investment:panel data results from Estonia, 1995–1999

To investigate investment behaviour the present study applies panel data techniques, in particular the Arellano-Bond (1991) GMM estimator, based on data on Estonian manufacturing firms from the period 1995-1999. We employ the model of optimal capital accumulation in the presence of convex adjustment costs. The main research findings are that domestic companies seem to be financially more constrained than those where foreign investors are present, and also, smaller firms are more constrained than their larger counterparts.

The MODE-GAP project

The MODE-GAP project was established to identify the optimum fiber type for use beyond the capacity crunch. This paper will review some of the key drivers behind the project and analyze the key limits and potential improvements © 2013 IEEE.