The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective

Abstract

This paper examines the beneficial impact of information sharing in multi-echelon supply chains. We compare a traditional supply chain, in which only the first stage in the chain observes end consumer demand and upstream stages have to base their forecasts on incoming orders, with an information enriched supply chain where customer demand data (e.g. EPOS data) is shared throughout the chain. Two types of replenishment rules are analysed: orderup-to policies and smoothing policies (policies used to reduce or dampen variability in the demand). For the class of order-up-to policies, we will show that information sharing helps to reduce the bullwhip effect (variance amplification of ordering quantities in supply chains) significantly, especially at higher levels in the chain. However, the bullwhip problem is not completely eliminated and it still increases as one moves up the chain. For the smoothing policies, we show that information sharing is necessary to reduce order variance at higher levels of the chain. The methodology is based on control systems engineering and allows us to gain valuable insights into the dynamic behaviour of supply chain replenishment rules. We also introduce a control engineering based measure to quantify the variance amplification (bullwhip) or variance reduction.

Current economic downturn and supply chain: the significance of demand and inventory smoothing

“…In order to contrast the scenarios, Figure 1 depicts the ORVrR measures using the echelon position as independent variable, according to Dejonckheere et al (2004). Furthermore, we report the equation of the trend line for each supply chain configuration and consequently the angle of inclination of the linear regression of Dejonckheere et al's (2004) curves.…”

“…In order to dampen or avoid the bullwhip effect, several countermeasures can be undertaken. Among those are (1) redesigning the physical process, (2) redesigning the information patterns and (3) redesigning the decision process (Van Ackere et al 1993;Dejonckheere et al 2004). The first approach could be realised through two of the bullwhip reduction principles: 'time compression' and 'echelon elimination' (Geary et al 2006).…”

“…The authors state that a geometric increase of the ORVrR interpolating curve is representative of strong bullwhip propagation, more intense than in a linear trend Cannella and Ciancimino (2010). Dejonckheere et al's (2004) curve is a smart representation of bullwhip propagation in a multi-echelon system and serves to concisely compare different supply chain configurations (Ciancimino et al 2012 …”

Altruistic behaviour in a two-echelon supply chain with unmatched proportional feedback controllers

“…ISSN 1740-8865. DOI: 10.1504/IJISTA.2009 These expressions for the variances may, in general, be obtained by a variety of ways, from stochastic analysis Hosoda and Disney (2006a) via the frequency domain Dejonckheere et al (2004), control theory Disney and Towill (2003), or state space methods Gaalman and Disney (2006). However we will not provide further details here due to space requirements.…”

On the Bullwhip Avoidance Phase: supply chain collaboration and order smoothing

“…The rationale for this policy is to limit the tiers' overreaction/under-reaction upon changes in demand. Smoothing replenishment rules mitigate bullwhip effect (Dejonckheere et al 2004, Disney et al 2004b, Warburton 2004, Jakšič and Rusjan 2008, Kim and Springer 2008 ). However, dampening order variability may have negative impact on customer service (Dejonckheere et al 2003, Disney and Lambrecht 2008, as excessive smoothing of order rate could impede to fulfil the marketplace demand in time.…”