Base Stock Model Research Articles

Modeling Payment Timing in Multiechelon Inventory Systems with Applications to Supply Chain Coordination

Problem definition: How can one adapt multiechelon inventory models to capture the cash flows generated by various payment-timing contracts? What competitive inventory policy behavior arises under these various payment-timing arrangements? Academic/practical relevance: Technology advancements are increasing the variety of payment triggers between supply chain stages. However, traditional multiechelon inventory models, which were originally developed for vertically integrated systems, do not explicitly account for payments flowing up the supply chain nor issues of payment timing between stages. Methodology: We introduce an analytical modeling methodology to incorporate financial inventory costs generated by payment-timing arrangements between stages in multiechelon inventory models. We also combine these methods with established inventory theory to study competitive inventory policies in a two-echelon system. Results: Under a class of payment-timing contracts, we show how to express payment flows at each stage in terms of standard physical inventory metrics and the demand-arrival process. We also show how to calculate the average inventory costs for each stage under given inventory policies and payment-timing contracts. In the two-echelon base-stock model, we first show that the cost of decentralization under standard wholesale price contracts is significantly driven by too much inventory at the supplier; it is not exclusively driven by too little inventory at the retailer as in the selling-to-the-newsvendor literature. We show that wholesale price contracts with a type of popular consignment payment timing still leads to too little inventory at the retailer. However, we prove there exists a wholesale price contract with partial consignment timing that can achieve the centralized inventory levels at both the supplier and the retailer. Managerial implications: Researchers can leverage our methodology to incorporate the price transfers and timing aspects of contracts in multiechelon inventory models. Our insights also help managers better understand the impact of prices and payment timing on decentralized chain behavior and performance.

An option mechanism to coordinate a dyadic supply chain bilaterally in a multi-period setting

In today's global environment competition is no longer limited to companies, but has evolved to supply chains seeking to coordinate ordering and production when faced with challenges such as random yield, lead time uncertainty and stochastic demand. This paper presents an algorithm that combines the base stock model and the option mechanism to address that key problem on how to coordinate ordering and production bilaterally in a dyadic supply chain in a multi-period setting. A numerical study that uses fictive data based on real quantitative data from a multinational manufacturer and its preferred supplier are included to illustrate the effects of the algorithm and show how two decentralised companies can each maximise profits (compared to a wholesale contract) while reaching the optimal centralised system level. The manuscript contributes by extending existing literature on supply chain management and finance to coordinate ordering and production bilaterally in a multi-period. Furthermore, the algorithm provides researchers the possibility to study Fisher´s portfolio framework from a dyadic supply chain position.

Payment Timing in Multiechelon Supply Chains: Cost Assessment, Incentives, and Coordination

Advanced information technologies are increasing the variety of payment triggers between supply chain stages. The cost assumptions made by standard multiechelon inventory models, however, do not have the flexibility to account for these rapid changes. We take one step towards closing this gap by focusing on common payment triggers used with wholesale prices. We introduce a notation system to explicitly connect physical and financial flows so that payment streams can be expressed as functions of standard physical inventory metrics. We then derive a new cost formulation that captures the financial inventory cost implications of such payment streams. These tools are powerful because they dramatically increase the usability and adaptability of existing multiechelon inventory models, under both traditional and more general payment terms. To illustrate, we apply these methods to derive insights on three supply chain topics in a two-echelon base stock model. First, we examine how wholesale prices affect competitive inventory policies and supply chain efficiency under standard payment terms – showing that the cost of decentralization is not only due to too little inventory at the retailer, but also to too much inventory at the supplier. Second, we explore consignment as a possible coordination mechanism – showing that full consignment causes the supplier to overly restrict the inventory at the retailer, but partial consignment can achieve coordination. Third, we study the impact of unequal costs of capital between supply chain members - providing insight into when extending lower capital cost rates to supply chain partners is beneficial.

The price of reverse factoring: Financing rates vs. payment delays

Reverse factoring—a financial arrangement where a corporation facilitates early payment of its trade credit obligations to suppliers—is increasingly popular in industry. Many firms use the scheme to induce their suppliers to grant them more lenient payment terms. By means of a periodic review base stock model that includes alternative sources of financing, we explore the following question: what extensions of payment terms allow the supplier to benefit from reverse factoring? We obtain solutions by means of simulation optimisation. We find that an extension of payment terms induces a non-linear financing cost for the supplier, beyond the opportunity cost of carrying additional receivables. Furthermore, we find that the size of the payment term extension that a supplier can accommodate depends on demand uncertainty and the cost structure of the supplier. Overall, our results show that the financial implications of an extension of payment terms need careful assessment in stochastic settings.

Analysis of inventory policies for perishable items with fixed leadtimes and lifetimes

This paper deals with a continuous review inventory system with perishable items and Poisson demand. Lifetimes and leadtimes are assumed to be fixed. First, a quite general base-stock model is developed where a number of combinations between backorder- and lost sales policies are evaluated and optimized. The solution technique for all these combinations is exact. Secondly, we consider the case with non-negligible ordering costs and assume that the inventory policy employed is the commonly used (\(R,Q\)) policy. We develop a new heuristic approach for evaluating and analyzing the proposed \((R,Q)\) model and compare our results with those obtained by related papers. This heuristic approach uses the base-stock model developed as a building block. The results reveal that our approach works reasonably well in all cases considered.

Comparison between Two Types of Inventory Targets under Variability of a Semiconductor Supply Chain

As an innovation in the semiconductor industry grows speedy, supply chain processes have not followed up. The variability in semiconductor supply chain have increased and been more complicated. These results in accurately forecast demand and set inventory target. Demand and supply are more and more stochastic and non-stationary. Inventory is one of the methods that companies are able to buffer themselves from complex and variable environment, while still being able to satisfy customer needs. We explore the variability of semiconductor industry in automotive industry. On the supply side, we evaluate variability in complexities of manufacturing process and also products are composed with multiple parts efforts to stochastic production lead-time. However in this paper, we disregard the variability arising from supply side so we assumed lead-time is fixed at 16 weeks. For demand side, the phenomenon is known as the bullwhip effect, the demand variability increases as one move up a supply chain, severely effects to semiconductor supply chain. This results the stochastic demand process is not well understood. Thus we evaluate the stochastic in demand as two aspects: 1) the dispersion of historical demand data from its mean which denoted as standard deviation of demand, 2) the difference between the actual demand and forecast data which denoted as standard deviation of forecast error. We use them as a proxy for demand variability. Then we apply the data to the base stock model. Then, we determine what each variability parameter contributes to inventory. The inventory model represents the semiconductor manufactory’s inventory with actual statistical data which provided from semiconductor company to calculate inventory target required to meet the desired customer service level.

Entrepreneurs and Newsvendors: Do Small Businesses Follow the Newsvendor Logic When Making Inventory Decisions?

This work empirically assesses the degree to which inventory decisions made by entrepreneurs and small businesses are informed by the logic underlying the newsvendor or base stock model and are influenced by the decision-maker's risk profile. We used a web- and email-based survey, combined with a telephone follow-up to elicit risk profiles, obtaining 51 usable responses. Our findings suggest that entrepreneurs do follow the newsvendor logic, but more so for high-margin than for best-selling products. We find that entrepreneurs' risk profiles are consistent with a key prediction from prospect theory, displaying risk aversion for profits and risk-seeking behavior for losses. Furthermore, we find that risk aversion for profits is associated with higher safety stocks, in contradiction to existing theory, and discuss several possible explanations for this finding.

Improving agility of supply chains using base stock model and computer based simulations

PurposeThe purpose of this paper is to deal with the application of the stochastic inventory model to the three‐tier supply chain and verify the values obtained by mathematical model in physical simulation.Design/methodology/approachThe paper investigates three‐stage serial supply chain with stochastic demand and fixed replenishment lead‐time. Inventory holding costs are charged at each stage, and each stage may incur a consumer backorder penalty cost charged by primary supplier to secondary supplier. The customer‐demand follows Poisson distribution. The base stock model is implemented for inventory control at both suppliers. Physical simulation is then designed in such a way that it satisfies all the assumptions for mathematical model. Simulation is run to verify the values obtained from mathematical model.FindingsComputer simulation is designed to include all the assumptions made by mathematical model. Hence, mathematical base stock model and computer simulation model are comparable. Demand follows Poisson distribution in both cases. The backorder cost and inventory holding cost are calculated in each phase of simulation and summarized. The paper infers that the total inventory cost is optimum in phase II, in which reorder point is same as that calculated by mathematical model. In phase I, total inventory cost is more than that of phase II because of backorders. In phase III, excess inventory increased the total cost. Thus, the values obtained from mathematical model produce optimal inventory cost. Base stock model is effective when the demand is not deterministic and service factor assumed in mathematical model is 0.9, which is quite acceptable. Base stock model assumes replenishment order quantity as 1 and the total inventory cost decreases with replenishment lead time. Base stock model is beneficial for supply chains having short replenishment lead time. Computer simulation results indicate that discrete event simulations can be used to model stochastic systems like organizational supply chains and to validate the results from mathematical models.Originality/valueThe paper offers a review of simulation work aiming to support improvement of agility in the supply chain.

Measuring Item Fill-Rate Performance in a Finite Horizon

The standard treatment of fill rate relies on stationary and serially independent demand over an infinite horizon. Even if demand is stationary, managers are held accountable for performance over a finite horizon. In a finite horizon, the fill rate is a random variable. Studying the distribution is relevant because a vendor may be subject to financial penalty if she fails to achieve her target fill rate over a specified finite period. It is known that for a zero lead time, base-stock model, the expected value of a finite-horizon fill rate exceeds the long-run fill rate. In this paper, I investigate the behavior of the distribution of the finite-horizon fill rate when a stationary base-stock policy is used to control inventory. For a vendor facing a finite-horizon, fill-rate-level contract and using a stationary stocking policy, I examine how the the length of the review horizon (i.e., monthly or quarterly), the demand distribution, and the cost of failing to meet the target affect the stocking decision.

Some stochastic inventory models with deterministic variable lead time

In this paper we determine optimal reduction in the procurement lead time duration for some stochastic inventory models, jointly with the optimal ordering decisions. The models are developed with complete and partial information about the lead time demand distribution. The stochastic models analyzed in this paper are the classical continuous and periodic review models with a mixture of backorders and lost sales and the base stock model. For each of these models, we provide sufficient conditions for the uniqueness of the optimal operating policy. We also develop algorithms for solving these models and provide illustrative numerical examples.