Ahmed Shaban

The impact of information sharing and inventory control coordination on supply chain performances

We analyze bullwhip effect and inventory stability in a multi-echelon supply chain.The impact of many information sharing scenarios is investigated via simulation.We further investigate the impact of forecasting and inventory control parameters.Information sharing is the most significant factor to improve the performances.Complex interactions in supply chain decrease the benefits of information sharing. The lack of coordination in supply chains can cause various inefficiencies like bullwhip effect and inventory instability. Extensive researches quantified the value of sharing and forecasting of customer demand, considering that all the supply chain partners can have access to the same information. However, only few studies devoted to identify the value of limited collaboration or information visibility, considering their impact on the overall supply chain performances for local and global service level. This paper attempts to fill this gap by investigating the interaction of collaboration and coordination in a four-echelon supply chain under different scenarios of information sharing level. The results of the simulation study show to what extent the bullwhip effect and the inventory variance increase and amplify when a periodic review order-up-to level policy applies, noting that more benefits generate when coordination starts at downstream echelons. A factorial design confirmed the importance of information sharing and quantified its interactions with inventory control parameters, proving that a poor forecasting and definition of safety stock levels have a significant contribution to the instability across the chain. These results provide useful implications for supply chain managers on how to control and drive supply chain performances.

The impact of information sharing on ordering policies to improve supply chain performances

We study bullwhip effect and inventory stability in a multi-echelon supply chain.The impact of information sharing in supply chain is investigated via simulation.We first examine the supply chain performances under a traditional (R, S) policy.An easy-to-implement information sharing ordering policy (IS) is evaluated.The IS outperforms the (R, S) in terms of bullwhip effect and inventory variance. Bullwhip effect represents the amplification and distortion of demand variability as moving upstream in a supply chain, causing excessive inventories, insufficient capacities and high operational costs. A growing body of literature recognizes ordering policies and the lack of coordination as two main causes of the bullwhip effect, suggesting different techniques of intervention. This paper investigates the impact of information sharing on ordering policies through a comparison between a traditional (R, S) policy and a coordination mechanism based on ordering policy (a combination of (R, D) and (R, S) policies). This policy relies on a slow, easy to implement, information sharing to overcome drawbacks of the effect, in which replenishment orders are divided into two parts; the first is to inform the upstream echelons about the actual customer demand and the second is to inform about the adjustment of the inventory position, smoothing at the same time the orders of the different levels of the supply chain. A simulation model for a multi-echelon supply chain quantifies the supply chain dynamics under these different policies, identifying how information sharing succeeds to achieve an acceptable performance in terms of both bullwhip effect and inventory variance.

Exploring the Bullwhip Effect and Inventory Stability in a Seasonal Supply Chain

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.

SPC forecasting system to mitigate the bullwhip effect and inventory variance in supply chains

We study the impact of forecasting on the bullwhip effect and inventory variance.A forecasting system (SPC-FS) based on control charts is presented and evaluated.Simulation is adopted to evaluate SPC-FS in a supply chain employs OUT policy.SPC-FS leads to lower bullwhip effect and inventory variance than MA and ES.SPC-FS has less sensitivity to lead-time compared to the other methods. Demand signal processing contributes significantly to the bullwhip effect and inventory instability in supply chains. Most previous studies have been attempting to evaluate the impact of available traditional forecasting methods on the bullwhip effect. Recently, some researchers have employed SPC control charts for developing forecasting and inventory control systems that can regulate the reaction to short-run fluctuations in demand. This paper evaluates a SPC forecasting system denoted as SPC-FS that utilizes a control chart approach integrated with a set of simple decision rules to counteract the bullwhip effect whilst keeping a competitive inventory performance. The performance of SPC-FS is evaluated and compared with moving average and exponential smoothing in a four-echelon supply chain employs the order-up-to (OUT) inventory policy, through a simulation study. The results show that SPC-FS is superior to the other traditional forecasting methods in terms of bullwhip effect and inventory variance under different operational settings. The results confirm the previous researches that the moving average achieves a lower bullwhip effect than the exponential smoothing, and we further extend this conclusion to the inventory variance.

Information sharing policies based on tokens to improve supply chain performances

One of the most studied dynamics of supply chains is a phenomenon that has been named ‘the bullwhip effect’. What happens is that variations in customer demand are translated into wider and wider variations in orders issued by companies along the supply chain, affecting performances and increasing the level of complexity in transactions and relationships among partners. This paper introduces the opportunity of measuring the performance of a supply chain in case of disruption, proposing a progressive information sharing technique (token approach) in order to control bullwhip effect. This technique relies on dividing orders into two streams: the first stream transmits the value of the demand to the whole supply chain echelons whereas the second one includes the adjustments needed to keep a stable inventory for each partner of the network. To investigate the token approach, a simulation model is developed for a four-echelon supply chain where it is assumed that lead times for transferring information or materials are deterministic and suppliers have unlimited production and inventory capacities. Four different ordering policies are evaluated and the results analysed to identify general findings.

Replenishment policy based on information sharing to mitigate the severity of supply chain disruption

Modern supply chains are attempting to gain competitive advantages in a fiercely competitive marketplace through adopting new initiatives and practices such as lean and just-in-time. These initiatives are suitable for a stable world but they could make the supply chain more vulnerable to the external disruptions such as natural and man-made disasters. This paper aims at studying the dynamics of a disrupted supply chain under a coordination mechanism that is designed to achieve efficiency and resiliency. The proposed procedure relies on establishing a novel replenishment policy based on an information sharing approach to replace traditional policies. In this policy, replenishment orders will be divided into two streams, transmitting both real demand information and required inventory adjustments to the whole supply chain. A simulation model for a four-echelon supply chain has been considered to evaluate the information sharing policy and to compare it with an order-up-to level policy, determining the dynamics of ordering and inventory before and after the disruption. The results showed how the suggested approach was successful in recovering the disrupted supply chain to a stable performance by reducing effects on inventory and ordering patterns.

Automated Identification of Basic Control Charts Patterns Using Neural Networks

The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns; natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.

A real-time SPC inventory replenishment system to improve supply chain performances

We study the impact of inventory replenishment rules on supply chain performances.We propose a real-time inventory replenishment system (SPC) based on control chart.Simulation is adopted to compare SPC with the generalized (R, S) in a supply chain.SPC is superior to (R, S) in terms of bullwhip effect and inventory stability.SPC has lower sensitivity to lead-time than the generalized (R, S). Inventory replenishment rules contribute significantly to the bullwhip effect and inventory instability in supply chains. Smoothing replenishment rules have been suggested as a mitigation solution for the bullwhip effect but dampening the bullwhip effect might increase inventory instability. This paper evaluates a real-time inventory replenishment system denoted as SPC that utilizes a control chart approach to counteract the bullwhip effect whilst achieving competitive inventory stability. The SPC employs two control charts integrated with a set of decision rules to estimate the expected demand and adjust the inventory position, respectively. The first control chart works as a forecasting mechanism and the second control chart is devoted to control the inventory position variation whilst allowing order smoothing. A simulation analysis has been conducted to evaluate and compare SPC with a generalized (R, S) policy in a four-echelon supply chain, under various operational settings in terms of demand process, lead-time and information sharing. The results show that SPC is superior to the traditional (R, S) and comparable to the smoothing one in terms of bullwhip effect, inventory variance, and service level. Further managerial implications have been obtained from the results.

A simulation based game approach for teaching operations management topics

Simulation games have been utilized as an educational tool in order to complement the traditional teaching methods. They have been widely applied in the teaching of different subjects such as business management, nursing, and medicine. This paper proposes a new simulation game which simulates a production system that consists of a set of machines, conveyors, and other components. The objective of the proposed game is to enhance the teaching of some concepts of operations management such as capacity utilization and maintenance planning. The game decisions are repeatedly made in two consecutive steps of playing in order to enhance the learning of students. This framework of decision making can be utilized to evaluate the progression of students learning and the educational effectiveness of the game. Students showed a positive response to the game and learning through gaming in an evaluation conducted after playing the game.

Smoothing inventory decision rules in seasonal supply chains

We study the value of smoothing replenishment rules in seasonal supply chains.Simulation modeling is adopted to compare the traditional and smoothing OUT.The impact of Holt-Winters parameters are studied under both replenishment rules.Smoothing improves the ordering and inventory stability in seasonal supply chains.Increasing the smoothing level reduces the bullwhip effect and inventory variance. A major cause of supply chain deficiencies is the bullwhip effect, which implies that demand variability amplifies as one moves upstream in supply chains. Smoothing inventory decision rules have been recognized as the most powerful approach to counteract the bullwhip effect. Although several studies have evaluated these smoothing rules with respect to several demand processes, focusing mainly on the smoothing order-up-to (OUT) replenishment rule, less attention has been devoted to investigate their effectiveness in seasonal supply chains. This research addresses this gap by investigating the impact of the smoothing OUT on the seasonal supply chain performances. A simulation study has been conducted to evaluate and compare the smoothing OUT with the traditional OUT (no smoothing), both integrated with the Holt-Winters (HW) forecasting method, in a four-echelon supply chain experiences seasonal demand modified by random variation. The results show that the smoothing OUT replenishment rule is superior to the traditional OUT, in terms of the bullwhip effect, inventory variance ratio and average fill rate, especially when the seasonal cycle is small. In addition, the sensitivity analysis reveals that employing the smoothing replenishment rules reduces the impact of the demand parameters and the poor selection of the forecasting parameters on the ordering and inventory stability. Therefore, seasonal supply chain managers are strongly recommended to adopt the smoothing replenishment rules. Further managerial implications have been derived from the results.