Lead Time Information Research Articles

Supply chain pricing strategies for advance selling with a deposit

Abstract Accepted by: Konstantinos Nikolopoulos This research studies the pricing strategy of a supply chain with a manufacturer and a retailer. We analyze the customers’ purchase behaviour in advance selling with deposit by using utility theory and obtain the pricing decision of the supply chain by using the Stackelberg model. We find that when consumers are not sensitive to the distribution lead time, the retailer’s advance selling strategy could Pareto improve the whole supply chain. Better informed consumers and moderate advance selling distribution lead time information could expand the retailer’s pre-sale possibilities by enlarging the feasible range of advance selling.

Replenishment strategies for lost sales inventory systems of perishables under demand and lead time uncertainty

We develop an inventory control policy for perishable products considering both random demand and random lead time. We consider a B2C retail environment where excess demand is lost. The policy dynamically determines the optimal replenishment quantity under a service level constraint in every period, allowing for order-crossing, a widely disregarded characteristic in the literature. Regarding perishability, we compare the two most extreme issuing policies, first-expired-first-out (FEFO) and last-expired-first-out (LEFO), and evaluate our policy to existing inventory policies for perishables that typically ignore lead time uncertainty.We obtain several interesting findings. First, we show that ignoring lead time uncertainty and planning based on the expected lead time significantly undershoots the target service level. Even planning with the maximum lead time, under LEFO, the achieved service level would still fall considerably below the target, which the lost-sales structure can explain. On the other hand, under FEFO, the achieved service level would overshoot the target service level, which leads to unnecessary waste. Second, a more reliable lead time can significantly reduce waste, especially under LEFO. Third, our model allows us to distinguish between past, present, and future lead time uncertainty and thus to consider partial lead time information. We show the value of lead time information on outstanding orders. Fourth, we evaluate the impact of a fast but unreliable delivery option and a slow but reliable delivery option on the retailer’s average waste and ordering process. We find that the optimal choice depends on the demand characteristics.

11-Year Radar-Based Study of Tornadic Thunderstorms over Central Oklahoma

In an 11-y period (2000–2010) 126 tornadoes affected central Oklahoma within a 111-km (60-nm) radius of the Twin Lakes, Oklahoma (KTLX) WSR-88D. The tornadoes resulted in 265 injuries and 3 deaths. This study used archived WSR-88D data to obtain information about storm characteristics such as mode, width, height, and measures of the mesocyclone, at the time of initial tornado formation. The radar data provided information about the supercell spectrum and highlighted differences between tornado-producing supercells and tornado-producing quasi-linear convective systems (QLCSs), especially with respect to midlevel rotational velocity. Warning lead-time information also was obtained and compared with the radar characteristics. No specific radar attribute was strongly correlated with lead time, likely due to the multitude of variables involved in the tornado warning process. A strong correlation did exist between lead time and storm mode. Applying these findings in an operational environment similar to that found in central Oklahoma may enhance tornado warning performance.

Price and Lead time Disclosure Strategies in Inventory Systems

We consider a firm that faces the following pricing and leadtime disclosure decision. Either, (i) offer the same price to all arriving customers and disclose no leadtime information, in which case customers know only an expected leadtime, or, (ii) vary the price dynamically based on the current inventory status, and disclose leadtime information dynamically. The second option allows the firm to offer a price discount for a long leadtime, and to offer a short leadtime when it has a large inventory. In order to determine which pricing and leadtime disclosure policy attains higher maximum profit, we formulate and solve a Markov decision problem for each policy and compare the two solutions. The comparison suggests that, typically, there is a critical inventory level, above which static pricing and no leadtime disclosure yields higher profit, and, below that level, dynamic pricing and leadtime disclosure yields a higher profit. We also identify certain situations where the static policy leads to profits at least as large as those under the dynamic policy for any state, and we examine the price differences between the two policies. Finally, the structure of the solution is similar to the situation where the system manager can pause the production process, except that now this extra lever partly mitigates the superiority of the static policy.

Price and Leadtime Disclosure Strategies in Inventory Systems

We consider a firm that faces the following pricing and leadtime disclosure decision. Either, (1) offer the same price to all arriving customers and disclose no leadtime information, in which case customers know only an expected leadtime, or, (2) vary the price dynamically based on the current inventory status, and disclose leadtime information dynamically. The second option allows the firm to offer a price discount for a long leadtime, and to offer a short leadtime when it has a large inventory. In order to determine which pricing and leadtime disclosure policy attains higher maximum profit, we formulate and solve a Markov decision problem for each policy and compare the two solutions. The comparison suggests that, typically, there is a critical inventory level, above which static pricing and no leadtime disclosure yields higher profit, and, below that level, dynamic pricing and leadtime disclosure yields a higher profit. We also identify certain situations where the static policy leads to profits at least as large as those under the dynamic policy, for any state, and we examine the price differences between the two policies. Finally, the structure of the solution is similar to the situation where the system manager can pause the production process, except that now this extra lever partly mitigates the superiority of the static policy.

Direct Polymer Additive Tooling - Economic Analysis of Additive Manufacturing Technologies for Fabrication of Polymer Tools for Injection Molding

This research project aims to analyze the economic feasibility of Additive Manufacturing (AM) as supportive technology in Injection Molding (IM) tool manufacturing by estimating the potential cost-savings and lead-time reduction. Cost and lead-time considerations during small part quantity production are analyzed by developing an extended estimation model for the integration of AM in tool production. Based on six different real reference parts, the developed model shows the projected savings and the current limitations of AM applications in tool production due to material constraints. Furthermore, this extended model offers a holistic view on part cost and lead-time information, by considering tooling as well as production phases in IM and integrating tool life as a key variable. Hence, this research project closes a literature gap and facilitates the integration of AM into the IM process chain.

Proactive Customer Service: Operational Benefits and Economic Frictions

Problem definition: We study a service setting where the provider has information about some customers’ future service needs and may initiate service for such customers proactively, if they agree to be flexible with respect to the timing of service delivery. Academic/practical relevance: Information about future customer-service needs is becoming increasingly available through remote monitoring systems and data analytics. However, the literature has not systematically examined proactive service as a tool that can be used to better match demand to service supply when customers are strategic. Methodology: We combine (i) queueing theory, and in particular a diffusion approximation developed specifically for this problem that allows us to derive analytic approximations for customer waiting times, with (ii) game theory, which captures customer incentives to adopt proactive service. Results: We show that proactive service can reduce customer waiting times, even if only a relatively small proportion of customers agree to be flexible, the information lead time is limited, and the system makes occasional errors in providing proactive service—in fact, we show that the system’s ability to tolerate errors increases with (nominal) utilization. Nevertheless, we show that these benefits may fail to materialize in equilibrium because of economic frictions: Customers will underadopt proactive service (due to free-riding) and overjoin the system (due to negative congestion-based externalities). We also show that the service provider can incentivize optimal customer behavior through appropriate pricing. Managerial implications: Our results suggest that proactive service may offer substantial operational benefits, but caution that it may fail to fulfill its potential due to customer self-interested behavior.

Vertical and horizontal impacts of information sharing on a divergent supply chain

Despite the tremendous advancements in communication technologies, convincing decision makers in supply chains to establish cooperation mechanisms remains a challenge. This paper aims to raise awareness among supply chain partners regarding the importance of information sharing by assessing the value of upstream (supplier lead time) and downstream (customer demand) information sharing in a divergent supply chain. Through a simulation study, this work investigates the impact of information sharing on the logistic cost of each link horizontally and vertically and for each cost component for three different structures of the distribution network. The results show that the warehouse benefits the most from information sharing and should implement revenue-sharing contracts to motivate its partners to share their information. In addition, the gains are not sensitive to the structure of the distribution network, and the retailers' gains are proportional to their level of demand and unit holding and penalty costs. Moreover, the holding and penalty costs for the warehouse are sensitive to the sharing of supplier lead time information when only holding costs are sensitive to the sharing of customer demand information at the warehouse and retailers.

Impacts of El Nino Southern oscillation and the Indian Ocean SSTs on inflow to the Roseires Dam in the Sudan

In this study, potential impacts of ENSO and Indian Ocean SSTs indices on variability of inflow to the Roseires Dam are investigated. The objective is to investigate the relationship between these climatic indices and the river flow to enable building a forecasting tool to give a lead time prediction of inflow to the dam using lead time information of these indices. Ninety one years of annual flow (1914–2004) were used in the analysis. Correlation results showed that the annual inflow to the dam has negative relation with the ENSO-SST index and SST indices from two regions in the Indian Ocean. Two probabilistic prediction models were developed using the concept of conditional probability to forecast the inflow to the dam. Modelling results showed significant improvements in predictability of inflow when these indices are used as predictors. The models developed could contribute to better water management and operation of the dam reservoir.

Impacts of El Nino Southern oscillation and the Indian Ocean SSTs on inflow to the Roseires Dam in the Sudan

In this study, potential impacts of ENSO and Indian Ocean SSTs indices on variability of inflow to the Roseires Dam are investigated. The objective is to investigate the relationship between these climatic indices and the river flow to enable building a forecasting tool to give a lead time prediction of inflow to the dam using lead time information of these indices. Ninety one years of annual flow (1914–2004) were used in the analysis. Correlation results showed that the annual inflow to the dam has negative relation with the ENSO-SST index and SST indices from two regions in the Indian Ocean. Two probabilistic prediction models were developed using the concept of conditional probability to forecast the inflow to the dam. Modelling results showed significant improvements in predictability of inflow when these indices are used as predictors. The models developed could contribute to better water management and operation of the dam reservoir.

Incorporating Order Crossover Information into Service-Oriented Base Stock Policy Decisions

We investigate the impact of order crossover on a periodic, order-up-to (R,S) inventory system where attaining a target service level is the focus. Order crossover, when orders arrive in a sequence different from that in which they were originally placed, is increasingly likely to occur in modern supply chains. One reason for this is the increasing pressure to focus on service performance, which we refer to as the “Amazon Service Effect”. Many service-oriented changes to order placement and fulfillment strategies will increase the chance of order crossover. While the majority of inventory control studies, including crossover-focused studies, tend to be cost-oriented, in practice many inventory systems are driven by service goals. To explore the use of crossover information in inventory policy decision-making we adopt the “effective lead-time” (ELT) approach (Hayya et al., 2008) to representing order lead-times where order crossover is present. We develop a hybrid discrete-event/continuous simulation model that utilizes a unique structure to provide a detailed representation of a stochastic inventory system and collect precise information about order crossover, effective lead-time, service performance, and related operating characteristics. We first address two crossover related assumptions that have not been directly addressed in the literature. We find that order crossover clearly impacts service-oriented inventory system performance (much as it does cost-oriented systems) and that a protection period of R ELT time units is appropriate for an R,S inventory policy under crossover. We then explore five methods of including ELT information into the process of determining the order-up-to level (S) for a periodic, order-up-to (R,S) inventory policy. We perform a full-factorial simulation study under various conditions of target service level, demand variance, and lead-time variance, for a total of 375 scenarios. We find the method used to include effective lead-time information into the decision significantly impacts service performance. We compare and contrast the different methods, discuss issues such as data requirements and the sources of error, and address the research and managerial implications of our findings.

Decentralised decision-making with information sharing vs. centralised decision-making in supply chains

In recent years, implementing coordination mechanisms in decentralised supply chains to reduce the well-known negative effects of decentralisation, such as the ‘bullwhip effect’, has become a considerable challenge. Furthermore, with the dramatic developments in information and communication technologies, real-time information sharing has become increasingly easier to implement. In this work, we study a mono-product divergent supply chain composed of a supplier, a warehouse, retailers and customers in the context of decentralised and centralised decisions. The main objective of this study is to compare a decentralised supply chain combined with different scenarios of simultaneous upstream and downstream information sharing vs. a centralised supply chain. A mathematical model is developed to compare the logistics costs in the two decision contexts. The experimental results clearly show that the simultaneous sharing of customer demand and supplier-warehouse lead time information in a decentralised supply chain yields nearly equivalent logistics costs as the centralised supply chain context. However, the main beneficiary of the sharing is the warehouse, which receives approximately two-thirds of the benefit. Thus, incentives and revenue sharing contracts should be implemented to motivate and balance the benefits between supply chain partners.

Benefits of leadtime information and of its combination with demand forecast information

This paper studies both the benefits of leadtime information and the interrelationships among demand forecast information, leadtime uncertainty, and leadtime information, for improving inventory control performance. To this end, it explicitly incorporates both leadtime information and demand forecast information into an inventory model although they have conventionally been dealt with individually in the literature. Specifically, it considers a periodic-review inventory model with uncertain leadtimes, in which both leadtime information and demand forecast information are available. The leadtime information is modeled as the information of the arrival of outstanding orders and the demand forecast information as the Martingale Model of Forecast Evolution (MMFE).The paper analyzes the way to use both leadtime information and demand forecast information for inventory control operations. Specifically, under certain conditions, the optimality of the state-dependent base-stock policy is shown, and an effective heuristic policy is developed.It also derives some managerial insights for exploiting both leadtime information and demand forecast information in the case where leadtimes are uncertain. For instance, while leadtime information itself significantly improves inventory control performance, it also enhances the benefits of demand forecast information. Specifically, the benefits of demand forecast information may significantly decrease as leadtime uncertainty increases. However, the decrease of the benefits of demand forecast information may be alleviated with leadtime information.

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.

Stability analysis of constrained inventory systems with transportation delay

Stability is a fundamental design property of inventory systems. However, the often exploited linearity assumptions in the current literature create a major gap between theory and practice. In this paper the stability of a constrained production and inventory system with a Forbidden Returns constraint (that is, a non-negative order rate) is studied via a piecewise linear model, an eigenvalue analysis and a simulation investigation. The APVIOBPCS (Automatic Pipeline, Variable Inventory and Order Based Production Control System) and EPVIOBPCS (Estimated Pipeline, Variable Inventory and Order Based Production Control System) replenishment policies are adopted. Surprisingly, all kinds of non-linear dynamical behaviours of systems can be observed in these simple models. Exact expressions of the asymptotic stability boundaries and Lyapunovian stability boundaries are derived when actual and perceived transportation lead-time is 1 and 2 periods long respectively. Asymptotically stable regions in the non-linear Forbidden Return systems are identical to the stable regions in its unconstrained counterpart. However, regions of bounded fluctuations that continue forever, including both periodicity and chaos, exist in the parametrical plane outside the asymptotically stable region. Simulation shows a complex and delicate structure in these regions. The results suggest that accurate lead-time information is essential to eliminate inventory drift and instability and that ordering policies have to be designed properly in accordance with the actual lead-time to avoid these fluctuations and divergence.

A simulation study on strategy to mitigate leadtime uncertainty risk in the context of information sharing

Lead time is an inseparable factor of any supply chain. Lead time uncertainty is known as one of the types of supply side risks or uncertainty that affects ordering policies, inventory levels and cost, and product availability level. Information sharing between supply chain members provides opportunities to reduce the inventory levels held to face such uncertainty thereby improving the performance of the supply chain. In this context, we study the effect of sharing the information regarding replenishment leadtime on total inventory costs in a two-level supply chain. In this paper, we have proposed two-level supply chain model to analyse the effects of leadtime information and to quantify the value of the information about lead-times to the retailer. We assume that the replenishment leadtime of the supplier is random and following uniform distribution and we consider two replenishment policies the first one for the case without information sharing and the second one with information sharing. With information sharing in place the retailer exercise the strategy of employing an emergency supplier to mitigate the leadtime uncertainty. The system under consideration is formulated as a mixed integer linear programming (MILP) problem and the simulation exercise results indicate that the value of leadtime information can be significant.

An integration methodology based on fuzzy inference systems and neural approaches for multi-stage supply-chains

This paper proposes a methodology for supply chain (SC) integration from customers to suppliers through warehouses, retailers, and plants via both adaptive network based fuzzy inference system and artificial neural networks approaches. The methodology presented provides this integration by finding the requested supplier capacities using the demand and order lead time information across the whole SC in an uncertain environment. The SC structure is investigated stage by stage. The sensitivity analysis is made by comparing the obtained results with the traditional statistical techniques. A company serving in durable consumer goods industry that produces consumer electronics in Istanbul, Turkey was examined to demonstrate the applicability of the proposed methodology.

Managing Bullwhip-induced risks in supply chains

We discuss the exploitation of a well-established replenishment rule, the order-up-to policy, to control the supply chain risk resulting from the Bullwhip effect. To avoid this risk within supply chains, two specific recommendations are highly important. First, we provide strong evidence that availability of high fidelity up-to-date delivery lead-time information is essential. This inevitably requires transparency and trust between various 'players' within the supply chain. For many practical reasons this is easier to recommend than to actually achieve. Secondly, such reliable data needs to be automatically fed into the replenishment system to enable it to function in an adaptive mode. This step offers a reasonable guarantee of a well-controlled performance. It is also advisable that at the design stage, replenishment rule parameters should be set so that 'conservative' operation is achieved. The new theoretical advances described in the paper are validated via simulation models of the delivery process.

The value of sharing lead time information

The widespread adoption of Enterprise Resource Planning (ERP) systems has, among many other benefits, increased the ability of a firm to share operational data with customers. In this paper we analyze the factors that determine whether or not sharing a specific type of information, namely state-dependent lead time information, can benefit a firm. We develop a stochastic model of a custom-production environment, in which customers are handled on a first-come first-served basis but have differing tolerances for waiting. The firm has the option to share different amounts of information about the lead time a potential customer may incur. Although the information differs across scenarios, the reliability of that information in terms of the probability that a stated lead time is met is equal in the eyes of the customers. We derive conditions under which sharing more information with customers improves the firm's profits and the customers' experiences. We show that it is not always the case that sharing information improves the lot of the firm. We show that when customers' tolerances for waiting are more heterogeneous then the benefit to the firm from sharing lead time information increases. Our conclusion is that management should only authorize sharing detailed lead time information, be it through information system integration or frontline sales people, after a careful analysis of a customer's sensitivity to delay.

Manufacturing lead-time rules: Customer retention versus tardiness costs

Inaccurate production backlog information is a major cause of late deliveries, which can result in penalty fees and loss of reputation. We identify conditions when it is particularly worthwhile to improve an information system to provide good lead-time information. We first analyze a sequential decision process model of lead-time decisions at a firm which manufactures standard products to order, and has complete backlog information. There are Poisson arrivals, stochastic processing times, customers may balk in response to quoted delivery dates, and revenues are offset by tardiness penalties. We characterize an optimal policy and show how to accelerate computations. The second part of the paper is a computational comparison of this optimum (with full backlog information) with a lead-time quotation rule that is optimal with statistical shop-status information. This reveals when the partial-information method does well and when it is worth implementing measures to improve information transfer between operations and sales.