Revisiting rescheduling: MRP nervousness and the bullwhip effect

We study the material requirement planning (MRP) system nervousness problem from a dynamic, stochastic and economic perspective in a two-echelon supply chain under first order auto-regressive demand. MRP nervousness is an effect where the future order forecasts, given to suppliers so that they may plan production and organize their affairs, exhibits extreme period-to-period variability. We develop a measure of nervousness that weights future forecast errors geometrically over time. Near-term forecast errors are weighted higher than distant forecast errors. Focusing on replenishment policies for high volume items, we investigate two methods of generating order call-offs and two methods of creating order forecasts. For order call-offs, we consider the traditional order-up-to (OUT) policy and the proportional OUT policy (POUT). For order forecasts, we study both minimum mean square error (MMSE) forecasts of the demand process and MMSE forecasts coupled with a procedure that accounts for the known future influence of the POUT policy. We show that when retailers use the POUT policy and account for its predictable future behavior, they can reduce the bullwhip effect, supply chain inventory costs and the manufacturer’s MRP nervousness

Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy

We study the Damped Trend forecasting method and its bullwhip generating behaviour when used within the Order-Up-To (OUT) replenishment policy. Using z-transform transfer functions we determine complete stability criteria for the Damped Trend forecasting method. We show that this forecasting mechanism is stable for a much larger proportion of the parametrical space than is generally acknowledged in the literature. We provide a new proof to the known fact that the Naïve, Exponential Smoothing and Holts Method forecasting, when used inside the OUT policy, will always generate bullwhip for every possible demand process, for any lead-time. Further, we demonstrate the Damped Trend OUT system behaves differently. Sometimes it will generate bullwhip and sometimes it will not. Bullwhip avoidance occurs when demand is dominated by low frequency harmonics in some instances. In other instances bullwhip avoidance happens when demand is dominated by high frequency harmonics. We derive sufficient conditions for when bullwhip will definitely be generated and necessary conditions for when bullwhip may be avoided. We verify our analytical findings with a numerical investigation

A systems dynamics perspective of forecasting in supply chains

Purpose: To evaluate the impact of forecasting on supply chain via a system dynamics perspective. Method/approach: Techniques from Control Theory (such as block diagram, z-transforms, Fourier transforms, Jury’s Inners approach, and frequency response analysis) and Time Series Analysis are used to investigate the performance of supply chains analytically. Simulation is also used to verify the results. Findings: This thesis provides a new and complete proof to the knowledge that Naïve, simple exponential smoothing, and Holt’s forecasting when used in the Order-Up-To (OUT) policy always produce the bullwhip effect for any demand pattern and for all lead-times. In terms of the bullwhip performance when Damped Trend (DT) forecasts are used in the OUT policy, the bullwhip effect is always generated for traditional parameter suggestions. However, the bullwhip avoidance behaviour occurs for some unconventional parameter values. Using these unconventional parameter values, the DT / OUT system acts like a low-pass filter that can eliminate the bullwhip effect and maintain good inventory performance at the same time. The thesis also proves that the Proportional Order-Up-To (POUT) policy is able to reduce system nervousness at the manufacturer. Moreover, the proportional future guidance (PFG) mechanism proposed may reduce system nervousness and inventory costs at the manufacturer and reduce the bullwhip effect in the supply chain simultaneously. Implications: This thesis shows that the bullwhip and net stock variance reduction behaviours exist when unconventional parameter values are used in the DT forecasting procedure. It is the first evidence that it is possible to design a system with good financial performance but without directly looking into the performance of forecasting. The thesis is also the first to consider the MRP nervousness problem and the bullwhip effect at the same time. The PFG method proposed is easy to understand, and since it does not require sophisticated integrated IT systems, or demand / inventory information sharing, it should be easy to implement

Inventory performance of the damped trend forecasting method

The Damped Trend (DT) forecasting method has been recognized for its superior accuracy. Li et al., (2014) show when DT forecasts are used within the order-up-to (OUT) policy, the bullwhip effect is avoided by using unconventional DT parameter settings. We extend this study in three directions. First, by investigating the relationship between the stability and invertibility, we show that stable DT parameter sets produce feasible forecasts. This further justifies the choice of unconventional DT parameter values. Second, we extend the bullwhip analysis from a lead-time of one to a general lead-time, identifying a stable and invertible region in the parameter space that possesses enviable bullwhip avoidance behavior. Third, we characterize the frequency response of the inventory levels maintained by the OUT policy with DT forecasts. For independently and identically distributed (i.i.d.) demand the net stock amplification ratio can be close to (but never smaller than) the lead-time and review period, with the intriguing benefit of bullwhip avoidance. For other demand patterns, the net stock amplification can be less than the i.i.d. lower bound. The bullwhip can also be reduced at the same time. Simulations of 62 sets of real demand time series verify our analytical results

Damped trend forecasting and the order-up-to replenishment policy

We develop a z-transform transfer function model of the Damped Trend forecasting mechanism from which we determine its stability boundary. We show that the Damped Trend forecasting mechanism is stable for a much larger proportion of the parametrical space than is currently acknowledged in the literature. We incorporate the Damped Trend forecasting mechanism into an Order-Up-To (OUT) replenishment policy and investigate the frequency response of this system. We prove that Naïve, Exponential Smoothing and Holts forecasts, when used within the OUT policy, will always generate bullwhip, for every possible demand process, for any lead-time. However, the Damped Trend forecasting mechanism, when used within the OUT policy, behaves differently. Sometimes it will generate bullwhip and sometimes it will not. Bullwhip avoidance occurs when demand is dominated by low frequencies in some instances. In other instances bullwhip avoidance happens at high frequencies. We are also able to demonstrate a complex odd-even lead-time effect exists. Bullwhip may be avoided when the lead-time is odd for a particular demand pattern, but re-appears when the lead-time changes to an even number

The influence of online review adoption on the profitability of capacitated supply chains

The paper explores the influence of online review adoption on supply chain profitability under the presence of a capacity constraint. Nowadays, customers increasingly rely on online reviews for decision making, and online retailers regard reviews as a norm. Although online reviews have been extensively examined in marketing disciplines, little research has been conducted to investigate their influence from a supply chain perspective. In addition, previous research has largely focused on how online review information can influence customer purchase behaviours, but ignores the more basic decision: whether and when companies should adopt reviews. This paper examines the online review adoption decision from a capacitated supply chain perspective through mathematical modelling and simulation. The simulation considers the influence of variables including online review adoption decision, capacity constraint level, lost sales penalty level, and product quality estimation on supply chain profitability. Generally, we find that online reviews can bring more profit to the supply chain than without online reviews, although such influence is moderated by the other three variables. The findings reveal the complexity of the contextual variable impacts on online review adoption, and demonstrate that decisions concerning the adoption of online reviews should take all supply-chain-related variables into consideration rather than only aiming for increasing customer orders