Inventory Service Level Research Articles

Inventory Transshipment Considering Greenhouse Gas Emissions for Sustainable Cross-Filling in Cold Supply Chains

In recent years, sustainable supply chain management has gained increasing attention, and greenhouse gas (GHG) emissions throughout supply chains have been identified as one of the most important sustainability issues. This paper presents an investigation of the problem of transshipment among distribution centers (DCs) in a cold supply chain to achieve sustainable inventory cross-filling. Although transshipment is an effective tool for supply chain pooling, the possibility of increased GHG emissions raises environmental concerns. This study establishes a sustainable cold-chain logistics model that considers GHG emissions from DC storage and transshipment trucks. The new sustainable cold-chain model also reflects laden status and cargo weights of trucks for accurate emission assessment. An optimization model is also developed to minimize both GHG emissions and costs in the cold chain. Numerical simulations are conducted for diverse problem cases to examine important problem characteristics. The result analysis identifies that inventory service levels and demand variability have a strong impact on GHG emissions in transshipment; small p-values in the statistical analysis verify the significance of this effect. The different effects of demand variability and service levels on each emission source are also analyzed. The results demonstrate that transshipment among DCs can effectively reduce both GHG emissions and costs in cold supply chains. This study provides useful models and tools to assess GHG emissions and optimize decisions for the design and operation of transshipment. The proposed models will enable the assessment of sustainable alternatives and achieve sustainability objectives effectively for cold supply chains.

Multi-Criteria Inventory Optimisation of University of Ibadan, Ibadan, Nigeria’s Bakery Using Goal Programming Approach and Flour as the Major Raw-Material

Asides inventory cost, which is being used as the only inventory performance measure at the University of Ibadan bakery, a number of other criteria, such as inventory cost, service level, inventory turnover and delivery lead time which influence the performance of an inventory system have surfaced over the years. Hence, there is the need to examine all these criteria-objectives altogether. Therefore, this research was centred towards optimising the inventory system of University of Ibadan bakery, putting multiple criteria into consideration.
 Data on 17 raw materials: their costs, suppliers, usage rate, lead time, storage space and available capital were collected by means of interviews, past records and observations. The weighted goal program algorithm was adopted to find the best compromise between fulfilling the four objectives by minimising the sum of the deviation from the target values of the goals. Subsequently, Lingo 17.0 and Tora 1.0 optimisation software packages were used to solve and compare the model generated, while putting into consideration storage space constraint and budgeted capital.
 The developed model from the goal programming algorithm exhibited four goals (combined into one objective function). Same solutions were obtained from Lingo 17.0 and Tora 1.0. While Lingo 17.0 gave a uniform service level of 100% , a turnover ratio greater than 1(>1) for all the materials at a negligible increase (of < 0.0001%) in total inventory cost of the raw materials and available lead time duration of zero days (< 24 hours) for each material, Tora 1.0 gave a uniform service level of 100% , a turnover ratio greater than 1 (> 1) for all the materials at a negligible increase (of < 0.0001%) in total inventory cost of the raw materials and available lead time duration of zero days (< 24 hours) for each material.
 Implementation of the developed model will eliminate unnecessary waiting time between production, thereby ensuring effective and efficient utilisation of raw materials in production which will lead to reduced cost of holding inventory, elimination of unnecessary overall cost and wastages, and also improvement of the productivity and profit on the long run.

Empty container reposition using max-min review system: Simulation approach

Global economic conditions and differences in the ability and economic competitiveness of each country cause an imbalance between exports and imports. Shipping of goods is carried out mostly by sea transportation through the use of containers and involves the port as the exit and entry of goods. An imbalance in export-import causes the number of containers in some ports to have a surplus, while others have a deficit. To maintain container availability, shippinglines must adjust the balance by repositioning from a surplus port to a deficit port. Shippinglines must determine the inventory to meet the needs at the port and the number of containers that can be repositioned to the other ports that need it. If the inventory at the port reaches a minimum level, then the order must be immediately carried out, on the contrary, it will be repositioned to another port that needs it. By adopting the concept of a review system (U, D), when the inventory is less than U, an order of Q is immediately placed. But if the inventory is greater or equal to D, repositioning is carried out. Repositioning is done by I-D. In this study, the maximum inventory value is determined based on the value of the inventory service level of 95%. From the simulation results it was concluded that the min-max review system policy with a service level of 95% could reduce inventory by 35.64% and reduce shortage by 8.82%.

The Impact of Lead Time Uncertainty on Supply Chain Performance Considering Carbon Cost

In supply chain operation practices, lead time uncertainty is a common management issue. Uncertain lead time can lead to increased inventory costs and unstable service levels, which will directly affect the overall operation performance of the supply chain. While considering environmental performance in supply chain, it is important to understand how an uncertain lead time will affect sustainable performance. In this paper, we constructed a supply chain model with stochastic lead time and explored the relationship between uncertain lead time and supply chain performance. We considered carbon cost, inventory cost, and service level as a supply chain performance. System dynamics methodology was employed to observe and explore the dynamic change trend of the overall performance in the complicated supply chain model. This was done under both different levels of lead time standard deviation and different order policies. The results demonstrate how stochastic lead times can significantly increase inventory costs and carbon costs. Therefore, we propose appropriate ordering policies which mitigate the negative impacts of stochastic lead times.

Classification methods for problem size reduction in spare part provisioning

Spare part provisioning for asset-intensive companies is a complicated problem due to the large number of items, low demand rates, and multi-echelon environment. A primary strategy for reducing the size and managing a large number of spare parts is using grouping techniques and data aggregation. In this paper, we address the question of how to reduce the size and complexity of large-scale, two-echelon, service part provisioning systems to benefit both inventory service levels and managerial processes while considering performance trade-offs. This paper contributes a performance-based inventory classification approach for a two-echelon inventory model by developing a novel ranking method. First, it defines the concept of the artificial stocking policy as a new classification criterion in the literature. Then, it adopts a non-subjective weighted linear scoring method for ranking items in the entire network. Finally, it presents a heuristic partitioning method, which is evaluated and compared with complete enumeration and eight alternative clustering and classification methods. The proposed model is implemented and tested in the context of the classic repairable spare part inventory model, called the VARI-METRIC. The results indicate that the proposed method is easy to apply and significantly outperforms the alternatives.

Consistency and Recovery in Retail Supply Chains

Practitioners and researchers describe inventory service level with metrics that communicate the likelihood of demand fulfillment without considering the ongoing capabilities of the supplier, for example, in‐stock and fill rate. We develop a method for measuring inventory service level that incorporates such supplier capabilities, namely consistency (the ability of a supplier to fulfill orders repeatedly) and recovery (the ability of a supplier to fulfill orders after a lapse in service). Using data from two retail supply chains, we illustrate our approach. To demonstrate the impact of consistency and recovery on supply chain performance, we model a retailer purchasing from competing suppliers with different levels of consistency and recovery. The model incorporates the retailer's uncertainty about demand and the retailer's uncertainty about its suppliers' service levels. We characterize how the retailer's orders and profitability change with a supplier's delivery performance through numerical experiments calibrated with field data. We find notable differences in market share across suppliers with similar traditional inventory service level metrics but differences in consistency and recovery. Further, we observe that a retailer can increase its profitability by determining orders via consistency and recovery in lieu of common metrics like in‐stock. Given the influence of consistency and recovery on supply chain outcomes, we discuss implications for practice and future research.

Quick response strategy with cleaner technology in a supply chain: coordination and win-win situation analysis

Quick response strategy (QRS) has been widely adopted in a supply chain where members collect timely market information for better forecasting, and then respond promptly to the market changes by adjusting initial inventory decision. After adopting the QRS, sustainability issues such as greenhouse gas emission and energy waste may be more serious as production lead time is shorter. In this study, due to this dilemma, we develop a two-stage quick response supply chain with cleaner technology, where the manufacturer determines the cleaner technology investment and afterwards the retailer decides the ordering quantity. Based on Bayesian theory, we depict an information updating process for the QRS with cleaner technology. First, we find that the inventory service level significantly affects both manufacturer’s and retailer’s performance under the QRS with cleaner technology. Moreover, our analytical results indicate that the performance of centralised supply chain system is always better than the decentralised one. As a result, we propose two supply chain contracts, minimum ordering quantity (MOQ) and MOQ with buyback (MOQ-BB) to achieve supply chain coordination. Comparing with the MOQ, the MOQ-BB is more flexible to allow better allocation of the ‘additional’ expected profit between the channel members through the buyback price negotiation.

Measuring consumer awareness and marketing's impact on brand selection

Life and values are changed; the work is not only the basic questions of life. The underlying assumption is that a particular form of advertising can encourage or discourage, this view is not going to change for the brand. Brand is the prosperity of any business, productivity, and efficiency is crucial. It our country it is seen that mostly marketing efforts are intense and give way to people selecting new brands and impulse buying in bigger shopping settings. The effect was seen on the perforamcce of the brand as well the brand in totality. Subliminal advertising was considered to be a key braeakthrough of the 21st centruy where the customers subconcsuons was targeted inorder to incite a sale or purchase. In doing so it provided a foundation based on clear concepts and guidelines of a rather broad manner. This was also done to develop and evaluate new techniques for interactive communication. In particular, it has been found to support the theory, inventory is a function of the square root of sales. Additionally, a greater variety of products lead to an increase of stocks and inventory service level is an exponential function. Finally, the uncertainties of demand and inventory levels are not found to

Analysis of lead time effects on VMI system: system dynamics approach

Vendor managed inventory (VMI) is a widely used strategy for the collaborative inventory management. It is known to be a very effective strategy in terms of inventory reduction and service level improvement. And these benefits of VMI depend on the environment of supply chain such as customer demand, ordering policy and lead time. This research aims to figure out the effect of lead time on the benefit of VMI system. System dynamics approach is adopted so as to model the traditional supply chain and the VMI supply chain. Verification and validation tests are carried out to check the appropriateness of the models. To perform the experimental test under various environments, multiple scenarios considering various characteristics of lead time were developed. Some interesting findings are observed from the experimental test and their managerial implications are discussed.

Product Level Evidence of the Bullwhip Effect: Measurement, Aggregation and Impact

Empirical estimation of the bullwhip effect has several challenges as discussed in prior literature (e.g., Cachon et al. 2007; Bray and Mendleson 2012; Chen and Lee 2012), although the bullwhip effect has been well studied in the modeling papers. We address these empirical challenges using a large-scale dataset collected from a large Chinese supermarket chain, we estimate the bullwhip effect at product level using various methods, analyze different measurements and aggregations of the bullwhip effect, and examine the impact of the bullwhip effect on supply chain performance in terms of inventory and stockouts. We find that: (1) The bullwhip effect measured using shipment variance is lower than that measured using order variance, and the bullwhip effect measured using material flow is greater than that measured using information flow. (2) The aggregated bullwhip effects, by product, by store and by time, are all lower than the disaggregated bullwhip effect. (3) The bullwhip effect is associated with poor supply chain performance measured by elevated inventory ratio and stockouts. We estimate that, on average, a decrease in the bullwhip effect ratio by 1 can translate into inventory cost savings of $26 and stockout reduction of 0.15 days for a product at a store during a year. These results suggest that order pooling can be an effective way to mitigate the bullwhip effect, and mitigating the bullwhip effect can improve supply chain performance and generate sizable benefits in terms of inventory cost savings and service level improvement.

Inventory Service Target in Quick Response Fashion Retail Supply Chains

Different fashion brands have different inventory service targets. To achieve the desirable inventory service level without the need of holding a lot of inventory, fashion companies commonly adopt the quick response practice. In this paper, we conduct an analytical study on the influence played by the inventory service target on quick response fashion retail supply chains. To be specific, we consider the case when the fashion retailer, who is the channel leader, aims to achieve an inventory service target in its inventory planning. We explore impacts of the inventory service-targeted quick response program, in terms of expected profit, and profit risk on the fashion retailer, the fashion manufacturer, and the fashion supply chain system. We also examine the quick response’s impact on social welfare. We show how three practical contracts, namely, the fixed-fee contract, the wholesale pricing contract, and the product variety contract, can be employed to achieve a win–win situation in the fashion retail supply chain system with the adoption of the inventory service-targeted quick response program. Closed-form managerial insights on how the inventory service target affects the expected benefit of quick response, the reduction of profit risk under quick response, the expected gain of social welfare, the achievability of the win–win situation, and the setting of contractual parameters, are derived.

The impact of product returns and remanufacturing uncertainties on the dynamic performance of a multi-echelon closed-loop supply chain

We investigate a three-echelon manufacturing and remanufacturing closed-loop supply chain (CLSC) constituting of a retailer, a manufacturer and a supplier. Each echelon, apart from its usual operations in the forward SC (FSC), has its own reverse logistics (RL) operations. We assume that RL information is transparent to the FSC, and the same replenishment policies are used throughout the supply chain. We focus on the impact on dynamic performance of uncertainties in the return yield, RL lead time and the product consumption lead time. Two outcomes are studied: order rate and serviceable inventory. The results suggest that higher return yield improves dynamic performance in terms of overshoot and risk of stock-out with a unit step response as input. However, when the return yield reaches a certain level, the classic bullwhip propagation normally associated with the FSC does not always hold. The longer remanufacturing and product consumption lead times result in a higher overshoot and a longer time to recover inventory, as well as more oscillation in the step response at the upstream echelons. We also study bullwhip and inventory variance when demand is a random variable. Our analysis suggests that higher return yield contributes to reduced bullwhip and inventory variance at the echelon level but for the CLSC as a whole the level of bullwhip may decrease as well as increase as it propagates along the supply chain. The reason for such behaviour is due to the interaction of the various model parameters and should be the subject of further analytical research. Furthermore, by studying the three-echelon CLSC, we produce a general equation for eliminating inventory offsets in an n-echelon CLSC. This is helpful to managers who wish to maintain inventory service levels in multi-echelon CLSCs.

Impacts of retailer’s risk averse behaviors on quick response fashion supply chain systems

Supply chain systems for fashion apparel products face a high level of risk as the market demand is very volatile and unpredictable. In order to cope with demand volatility, the quick response system which aims to shorten replenishment lead time has been well-established. With a shortened lead time, retailers can postpone the ordering decision and improve their demand forecast by gathering updated market information. However, there is a limit for quick response in which the market demand forecast is never fully accurate and uncertainty can never be fully eliminated. Thus, to keep the level of risk under control, retailers tend to possess a risk-averse behaviour in making their respective inventory decisions. In this paper, we explore the make-to-order quick response fashion supply chain with a risk averse retailer. We employ the mean-risk framework to incorporate the retailer’s risk averse behavior into the optimization model. With the focal point on uncovering the impacts brought by the retailer’s risk averse behavior to the quick response fashion supply chain system, we analytically derive important theoretical insights regarding the retailer’s optimal decisions, the implied inventory service levels, the values of quick response, and the contractual arrangements to attain Pareto improvement when the retailer is risk averse.

Consistency and Recovery in Retail Supply Chains

Practitioners and researchers describe inventory service level using uni-dimensional metrics, e.g., in-stock and fill rate. However, prior research identifies dynamic aspects of supplier performance: consistency (the ability of a supplier to fulfill orders repeatedly) and recovery (the ability of a supplier to fulfill orders after a lapse in service). We develop a method for measuring consistency and recovery, and we test the method in the retail supply chain context using more than 40,000 order and fulfillment records provided by suppliers to retailers in two distinct settings. We model a retailer purchasing from competing suppliers with different levels of consistency and recovery. This model incorporates the retailer's uncertainty about demand and the retailer's uncertainty about its suppliers' service levels, which the retailer assesses using each supplier's prior performance. We characterize how the retailer's orders change with a supplier's performance through numerical experiments designed using field-based data. Specifically, we find pronounced contrasts in market share across suppliers with similar traditional inventory service levels metrics but differences in consistency and recovery. Given the influence of consistency and recovery on supply chain outcomes, we discuss implications for practice and future research.

USING COST-VOLUME-PROFIT TO ANALYSE THE VIABILITY OF IMPLEMENTING A NEW DISTRIBUTION CENTER

Firms are constantly improving their activities in order to become more competitive. With the diffusion of international competition and easier access to global markets, effective logistic and inventory management strategies become essential to all players. In this sense, there is a clear tradeoff between inventory costs and service level. A common strategy to address this issue is to locate distribution centers (DC) near key markets. However, the decision to build new DCs must be supported by clear and convincing analysis. In this context, this paper reports the use of cost-volume-profit analysis to assess the viability of establishing a new DC by a real company that manufactures radiopharmaceutical products. The researchers collected and analysed detailed financial information from the company and compared the current scenario with potential future scenarios using the cost-volume-profit technique. Next, expected firm profitability is compared for two scenarios: with and without the new DC.

The Impact of Supplier Reliability Tracking on Customer Demand: Model and Estimation Methodology

To set inventory service levels, suppliers must understand how changes in inventory service level affect demand. We build on prior research, which uses analytical models and laboratory experiments to study the impact of a supplier's service level on demand from retailers, by testing this relationship in the field. We analyze a field experiment at the supplier Hugo Boss to deter- mine how the supplier's inventory service level affects demand from its retailer customers. We find increases in historical fill rate to be associated with statistically significant and managerially substantial increases in current retailer orders (i.e., demand, not just sales). Specifically, a one percentage point increase in fill rate, measured over the prior year, is associated with a statistically significant 11% increase in current retailer demand, controlling for other factors that might affect retailer demand. We explore the drivers of this demand increase, including changes in retailer assortment and order frequency. We discuss features of a retail buyer's decision context identified through our field work that may explain the magnitude of the relationship we observe.

Managing Inventory with Cash Register Information: Sales Recorded but Not Demands

Inventory inaccuracy is common in many businesses. While retailers employ cash registers to enter incoming orders and outgoing sales, inaccuracy arises because they do not record invisible demand such as spoilage, damage, pilferage, or returns. This setting results in incomplete inventory and demand information. An important inventory control problem therefore is to maximize the total expected discounted profit under this setting. Allowing for dependence between demand and invisible demand, we obtain the associated dynamic programming equation with an infinite‐dimensional state space, and reduce it to a simpler form by employing the concept of unnormalized probability. We develop an analytical upper bound on the optimal profit as well as an iterative algorithm for an approximate solution of the problem. We compare profits of the iterative solution and the myopic solution, and then to the upper bound. We see that the iterative solution performs better than the myopic solution, and significantly so in many cases. Furthermore, it gives a profit not far from the upper bound, and is therefore close to optimal. Using our results, we also discuss meeting inventory service levels.

A Congested Double Path Approach for a Hub Location-allocation Problem with Service Level at Hubs

In this paper an uncapacitated multiple allocation p-hub median problem is discussed. The model minimizes the transportation cost based on inventory service level at hubs through double paths between hubs. Inventory service level is defined as the percent of inventory shortage of vehicles at hubs according to passengers demand and replenishment time of vehicles. A real data from 25 nodes of municipality district 14 of Tehran is used to evaluate the performance of inventory service level in hub networks.

Late customization strategy with service levels requirements

Both supply issues and interaction between various stages of a production network are common occurrences in product customization under a delayed differentiation strategy. This paper studies non-decouple systems for product customization in the context of a single-market segment under a delayed differentiation strategy by incorporating supply issues and interaction between various stages of a production network. We analyze two alternatives for customizing two individual products. One alternative is the contractual obligations for raw materials delivery where the supplier is responsible for maintaining certain inventory service levels agreements. The other alternative is the customer demand requirements where the manufacturer is responsible for maintaining certain end-products service levels to prevent customer erosion. We characterize the service level requirements in both scenarios and determine the optimal customization point of the production network where the practitioner can operate at minimum cost. We use a Bayesian Belief Networks method to model interaction between stages of the production network and derive the inventory service level requirements. A mini-case involving the customization of a personal desktop computer is used to illustrate the applicability of this framework. We also compare the benefits of delayed product differentiation under non-decouple systems and the traditional decouple systems and provide insights into how firms can choose the right strategy to effectively compete.

Stochastic economic lot sizing and scheduling problem with pitch interval, reorder points and flexible sequence

This paper presents a solution for a class of the stochastic economic lot sizing scheduling problem that is typical of the replenishment pull system proposed by the lean manufacturing approach. In this class, lots of any product are produced in fixed intervals called pitch. The proposed solution uses flexible production sequences and reorder points that are compatible with the concepts of supermarket and level production. It adopts the queuing discipline obtained from a fluid model that approximates the stochastic process of arrival and production orders. Given the queuing discipline, an iterative algorithm returns a near-optimal solution for the system. The proposed approach allows us possible to differentiate inventory cost and service levels by product, and the stock required is lower than that required by the discipline ‘first stock out, first out’. The algorithm is fast and stable, allowing its frequent use in real-world instances.