Impact of metric-alignment on supply chain performance: a behavioral study

PurposePrevious research has shown that firms are struggling to incorporate collaboration among supply chain partners. This paper presents a new approach to incorporate collaboration using metric-alignment. The analysis provides key insights regarding the usefulness of this approach to synchronize decision-making that leads to reduced bullwhip effect, less backordering and lower supply chain costs.Design/methodology/approachThis research is based on a large-scale behavioral study comprising 556 participants in multi-echelon supply chain games. Supply chain decisions from these experiments are evaluated to study the impact of metric-alignment on managerial decision-making and the corresponding effects on the overall supply chain performance.FindingsResults show that the metric-alignment approach offers an informal and self-enforced governance mechanism that changes managerial decision-making behaviors and improves supply chain performance. Results also show this approach to yield operational and financial benefits for all supply chain partners in the form of reduced bullwhip effect, less backordering and lower supply chain costs.Originality/valueThis is the first behavioral study of its kind that evaluates a new approach to incorporate collaboration in supply chains using metric-alignment. This approach avoids the shortcomings of current industry practices of using monetary penalties, such as on-time in-full (OTIF) mandates in supply contracts. The study shows that metric-alignment approach can improve overall supply chain performance while offering mutually beneficial rewards for all supply chain partners.

The Impact of Bullwhip Effect on the Cash Flow in Two-Parallel Supply Chain Systems with the Competition Effect

While considering the competition effect and market share, this study discusses how the cash flow bullwhip effect (CFBE) is impacted in two-product and two-parallel supply chain systems by comparing the situation that it has one kind of product in two-level supply chain (SC). Specifically, the study aimed to examine two-product and two-parallel SC systems that include two suppliers and two retailers. Assuming that the demand function is a linear relationship of price self-sensitivity coefficient and price cross-sensitivity coefficient, which is an AR(1) process, two retailers share the demand. After that, the quantitative equation of the CFBE was deduced from two-product and two-parallel SC systems. Finally, we get the condition that the competition effect and the market share increase or decrease the CFBE, which was in contrast to the situation without the competition effect and the market share. The paper suggested that the manager can cooperate with their partner if two products are substitutable. On the other hand, the firm should improve the forecasting accuracy of the customer’s demand and improve the service quality so that it can increase the market share and reduce the CFBE in two-parallel SC systems.

Impact of Price-Sensitive Demand and Premium Payment Scheme on Bullwhip Effect

Bullwhip effect reduces the efficiency, responsiveness, and value of the supply chain. There are some indirect causes like lead time, the number of echelons, and some direct causes of bullwhip effect such as rationing or price variation. Due to capacity constraints, retailers are forced to experience rationing of their demands. Fear of rationing usually gives rise to manipulable demand and hence increases the bullwhip effect. Moreover, if the retailer’s demand is price sensitive then it will cause price variation. The offerings of premium payment by retailers due to unfulfilled demand lure the supplier to extend his existing capacity and to allocate them more supply. In this paper, an attempt has been made to mitigate the impact of the bullwhip effect using a premium payment scheme. A technique has been coined that will help in reducing the bullwhip effect. The increased value of the supply chain on using a premium payment scheme is proof of the reduction of the bullwhip effect. Results are validated through numerical analysis.

Impact of Price Sensitive Demand and Premium Payment Scheme on Bullwhip Effect

Bullwhip effect reduces the efficiency, responsiveness, and value of the supply chain. There are some indirect causes like lead time, the number of echelons, and some direct causes of bullwhip effect such as rationing or price variation. Due to capacity constraints, retailers are forced to experience rationing of their demands. Fear of rationing usually gives rise to manipulable demand and hence increases the bullwhip effect. Moreover, if the retailer’s demand is price sensitive then it will cause price variation. The offerings of premium payment by retailers due to unfulfilled demand lure the supplier to extend his existing capacity and to allocate them more supply. In this paper, an attempt has been made to mitigate the impact of the bullwhip effect using a premium payment scheme. A technique has been coined that will help in reducing the bullwhip effect. The increased value of the supply chain on using a premium payment scheme is proof of the reduction of the bullwhip effect. Results are validated through numerical analysis.

Menu Costs and the Bullwhip Effect: Supply Chain Implications of Dynamic Pricing

Suppose that technology reduces price-adjustment costs (e.g., the costs of printing and changing price tags), and as a result prices at grocery stores change more dynamically. Will this change mean less stability or more stability for grocery supply chains? In other words, will more dynamic pricing downstream mean more last-minute purchases, more overtime work, and more empty space in trucks and warehouses? Or will it mean more regular and more standardized orders, smoother schedules, and less waste? To answer this question, we fit a pricing and ordering model to data from a large Chinese supermarket chain (daily prices, sales, inventories, and shipments from products from seven categories at 78 stores for 3.5 years) and then simulate a counterfactual grocery chain in which the estimated price-adjustment costs are set to zero. We find that the removal of price-adjustment costs stabilizes the supply chain, reducing both its shipment volatility, its sales volatility, and its bullwhip (the difference between the shipment and sales volatility).

Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers

With the development of economic globalization, the uncertainty of supply chains is also increasing, and alleviating the bullwhip effect has become an important issue. From previous discussions on alleviating the bullwhip effect, there was no research on alleviating it by enhancing supply chain agility through improving big data. Moreover, it has not been found that quality function deployment is used to analyze the interdependence between big data and supply chain agility, as well as between supply chain agility and the bullwhip effect. In particular, the interaction of bullwhip effect factors are not considered. In this study, the multicriteria decision-making integrated framework is proposed and the largest relay manufacturer in China is taken to identify key big data enablers to enhance supply chain agility and mitigate the bullwhip effect, thus providing an effective method for electronic equipment manufacturing enterprises to develop a supply chain that can quickly respond to changes and uncertainties. These big data enablers can enhance supply chain agility and reduce the bullwhip effect. This framework provides an effective method for electronic manufacturers to formulate supply chain agility indicators and big data enablers to mitigate the bullwhip effect and also provides a reference for other manufacturing enterprises in supply chain management.

Pricing strategy and product substitution of bullwhip effect in dual parallel supply chain: aggravation or mitigation?

The bullwhip effect (BE) affects not only the revenue of the retailer but also the revenue of the manufacture. Thus, a lot of retailers and manufacturers aim to attenuate the negative impact of the BE. In this research, two parallel supply chains distributing two substitutable products with price-sensitive demands are considered, the order-up-to inventory policy, as well as the MMSE forecasting method, are employed by retailers in these chains. The retailer’s price-setting follows the first-order vector autoregressive process, suggesting that its pricing decision depends on its previous price as well as its rival’s price, owing to the BE. The analytical expression of the BE is calculated by the statistical method. Besides, the effects of pricing strategy and product substitution on the BE are studied through simulation. A conclusion can be drawn that the BE of the two parallel supply chains will be affected by lead time, product substitution rate, and pricing coefficient. Of particular interest is that the BE can be efficiently alleviated by adopting a price strategy with many correlations and a small coefficient of autocorrelation.

Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development

Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce enterprise demand forecasting method based on Horizontal Federated Learning and ConvLSTM, from the perspective of sustainable development. First, in view of the shortcomings of traditional RNN and LSTM demand forecasting models, which cannot handle multi-dimensional time-series problems, we propose a demand forecasting model based on ConvLSTM. Secondly, to address the problem that data cannot be directly shared and exchanged between e-commerce enterprises of the same type, the goal of demand information sharing modeling is realized indirectly through Horizontal Federated Learning. Experimental results on a large number of real data sets show that, compared with benchmark experiments, our proposed method can improve the accuracy of e-commerce enterprise demand forecasting models while avoiding privacy data leakage, and the bullwhip effect value is closer to 1. Therefore, we effectively alleviate the bullwhip effect of the entire supply chain system in demand forecasting, and promote the sustainable development of e-commerce companies.

Unraveling Behavioral Ordering: Relative Costs and the Bullwhip Effect

Problem definition: We study the bullwhip effect and analyze the impact of human behavior. We separate rational ordering in response to increasing incoming orders from irrational ordering. Academic/practical relevance: Prior research has shown that the bullwhip effect occurs in about two-thirds of firms and impacts profitability by 10%–30%. Most bullwhip mitigation efforts emphasize processes such as information sharing, collaboration, and coordination. Previous work has not been able to separate the impact of behavioral ordering from rational increases in order quantities. Methodology: Using data from a laboratory experiment, we estimate behavioral parameters from three ordering models. We use a simulation to evaluate the cost impact of bullwhip behavior on the supply chain and by echelon. Results: We find that cost increases are not equally shared. Human biases (behavioral ordering) at the retailer results in higher relative costs elsewhere in the supply chain, even as similar ordering by a wholesaler, distributor, or factory results in increased costs within that echelon. These results are consistent regardless of the behavioral models that we consider. The cognitive profile of the decision maker impacts both echelon and supply chain costs. We show that the cost impact is higher as more decision makers enter a supply chain. Managerial implications: The cost of behavioral ordering is not consistent across the supply chain. Managers can use the estimation/simulation framework to analyze the impact of human behavior in their supply chains and evaluate improvement efforts such as coordination or information sharing. Our results show that behavioral ordering by a retailer has an out-sized impact on supply chain costs, which suggests that upstream echelons are better placed to make forecasting and replenishment decisions.