Search query: supply chain forecast

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abstract: Abstract The complexity involved in demand forecast for supply chain management of electronics components is exponentially increasing owing to demand fluctuations in consumer electronics, shortening of product life cycles, continuous technology migration, lengthy production cycle time, and long lead time for capacity expansion. While global manufacturing networks often suffer the risks of oversupply and shortage of key components, the distributor that is the key intermediate participator in electronics product supply chain buys components from the suppliers, warehouses them, and resells different parts to a number of electronics manufacturers with vendor managed inventories. Thus, the component distributors forecast the demands for large assortments of stock keeping units (SKUs) with distinct dynamics for inventory control and supply chain management. To address realistic needs to enhance demand forecast performance, this study aims to develop a UNISON data driven analytics framework that integrates machine learning technologies and temporal aggregation mechanism to forecast the demands of intermittent electronics components. An empirical study is conducted in a world leading semiconductor distributor for validation. The results have shown practical vitality of the proposed approach with better performance than conventional approaches and the existing practice. Indeed, the developed solution has been employed in this company to support flexible decisions to empower agile logistics and supply chain resilience for smart production.
author_names: Wenhan Fu

Chen-Fu Chien
title: UNISON data driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution
venue: Comput. Ind. Eng.
year: 2019

abstract: Abstract Policymakers during 1 1 We conducted this research in April June 2020 during the COVID 19 pandemic. The aim is to provide tools for immediate use during the pandemic: desperate times call for desperate academic measures, and as such this is our direct response to inform practice. We employ a phenomenon based research methodological approach, engaging in an early phase of a scientific inquiry, observing, researching, and providing solutions for a developing and novel phenomenon. COVID 19 operate in uncharted 2 2 Opinions expressed are solely my own and do not express the views or opinions of my previous or current employers. territory and must make tough decisions. Operational Research the ubiquitous 'science of better' plays a vital role in supporting this decision making process. To that end, using data from the USA, India, UK, Germany, and Singapore up to mid April 2020, we provide predictive analytics tools for forecasting and planning during a pandemic. We forecast COVID 19 growth rates with statistical, epidemiological, machine and deep learning models, and a new hybrid forecasting method based on nearest neighbors and clustering. We further model and forecast the excess demand for products and services during the pandemic using auxiliary data (google trends) and simulating governmental decisions (lockdown) Our empirical results can immediately help policymakers and planners make better decisions during the ongoing and future pandemics.
author_names: Konstantinos Nikolopoulos

Sushil Punia

Andreas Schafers

Christos Dimitris Tsinopoulos

Chrysovalantis Vasilakis
title: Forecasting and planning during a pandemic: COVID 19 growth rates, supply chain disruptions, and governmental decisions
venue: European Journal of Operational Research
year: 2020

abstract: We address the reservation pricing problem for a two echelon fashion supply chain in which the downstream manufacturer with private information on its operations cost (low or high type) reserves the capacity for a critical component from the upstream supplier before placing the final order. We consider the case when the demand forecast is partially updated. We find that a novel menu of reservation contracts containing the unit reservation fee with reservation quantity and final order could induce the manufacturer to reveal its operations cost information truthfully. We also show that the supplier should require less capacity reservation and charge a lower unit reservation fee if it has asymmetric information about the manufacturer's operations cost. Finally, we analyse the effects of forecast update, and our results indicate that: (i) the supplier benefits from forecast update because the optimal reservation pricing strategy is designed to reveal the true information and meanwhile induce a higher capacity reservation; and (ii) a greater amount of forecast update decreases the supply chain deficit and increases the supplier's agency cost.
author_names: Danqin Yang

Tiaojun Xiao

Tsan-Ming Choi

T C Edwin Cheng
title: Optimal reservation pricing strategy for a fashion supply chain with forecast update and asymmetric cost information
venue: Int. J. Prod. Res.
year: 2018

abstract: We investigate RFID adoption strategies under wholesale price and buy back contracts in a supply chain with one manufacturer and one retailer who faces inventory misplacement and demand forecast error. RFID can alleviate the misplacement problem, and can reduce demand forecast error by shortening order lead time. By a newsvendor model, we characterise the optimal contract terms in the supply chain without and with RFID adoption, respectively. We further analyse how the contract terms depend on RFID related parameters (e.g. salable rate and demand forecast error) We find that both without and with RFID, the wholesale price contract will lead to the double marginalisation problem, while the buy back contract can coordinate the supply chain. We show that the supply chain adopts RFID if and only if the tagging cost is below a threshold; the threshold is in negative correlation to the demand forecast error. The supply chain is more willing to adopt RFID under the buy back contract than under the wholesale price contract. RFID adoption can sometimes lessen the double marginalisation problem under the wholesale price contract, improving the supply chain efficiency. A smaller RFID tagging cost or a reduced forecast error do not necessarily lead to higher supply chain efficiency.
author_names: Lihao Zhang

Tian Li

Tijun Fan
title: Inventory misplacement and demand forecast error in the supply chain: profitable RFID strategies under wholesale and buy back contracts
venue: Int. J. Prod. Res.
year: 2018

abstract: The demand for a particular product or service is typically associated with different uncertainties that can make them volatile and challenging to predict. Demand unpredictability is one of the managers' concerns in the supply chain that can cause large forecasting errors, issues in the upstream supply chain and impose unnecessary costs. We investigate 843 real demand time series with different values of coefficient of variations (CoV) where promotion causes volatility over the entire demand series. In such a case, forecasting demand for different CoV require different models to capture the underlying behavior of demand series and pose significant challenges due to very different and diverse demand behavior. We decompose demand into baseline and promotional demand and propose a hybrid model to forecast demand. Our results indicate that our proposed hybrid model generates robust and accurate forecast across series with different levels of volatilities. We stress the necessity of decomposition for volatile demand series. We also model demand series with a number of well known statistical and machine learning (ML) models to investigate their performance empirically. We found that ARIMA with covariate (ARIMAX) works well to forecast volatile demand series, but exponential smoothing with covariate (ETSX) has a poor performance. Support vector regression (SVR) and dynamic linear regression (DLR) models generate robust forecasts across different categories of demands with different CoV values.
author_names: Mahdi Abolghasemi

Eric J Beh

Garth Tarr

Richard Gerlach
title: Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion
venue: Comput. Ind. Eng.
year: 2020

abstract: Purpose The purpose of this paper is to design a model that can accurately forecast the supply chain sales. Design/methodology/approach This paper proposed a new model based on lightGBM and LSTM to forecast the supply chain sales. In order to verify the accuracy and efficiency of this model, three representative supply chain sales data sets are selected for experiments. Findings The experimental results show that the combined model can forecast supply chain sales with high accuracy, efficiency and interpretability. Practical implications With the rapid development of big data and AI, using big data analysis and algorithm technology to accurately forecast the long term sales of goods will provide the database for the supply chain and key technical support for enterprises to establish supply chain solutions. This paper provides an effective method for supply chain sales forecasting, which can help enterprises to scientifically and reasonably forecast long term commodity sales. Originality/value The proposed model not only inherits the ability of LSTM model to automatically mine high level temporal features, but also has the advantages of lightGBM model, such as high efficiency, strong interpretability, which is suitable for industrial production environment.
author_names: Tingyu Weng

Wenyang Liu

Junjun Xiao
title: Supply chain sales forecasting based on lightGBM and LSTM combination model
venue: Ind. Manag. Data Syst.
year: 2020

abstract: Abstract Demand forecasts are the lifeblood of supply chains. Academic literature and common industry practices indicate that demand forecasts are often subject to human interventions. Judgmental forecasting or judgmental forecast adjustments can cause both positive and negative repercussions to the rest of the supply chain. This paper provides the first systematic literature review of judgmental forecasting and adjustments focusing on key features that impact various decisions in supply chains. A carefully assembled and shortlisted literature pool is analyzed for systematic mapping of the published works using bibliometric tools. The primary sub streams of research within the broader scope of the field are synthesized from a rigorous keyword cluster analysis and a thorough discussion is presented. Our review concludes by encapsulating the key learnings from four decades of academic research in judgmental forecasting and suggests future research avenues to expand our understanding of the role of humans in demand forecasting and supply chain decision making.
author_names: H Niles Perera

Jason Hurley

Behnam Fahimnia

Mohsen Reisi
title: The human factor in supply chain forecasting: A systematic review
venue: Eur. J. Oper. Res.
year: 2019

abstract: Abstract To comply with the stricter emission control regulations of recent years, shipping carriers, who may be risk averse, need to make decisions regarding their investments in environmental sustainability before market uncertainty is realized. The ports, as service providers of the carriers, may help the carriers on making such decisions through sharing market forecast information. In this study, we aim to examine the incentives of forecast information sharing from port and the effect of carrier's risk behavior on such sustainability investment decisions in a maritime supply chain. For this, we propose a two period game theoretic model framework. In this framework, both the port and the carrier face market uncertainty during the first period. The port determines whether to share its market forecast information with the carrier, and the carrier needs to decide her sustainability investment level. Then, in the second period, the port determines the service fee, and the carrier determines the freight rate to the customers. We analyze four different scenarios, based on the types of carrier's risk behavior and on whether the port decides to share the forecast information. We find that the relationship between sustainability investment level, and the port's service fee/the carrier's freight rate can be either positive or negative. We also show that the risk averse behavior reduces the carrier's profit, and lowers the level of sustainability investment, which in turn either positively or negatively influences the carrier's freight rate as before. Interestingly, the risk averse behavior always lowers the port's service fee. Moreover, we find that the forecast sharing from the port can not only improve the profits for both parties, but also facilitate the sustainability investment, even when the carrier is risk averse. We also find that the port always receives a larger percentage of the profit than the carrier.
author_names: Xiaofan Lai

Yi Tao

Fan Wang

Zongbao Zou
title: Sustainability investment in maritime supply chain with risk behavior and information sharing
year: 2019

abstract: Abstract This paper investigates after sale service deployment and information sharing strategies in a supply chain environment including the role of a manufacturer and an independent retailer. The manufacturer first needs to decide whether it is necessary to undertake the after sale service by herself or delegate it to the retailer. Then, the retailer possessing better demand forecast information decides whether to share their information with the manufacturer. When the manufacturer undertakes the after sale service, we find that the retailer prefers to share the information with the manufacturer on the condition of highly cost efficiency for the manufacturer's after sale service. However, we also find that information sharing does not always mean a win win benefit for the whole supply chain partners. When the after sale service is delegated to the retailer, our results show that only when the retailer's after sale service cost efficiency and forecast variability are relatively low, the retailer prefers not to share information. Our analysis also indicates that the manufacturer may delegate the after sale service to the retailer, even if the retailer does not have cost advantage. Compared with the situation where asymmetric information does not exist between the manufacturer and the retailer, the information advantage of the retailer may harm both parties and lead to a "prisoner's dilemma" By examining the value of information forecast, we demonstrate that enhancing the forecast accuracy may be harmful to not only the manufacturer but also the retailer.
author_names: Shuguang Zhang

Bin Dan

Maosen Zhou
title: After sale service deployment and information sharing in a supply chain under demand uncertainty
venue: Eur. J. Oper. Res.
year: 2019

abstract: In this study, the impact of information sharing on bullwhip effect (BWE) is investigated using a four echelon supply chain simulation model where each echelon shares some of the customer demand forecast information with a retailer, the lowest echelon. The level of the demand forecast shared at each echelon is represented as information sharing rate (ISR) Four different levels of ISR are considered to evaluate its impact on BWE. A full factorial design with 64 cases is used, followed by statistical analysis. The results show that (1) overall, higher ISR more significantly reduce BWE than lower ISR at all echelons; (2) further, the impact of ISR is not same between echelons. The ISR at an echelon where BWE is measured has the highest impact. However, its impact decreases at downstream echelons; (3) BWE is affected by not only the magnitude but also the balance of ISR's across echelons, while the former has three times more impact than the latter; (4) lastly, we demonstrate that a highly unbalanced ISR may cause reverse bullwhip effect (RBWE) particularly when the level of unblance at downstream echelons is high and the uppermost echelon where BWE is measured has the highest ISR. Based on this demonstration, we derive a functional relationship between ISR's and RBWE using regression analysis. We believe that results from this study provide useful implications and insights for better coordination and collaboration in a supply chain.
author_names: Ki-Young Jeong

Jae-Dong Hong
title: The impact of information sharing on bullwhip effect reduction in a supply chain
venue: J. Intell. Manuf.
year: 2019