An extension to fuzzy estimations and system dynamics for improving supply chains

Campuzano, Mula, and Peidro (“Fuzzy estimations and system dynamics for improving supply chains.” Fuzzy Sets and Systems 161: 1530–1542) evaluate the behaviour of fuzzy estimations of demand instead of exponential smoothing for demand forecasts in a two-level (manufacturer and end customer) supply chain and demonstrate how the bullwhip effect and the amplification of inventory variance (NSAmp) can be effectively reduced. In this note, we extend and test the previous model to a three-level supply chain which consists of an end consumer, a retailer and a manufacturer. Here, the model is tested by using both Gaussian and autoregressive demand patterns. We show that the bullwhip effect and NSAmp also reduce at the level where fuzzy orders exist with good fill rate values.     

Dynamic analysis and design of a semiconductor supply chain: a control engineering approach

The combined make-to-stock and make-to-order (MTS-MTO) supply chain is well-recognised in the semiconductor industry in order to find a competitive balance between agility, including customer responsiveness and minimum reasonable inventory, to achieve cost efficiency while maintaining customer service levels. Such a hybrid MTS-MTO supply chain may suffer from the bullwhip effect, but few researchers have attempted to understand the dynamic properties of such a hybrid system. We utilise a model of the Intel supply chain to analytically explore the underlying mechanisms of bullwhip generation and compare its dynamic performance to the well-known Inventory and Order-Based Production Control System (IOBPCS) archetype. Adopting a control engineering approach, we find that the feedforward forecasting compensation in the MTO element plays a major role in the degree of bullwhip and the Customer Order Decoupling Point (CODP) profoundly impacts both the bullwhip effect and the inventory variance in the MTS part. Thus, managers should carefully tune the CODP inventory correction and balance the benefit between CODP inventory and bullwhip costs in hybrid MTS-MTO supply chains.     

Underestimating the bullwhip effect: a simulation study of the decomposability assumption

We investigate the assumption of decomposability as it pertains to modelling the bullwhip effect in multi-stage supply chains. Decomposing a multi-stage supply chain into a set of node pairs, each of which can be efficiently represented with a two-stage model, is a common modelling technique when analysing the bullwhip effect in supply chains. This approach depends on the validity of the decomposability assumption since most supply chains are coupled systems that are a logical fit for singular, or ‘monolithic’, multi-stage models. We utilise a simulation study to compare decomposition-based supply-chain models with monolithic models and determine if decomposition modelling significantly alters the predicted severity of the bullwhip effect. We find decomposition-based models exhibit a significantly lower level of bullwhip effect than monolithic models of the same supply chain. The systematic underestimation of the bullwhip effect by decomposition-based models indicates that the assumption of decomposability is flawed. Our study also confirms previous work showing the significant benefit of using actual, instead of approximate, lead-time demand information. We discuss implications for supply-chain modelling, supply-chain design, and data collection.     

Effective bullwhip metrics for multi-echelon distribution systems under order batching policies with cyclic demand

A large number of problems in a distribution supply chain require that decisions are made in the presence of the bullwhip effect phenomenon. The impact of the order batching policies on the bullwhip effect is analysed in this paper, when cycle demand on a multi-echelon supply chain operating is considered. While investigating which bullwhip effect metrics are more adequate to measure the bullwhip effect in these type of systems, the optimal reordering plan that minimises the operation costs of the overall system is calculated. A Mixed Integer Linear Programming (MILP) model is developed that takes into account an inventory and distribution system formed by multiple warehouses and retailers with lateral transshipments. The bullwhip effect is measured through four metrics: the echelon average inventory; the echelon inventory variance ratio; the echelon average order; and the echelon order rate variance ratio. As conclusion the inventory metrics suggest that (i) using batching policy reduces instability; (ii) batching may reduce in general order variance if using larger batches and (iii) cycle demand length has no major impact in the bullwhip effect. A motivational example and a real word case study are used and tested.     

Two-stage make-to-order remanufacturing with service-level constraints

We consider a firm that meets demand for an order with remanufactured products, new products or a mix of both. There are constraints on the service level. We use a stylized two-stage GI/G/ 1 queuing network model to study the problem. The first stage is unique to each product, whereas remanufactured and new products share the second stage. The objective was to find the optimal, long run production mix that maximizes profit subject to a service-level constraint that restricts the average order lead-time. There is yield in the remanufacturing process, where yield is the per cent of returned used products that result in a good part after remanufacturing. In the analytic model, we make the simplifying assumption that the producer always gets enough return of used products to meet its remanufacturing needs in the production mix. However, we relax this assumption in a simulation. We find that the optimal solution is generally non-trivial, i.e. the firm generally uses a mix of remanufactured and new products to meet demand. When the new product is less profitable than the remanufactured product, then it is optimal to remanufacture 100%, provided that there is enough supply of used products. When the new product is more profitable, however, the proportion of remanufacturing increases as service level increases. We use simulation to test the robustness of the analytic model by including complexities such as stochastic product returns and stochastic production yield.     

The effect of categorizing returned products in remanufacturing

An increasing number of companies have been implementing comprehensive recycling and remanufacturing programs. These endeavors typically involve the operation of joint manufacturing and remanufacturing systems. One of the major challenges in managing such hybrid systems is the stochastic nature of product returns. In particular, there is significant variability in the condition of the returns. This paper presents an approach for assessing the impact of quality-based categorization of returned products. Through extensive numerical studies on a continuous-time Markov chain model, we show that incorporation of returned product quality in the remanufacturing and disposal decisions can lead to significant cost savings. We find that these savings are amplified as the return quality decreases, and as the return rate increases. We also show that prioritizing higher quality returns in remanufacturing is, in general, a better strategy.     

A control engineering approach to the assessment of supply chain resilience

There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and nonlinear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.     

Joint hybrid repair and remanufacturing systems and supply control

The control of a stochastic manufacturing system that executes capital asset repairs and remanufacturing in an integrated system is examined. The remanufacturing resources respond to planned returns of worn-out equipment at the end of their expected life and unplanned returns triggered by major equipment failures. Remanufacturing operations for planned demand can be executed at different rates and costs corresponding to different replacement and repair modes. The replacement components inventory is provided by an upstream supply with random lead times. The objective is to determine a control policy for both the supply and remanufacturing activities that minimises the average repair/replacement, acquisition and inventory/shortage total cost over an infinite horizon. We propose a suboptimal joint remanufacturing and supply control policy, composed of a multi-hedging point policy (MHPP) for the remanufacturing stage and an (s, Q) policy for the replacement parts supply. The MHPP is based on two inventory thresholds that trigger the use of predefined remanufacturing modes. Control policy parameters are obtained combining analytical modelling, simulation experiments and response surface methodology. The effects of the distribution, mean and variability of the lead time are tested and a sensitivity analysis of cost parameters is conducted to validate the proposed control policy. We also show that our policy leads to a significant cost reduction as compared to a combination of a hedging point policy (HPP) and an (s, Q) policy.     

Inventory performance of some supply chain inventory policies under impulse demands

This paper attempts to study the impact of impulsive demand disturbances on the inventory-based performance of some inventory control policies. The supply chain is modelled as a network of autonomous supply chain nodes. The customer places a constant demand except for a brief period of sudden and steep change in demand (called demand impulse). Under this setting, the behaviour of each inventory policy is analysed for inventory performance of each node. It is found that the independent decision-making by each node leads to a bullwhip effect in the supply chain whereby demand information is amplified and distorted. However, under a scenario where the retailer places a constant order irrespective of the end customer demand, the inventory variance was actually found to decrease along the supply chain. The variance of the inventory remained constant along the chain when only the actual demands are transmitted by each node. The results also showed that the inventory policy which is best for one supply chain node is generally less efficient from a supply chain perspective. Moreover, the policy which performs poorly for one node can be most efficient for the supply chain. In a way, our results also provide a case for coordinated inventory management in the supply chain where all members prepare a joint inventory management policy that is beneficial for all the supply chain nodes. The results have significant industrial implications.     

Lead-time setting, capacity utilization, and pricing decisions under lead-time dependent demand

This research examines the lead-time setting, capacity utilization, and pricing decisions facing a firm serving customers that are sensitive to quoted lead-times. We model the firm's operations as an M/M/1 queue and treat the demand as being linear in price and quoted lead-time. We analyze the quoted lead-time, capacity utilization, and price that maximize revenues less total variable production costs, WIP holding costs, and lateness penalty costs. We use this analysis to show that the capacity utilization should be lower when (1) customers are more sensitive to lead-times and/or (2) the firm incurs higher congestion related costs and/or (3) the penalty for lateness is higher. Further, we study the robustness of optimal profit contributions when the model parameters are mis-estimated.     

The bullwhip effect on inventory under different information sharing settings based on price-sensitive demand

Information sharing (IS) is proved to be a valid method to counter demand variability amplification along the supply chain, or bullwhip effect (BWE). Different from the traditional way of measuring the BWE based on order quantity, we measure the BWE on inventory in different IS settings and try to find the best IS approach. In this paper, the retailer will face the market demand which is price-sensitive, and the price follows a first-order autoregressive process. This demand model includes some indexes that can provide more useful managerial insights than previously studied parameters. Our study identifies the best IS setting under any conditions, and clarifies that the benefits of IS will be evident when the overall market product pricing process is highly correlated over time, the demand shocks to the retailer are high, the price sensitivity coefficient is small, the overall market shocks are low, the retailer’s lead-time is long and the manufacturer’s lead-time is short.     

On the Bullwhip Avoidance Phase: supply chain collaboration and order smoothing

This paper provides an assessment of the impact of collaboration and smoothing replenishment rules on supply chain operational performance and customer service level. Three supply chain configurations (i.e. Traditional, Information Exchange and Synchronised) in which orders are generated by smoothing (S, R) inventory control policies are studied for different proportional controllers. A supply chain stress test is performed through a sudden and intense change in demand. A structured and extended supply chain assessment framework is adopted. The main conclusions of this paper are the following. (i) The impact of Supply Chain Collaboration on overall supply chain performance is greater than that of order smoothing. Order smoothing mitigates the bullwhip effect, but it may have a negative impact on customer service. Supply Chain Collaboration mitigates the bullwhip effect, provides inventory stability, limits lumpy orders and enhances customer service level. (ii) The negative effect on customer service level of order smoothing is almost eliminated in synchronised supply chains.     

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems.     

A decision support framework for global supply chain modelling: an assessment of the impact of demand,supply and lead-time uncertainties on performance

We developed a decision support framework for a global manufacturer of specialty chemicals to study the relative impact of demand, supply and lead-time uncertainties on cost and customer service performance. Our approach combines optimisation and simulation methodologies as follows: mathematical models provide optimal plans via a novel approach to the supply chain planning mechanism of the Company. Simulation models execute the supply chain plans so as to allow the examination of the outcomes under the various sources of uncertainty. The iterative use of optimisation and simulation methodologies allows the user the benefit of obtaining optimal solutions while revealing the impact of uncertainties on system performance. Our results indicate that demand uncertainty has the greatest negative impact on performance for the supply chain that we modelled in this study, emphasising the importance of effective forecasting. The relative importance of supply and lead-time uncertainties varies according to the performance measures. While our results are valid for the specific supply chain and the operating environment we modelled, our study emphasises the importance of the ability to model supply chains realistically to obtain valid and useful results.     

The bullwhip effect on product orders and inventory: a perspective of demand forecasting techniques

Demand forecasting is one of the key causes of the bullwhip effect on product orders. Although this aspect of order oscillation is not ignored, the current study focuses on another critical aspect of oscillation: the bullwhip effect on inventory, i.e. the net inventory variance amplification. In particular, this paper studies a two-level supply chain in which the demand is price sensitive, while the price follows a first-order autoregressive pricing process. We derive the analytical expressions of the bullwhip effect on product orders and inventory using minimum mean-squared error, moving average and exponential smoothing forecasting techniques. We also propose the conditions under which the three forecasting techniques would be chosen by the retailer to minimise the sum of the bullwhip effect on product orders and inventory under different weightings. These observations are used to develop managerial insights regarding choosing an appropriate forecasting technique after considering certain distinct characteristics of the product.     

Optimal maintenance plan for two-level assembly system and risk study of machine failure

This paper deals with the optimisation of two-levels assembly system planning. This system is composed of a single machine, inventories at levels 1 and 2 for stock keeping components to assembly and final assembled product. It assumed that the machine processes all assembly operations and is subject to random failure. A mathematical model is developed to incorporate a supply planning for two-level assembly systems under stochastic lead times and breakdowns machine. A preventive maintenance plan is carried out to reduce the frequency of the corrective maintenance actions. This work has double goals. The first one is to find the optimal order release dates for the different components at level 2 and the optimal preventive maintenance plan. The second one is to quantify the risk due to machine failures which have an impact on the lead-time of the finished product. To consider the maintenance actions, preventive maintenance actions are perfectly performed to restore the machine to state “as good as new”, minimal repair is considered at failure. The model minimises the total cost, which is the sum of inventory holding cost for components at levels 1 and 2, backlogging and inventory holding cost for the finished products and maintenance costs. To illustrate the effectiveness of the proposed model, different meta-heuristics are applied; the genetic algorithm shows the most suited to our analytical model, the optimal release date founded by this algorithm allows finding the optimal preventive maintenance plan. The obtained optimal maintenance planning is used in the risk assessment in order to find the threshold repair period that avoids lost profit.     

Return policy and supply chain coordination with network-externality effect

The benefits of a consumer return policy have been extensively studied in extent literature. This paper explores the potentially damaging impact of a return policy on the retailer. We develop an analytical framework and examine the economic impact of consumer return among consumers, retailer and supply chain. We distinguish three network-externality (NE) cases: no network externality, fixed network-externality and variable network-externality contingent on return amount to discuss the retailer’s selling price, refund and inventory policies. Our analysis derives the optimal policies and shows that they take the form of contingence model in which the policies depend on consumer initial return and NE return. We also examine the influence of the consumer return NE effect on buy-back contracts of the supply chain and show that while the traditional buy-back contract fails to coordinate the supply chain, the NE effect does not render the differentiated buy-back contract less effective. Finally, we extend our study to a heterogeneous consumer case.     

Assessing the economic and environmental impact of remanufacturing: a decision support tool for OEM suppliers

The circular economy is often presented as a solution for companies to increase the sustainability of their business. In many situations where suppliers produce subassemblies or modules for OEMs in a B2B context, dependency on their clients limits their options for profitable closed-loop supply chains. In this paper, we develop a simple tool suppliers can use to quickly assess whether remanufacturing is economic and environmentally attractive compared to producing new components. We derive optimal acquisition and reuse quantities that minimise total costs. Based on our analysis with a supplier in the automotive industry, we find that used core prices and remanufacturing yield rates have a large impact while an optimised design for remanufacturing can only marginally improve the situation. The tool is applicable to a wide variety of suppliers and industries that remanufacture their modules or subassemblies, or are exploring the option to engage in remanufacturing operations.     

Optimal configuration of remanufacturing supply network with return quality decision

This research studies the configuration problem of a remanufacturing production network together with the decision for return quality thresholds, in which, the manufacturer has multiple remanufacturing facilities to satisfy different market demands. Quality of returns is stochastic, while demand for remanufactured products is either stochastic or deterministic. The problem we considered is to determine facilities to operate, minimum quality to accept into each operating facility, return quantity and demand allocation simultaneously so that the system’s profit is maximised. The problem is formulated as a mixed integer non-linear programming model. Through the use of a numerical example, the impact of quantity of returns, total spending, quality uncertainty, demand uncertainty and transportation cost on the remanufacturing system is analysed.     

Setting procurement safety lead-times for assembly systems

This paper describes a practical method for setting safety lead-times for purchased components in assembly systems with uncertainty in the supply process. The approach is specifically designed to be used in an MRP procurement environment. A two-stage production model in which suppliers were uncapacitated with stochastic lead times was developed. Then, a combinatorial optimization method that took advantage of structural properties was developed to produce an optimal integer solution of safety lead-times. In addition to yielding a practical lead-time setting tool, the model led to some interesting policy insights: (1) the flexibility achieved by bringing parts in early may have value even in situations with ideal suppliers; (2) purchasing components in batches offers some of the same protection as does safety lead time, but is less precise and subsequently more expensive; and (3) ignoring the complicating issues of MRP procurement results in far from optimal results.