TY - JOUR
T1 - Mathematical modelling of products allocation to customers for semiconductor supply chain
AU - Mousavi, Behrouz Alizadeh
AU - Azzouz, Radhia
AU - Heavey, Cathal
N1 - Publisher Copyright:
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
PY - 2019
Y1 - 2019
N2 - Where demand outstrips supply, there will result in shortages to end customers. In such a case decisions need to be made of how to allocate supply to customers. Customer satisfaction requires accurate order promising that leads to better cooperation, as well as trustable orders and forecasts from customers. As a result, customer satisfaction through a trustable promising system leads to more accurate planning for production. In this regard, modern Advanced Planning Systems (APS) provides allocation planning to customers' orders based on “Available To Promise” (ATP). Lack of supply, escalation, and excess demand are propelled by competitive plant capacity, dynamic behaviours of ATP, orders, and demand forecasts in demanding industries like semiconductor manufacturing. When demand exceeds supply, APS needs the support of experts (human intervention) about the time and amount to be allocated to customers. This feature of APS keeps the flexibility of planning to find feasible optimal decisions regarding allocations. In this paper, we propose a mathematical model for the optimization of ATP allocation to customers, where demand exceeds supply, which will be presented as a decision support tool to analyse allocation scenarios. The objective of the proposed mathematical model is maximizing customer service level which is directly related to customer satisfaction while keeping a maximum of stock. The model is being developed from a case study of a European semiconductor supply chain with a sales office in Ireland. In this case study, support will be provided to allocation managers that allows flexibility solutions to be developed. The obtained results have validated the proposed multi-objective mathematical model.
AB - Where demand outstrips supply, there will result in shortages to end customers. In such a case decisions need to be made of how to allocate supply to customers. Customer satisfaction requires accurate order promising that leads to better cooperation, as well as trustable orders and forecasts from customers. As a result, customer satisfaction through a trustable promising system leads to more accurate planning for production. In this regard, modern Advanced Planning Systems (APS) provides allocation planning to customers' orders based on “Available To Promise” (ATP). Lack of supply, escalation, and excess demand are propelled by competitive plant capacity, dynamic behaviours of ATP, orders, and demand forecasts in demanding industries like semiconductor manufacturing. When demand exceeds supply, APS needs the support of experts (human intervention) about the time and amount to be allocated to customers. This feature of APS keeps the flexibility of planning to find feasible optimal decisions regarding allocations. In this paper, we propose a mathematical model for the optimization of ATP allocation to customers, where demand exceeds supply, which will be presented as a decision support tool to analyse allocation scenarios. The objective of the proposed mathematical model is maximizing customer service level which is directly related to customer satisfaction while keeping a maximum of stock. The model is being developed from a case study of a European semiconductor supply chain with a sales office in Ireland. In this case study, support will be provided to allocation managers that allows flexibility solutions to be developed. The obtained results have validated the proposed multi-objective mathematical model.
KW - Allocation Planning
KW - Customer Satisfaction
KW - Industry 4.0
KW - Optimization
KW - Order Management
UR - http://www.scopus.com/inward/record.url?scp=85083531904&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.01.190
DO - 10.1016/j.promfg.2020.01.190
M3 - Conference article
AN - SCOPUS:85083531904
SN - 2351-9789
VL - 38
SP - 1042
EP - 1049
JO - Procedia Manufacturing
JF - Procedia Manufacturing
T2 - 29th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2019
Y2 - 24 June 2019 through 28 June 2019
ER -