A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

Cheng, Feng; Ettl, Markus; Lu, Yingdong; Yao, David D.

doi:10.1111/j.1937-5956.2011.01303.x

Cited by 21 publications

(6 citation statements)

References 31 publications

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“…Chou et al (2007) found that although reactively adjusting the capacity according to a demand forecast was the dominant alternative, the conservative strategy -which entails making capacity decisions only after the organisation is running at full capacity as a consequence of an actual increase in demand -worked better in some circumstances. Cheng et al (2012) proposed a production-inventory model for manufacturing systems with capacity constraints.…”

Section: Literature Review and Our Contributionmentioning

confidence: 99%

Exploring nonlinear supply chains: the dynamics of capacity constraints

Ponte

Wang

García

et al. 2016

International Journal of Production Research

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While most supply chain models assume linearity, real production and distribution systems often operate in constrained contexts. This article aims to analyse the consequences of capacity limits in the order-up-to replenishment policy with minimum mean squared error forecasting under independently and identically distributed random demand. Our study shows that the impact of this nonlinearity is often significant and should not be ignored. In this regard, we introduce the concept of a settling capacity, which informs when our knowledge from a linear analysis is a reasonable approximation in a nonlinear context. If the available capacity is less than the settling capacity, the nonlinear effects can have a significant impact. We compare the Bullwhip Effect and Fill Rate in constrained contexts to well-established results for linear supply chains. We reveal the capacity limit acts as a production smoothing mechanism, at the expense of increasing inventory variability. We proceed to analyse the economic consequences of the capacity constraint and show that it can actually reduce costs. We provide an approximate solution for determining the optimal capacity depending on the demand, the unit costs, and the lead time.

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Section: Literature Review and Our Contributionmentioning

confidence: 99%

Exploring nonlinear supply chains: the dynamics of capacity constraints

Ponte

Wang

García

et al. 2016

International Journal of Production Research

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“…This situation is plausible in pull production systems or when the production lead time is short. For instance, IBM orders some of its required components after realization of demand to customize the products based on customers' orders (Cheng, Ettl, Lu, & Yao, 2012). Thus, the ordering decision of the buyer will be postponed until after both players have decided on their investments.…”

Section: Extensionsmentioning

confidence: 99%

Investment Strategies in Supplier Development under Capacity and Demand Uncertainty

Golmohammadi

Hassini

2020

Decision Sciences

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We study joint investment by a buyer and a supplier in improving the supplier's capacity using a Stackelberg game model. We analyze both buyer‐led and supplier‐led situations. We show that in both cases the players have an opportunistic behavior toward investment. In the buyer‐led game, when the buyer finds the supplier motivated enough to invest, he avoids any direct contribution on capacity improvement. In this situation, the buyer follows an order inflation strategy to increase the investment of the supplier. However, when the supplier does not show the desire to make enough investment, the buyer will engage in direct investment in the supplier's capacity. We showed that although the order inflation strategy increases the buyer's optimal order quantity, it does not coordinate the supply chain. Also, in the case that the buyer is forced to share the investment costs with the supplier, he relies less on order inflation strategy. In the supplier‐led game, we demonstrated that the buyer has no motivation to use order inflation strategy. In the case that the supplier is the only investor, the buyer‐led game results in a higher profit for the buyer, supplier, and the supply chain. Finally, we looked at two extensions where the supplier is penalized for unsatisfied demand and the buyer uses an order‐postponement strategy.

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“…The authors formulate the model into a non-linear optimization program to find the optimal values of the planning variables. Cheng, Ettl, Lu, and Yao (2012) consider a push-pull production system with a two-stage manufacturing process. The authors develop a nonlinear optimization model to examine the tradeoff between capacity utilization and inventory cost reduction.…”

Section: Literature Reviewmentioning

confidence: 99%