Advance Demand Information Research Articles

Contract Portfolios with Order Lead Times and Advance Demand Information in Demand Fulfilment Processes

Contract Portfolios with Order Lead Times and Advance Demand Information in Demand Fulfilment Processes

Influence of lot size and planned lead time on service level and inventory for a single-stage production system with advance demand information and random required lead times

In this paper a single-stage make-to-stock production system with advance demand information and random required lead times is investigated which produces different items with item specific lot sizes and planned lead times. Stochastic modeling is applied to investigate the influence of the interrelated planning parameters lot size and planned lead time on the key figures service level and inventory. The interrelation between the planning parameters is implemented with an endogenous production lead time distribution which depends on the lot size. Furthermore, both planning parameters lead to a pre-production on stock either anonymous (lot size) or of known customer orders (planned lead time). Most analytical studies in this field either focus on planned lead time optimization, assuming single item processing or predefined production order processing distribution, or on the optimization of production lot sizes, neglecting the planned lead time effect. The main contribution of this paper is that it provides a simultaneous treatment of these two planning parameters linked to an endogenous production lead time. Analytical results enable the numerical optimization of both planning parameters to reach a service level constraint with minimum overall inventory and can, e.g., be applied to discuss the effect of customer required lead time variance on optimal planning parameters. Numerical study results indicate that for moderate service level objectives, optimal lot sizes are near the production lead time minimizing lot size. Furthermore, the results show that higher lot sizes can reduce the service level for predefined planned lead time values.

Knowledge You Can Act On: Optimal Policies for Assembly Systems with Expediting and Advance Demand Information

We consider a nonstationary, stochastic, multi-stage supply system with a general assembly structure, in which customers can place orders in advance of their future demand requirements. This advance demand information is now recognized in both theory and practice as an important strategy for managing the mismatch between supply and demand. In conjunction, we allow expediting of components and partially completed subassemblies in the system in order to provide the supply chain with the means to manage the stockout risk and significantly enhance cost savings realized through advance demand information. To solve the resulting assembly system, we develop a new method based on identifying local properties of optimal decisions. This new method allows us to solve assembly systems with multiple product flows. We derive the structure of the optimal policy, which represents a double-tiered echelon basestock policy whose basestock levels depend on the state of advance demand information. This form of the optimal policy allows us to: (i) provide actionable policies for firms to manage large-scale assembly systems with expediting and advance demand information; (ii) prove that advance demand information and expediting of stock both reduce the amount of inventory optimally held in the system; and (iii) numerically solve such assembly systems, and quantify the savings realized. In contrast to the conventional wisdom, we discover that advance demand information and expediting of stock are complementary under short demand information horizons. They are substitutes only under longer information horizons.

Price discount and capacity planning under demand postponement with opaque selling

In this paper, we consider the opaque selling strategy of a firm that uses a price discount to induce demand postponement. Under demand postponement, the firm offers a price discount to advance customers in exchange for the option to fulfill their orders after the spot demand has been satisfied. Advance customers who take the discount commit their orders early, but the actual delivery time is chosen by the firm. In effect, the price discount enables the firm to create a capacity buffer for the spot demand. We formulate a two-stage stochastic program, and characterize the firm's optimal capacity and price discount decisions to maximize its expected profit. We find that the driver of demand postponement is that the option to postpone allows the firm to not only use less safety stock to hedge against the risk in the spot demand, but also reduce capacity waste. In addition, the firm might gain from the potentially lower capacity cost for postponed demand. In the event that the advance demand information can be utilized to update the regular demand distribution, the firm can garner additional benefits from information updating through the early orders. Through numerical experiments, we demonstrate the significance of the value of demand postponement and information updating, and assess the impact of market conditions on the firm's optimal capacity and price discount decisions.

Special issue: emerging technologies for intelligent transportation

Special issue: emerging technologies for intelligent transportation

Advance Demand Information in a Multiproduct System

In this paper we examine the impact of different types of advance demand information on firm profit and on the benefits of resource flexibility. Specifically, we consider a firm that must choose capacities of resources that will be used to satisfy stochastic demand for multiple products, where demands follow a multivariate normal distribution. Prior to the capacity decision, the firm receives information revealing either the total volume of demand across products or the mix of demand between products. We examine two different scenarios: a dedicated resource setting with product-specific resources and a common resource scenario with one flexible resource. For both scenarios we derive the distribution of the (possibly imperfect) volume or mix demand signal, as well as the conditional distributions of demand given the particular signal. We explore the impact of either type of information on optimal capacities and profit. We find that commonality and volume information are strategic complements—so that it is more valuable to obtain volume information in settings with a common resource. On the other hand, commonality and mix information are strategic substitutes. Moreover, we find that mix and volume information themselves are complements in systems with dedicated resources. Having either type of information is valuable in reducing uncertainty for each individual product demand, but having both of them together provides information on two different dimensions, allowing for a much greater reduction in demand uncertainty. In systems with a common resource, however, the two types of information are substitutes. Because volume information is well aligned with commonality (both focus on total demand), such information already provides much of the value that can be obtained—having mix information adds limited additional value.

Evaluation of the Impact of Uncertain Advance Demand Information on Production/Inventory Systems

In this paper, we conduct a simulation study to evaluate the impact of using imperfect advance demand information (ADI) in a single stage production/inventory system. We mean by ADI the fact that the production system receives customer orders in advance of their due-dates. We consider four types of ADI: 1) Perfect ADI, 2) ADI with imperfect due-dates, 3) ADI with imperfect demand quantities and 4) ADI with updates. The production system is controlled by a modified base-stock policy which has two parameters that are S, the base-stock level, and L, the production release lead time. We intend to shed light on the impact of imperfect ADI (ADI types 2, 3, 4) on the performance of production/inventory systems. The aim of the simulation study is twofold. Firstly, the objective is to find the optimal parameters of the modified base-stock policy and secondly, to evaluate the benefits of imperfect ADI in a production/inventory system.

The impact of Advance Demand Information on the Performance of production/inventory systems

In this paper, we evaluate the effectiveness of using Advance Demand Information (ADI) in production systems. More specifically, a supplier-customer relationship from the supplier's perspective is studied where we integrate the value of Advance Demand Information provided by the customer. Hence, ADI is in the form of customer firm orders that are transmitted to the supplier in advance of customer's future demand requirements. In this paper, we study a production system, with a single product, finite capacity and constant production times where the policy used is a base stock based on ADI. Additionally, it is assumed that ADI is perfect which means that the quantity and the due date of the order transmitted by the customer to the supplier is deterministic and do not change over time. We perform a discrete-time simulation model to evaluate the impact of using ADI on the considered production system. Moreover, we conduct a sensitivity analysis to examine the effect of capacity and demand variability on the production system. We observe that a base-stock policy in conjunction with ADI may increase the supplier efficiency by reducing the total cost of inventory and backorder costs. The rate of total cost reduction is a function of demand variability and production capacity.

Supply Allocation Under Sequential Advance Demand Information

We study the problem of allocating supply under advance demand information (ADI). We consider a company that must allocate limited inventory to different markets that open sequentially. To reduce uncertainty, the company receives advance demand information and updates forecasts about its markets each time it makes an allocation decision. We study the value and optimal use of this information. Our research is motivated by an agri-food manufacturer that operates in several European countries. We develop the optimal policy under relaxed conditions and an efficient heuristic policy that performs close to optimal under general conditions. We derive structural properties of the model to gain managerial insights, and we derive the optimal policy in closed-form for the case of markets with identical prices. We use numerical experiments to demonstrate that the value of ADI can be significant. The managerial insights of this study include the observations that in environments as the one that motivated our research, early markets receive systematically less supply than late markets and that the value of ADI is greatest if the initial supply is close to the initial forecasts.

Experimental study on using advance demand information to mitigate the bullwhip effect via decentralised negotiations

This paper analyses experiment results to show that advance demand information (ADI) strategies have the potential to counteract the bullwhip effect and reduce supply chain costs in practice. The experiments are conducted with a new web-based interactive computer simulation game, where human players assuming the roles of suppliers not only make replenishment order decisions but also negotiate for agreements on ADI provision, consumption, and compensation. The game is designed with enhanced information provision and communication channels through which negotiations are conducted strictly between neighbouring suppliers. As such, the supply chain is kept decentralised (i.e. no information is shared across supply chain stages). The effectiveness of ADI strategies is tested in a variety of settings; e.g. when supply chain parameters are heterogeneous, when suppliers have (or do not have) partial knowledge on the customer demand, and when suppliers are inexperienced with the ADI strategy. It is observed repeatedly that the ADI negotiation mechanism significantly improves the supply chain performance, reducing not only the total chain-wide cost but also every individual supplier's cost. This improvement is generally achieved by the downstream suppliers (e.g. retailer) making future order commitments (while carrying higher inventory), which allows upstream suppliers to gain sufficient cost reduction and provide compensation to the downstream suppliers.

Willingness to Pay for Shifting Inventory Risk: The Role of Contractual Form

In order to reduce their inventory risk, firms can attempt to contract with their suppliers for shorter supply lead‐times, with their buyers for longer demand lead‐times, or both. We designed a controlled laboratory experiment to study contracts that shift a focal firm's inventory risk to its supply chain partners and address two questions. First, is it more effective if the cost of shifting inventory risk is framed as a fixed fee or in per‐unit cost terms? We find that, generally, our participants are willing to pay more to avoid supply–demand mismatches than the expected costs from such mismatches. This tendency to overpay is mitigated under fixed fee schemes. Second, does it matter whether the option to reduce inventory risk is the outcome of either increased responsiveness from the upstream supplier or advanced demand information from the downstream buyer? Our results suggest that this difference, when only a matter of framing, has no significant effect on willingness‐to‐pay.

A comparison of make-to-stock and make-to-order in multi-product manufacturing systems with variable due dates

This article models a single-stage hybrid production system, which can be regarded as a Make To Order (MTO) production system with safety stocks or a Make To Stock (MTS) production system with advance demand information. In an environment with multiple products and variable customer due dates, optimality conditions for safety stocks (base stocks) and safety lead times (work-ahead window) that minimize inventory and backorder costs are derived. For a simplified M/M/1 system with exponentially distributed customer required lead time, an explicit comparison between MTO and MTS is conducted. A pure MTO policy gets relatively more favorable to a pure MTS policy if inventory holding costs increase, backorder costs decrease, the mean customer required lead time increases, or the processing rate increases. In a numerical study, the influence of variance, the behavior of optimal parameters, and the cost reduction potential of this hybrid policy are shown.

The Promise of Strategic Customer Behavior: On the Value of Click Tracking

Click tracking is gaining in popularity, and the practice of web analytics is growing fast. Whether strategic customers are willing to visit a website when they know their clicks may be tracked is an important yet complex problem, which depends on various factors. Using a newsvendor framework, we examine this problem by focusing on the operational factor: how product availability induces strategic customers to voluntarily provide advance demand information. We find that a strong Nash equilibrium exists where every customer is willing to click, and customer incentives to click are robust to noise. Hence, we demonstrate the promise of strategic customer behavior in the context of click tracking, contrary to the conventional wisdom that it is typically a peril for the firm. Notably, click tracking is typically advantageous to both the firm and its customers, compared with other strategies such as advance selling, quantity commitment, availability guarantees, and quick response. Lastly, we extend to two settings by including marketing decisions, price‐sensitive demand and markdown pricing, and discuss how operations and marketing decisions interact in influencing the value of click tracking.

Advance Demand Information, Price Discrimination, and Preorder Strategies

This paper studies the preorder strategy that a seller may use to sell a perishable product in an uncertain market with heterogeneous consumers. By accepting preorders, the seller is able to obtain advance demand information for inventory planning and price discriminate the consumers. Given the preorder option, the consumers react strategically by optimizing the timing of purchase. We find that accurate demand information may improve the availability of the product, which undermines the seller's ability to charge a high preorder price. As a result, advance demand information may hurt the seller's profit due to its negative impact for the preorder season. This cautions the seller about a potential conflict between the benefits of advance demand information and price discrimination when facing strategic consumers. A common practice to contain consumers' strategic waiting is to offer price guarantees that compensate preorder consumers in case of a later price cut. Under price guarantees, the seller will reduce price in the regular season only if the preorder demand is low; however, such advance information implies weak demand in the regular season as well. This means that the seller can no longer benefit from a high demand in the regular season. Therefore, under price guarantees, more accurate advance demand information may still hurt the seller's profit due to its adverse impact for the regular season. We also investigate the seller's strategy choice in such a setting (i.e., whether the preorder option should be offered and whether it should be coupled with price guarantees) and find that the answer depends on the relative sizes of the heterogeneous consumer segments.

Use of advance demand information in multi-stage production-inventory systems with multiple demand classes

This paper considers three-stage production-inventory systems serving two customer classes, where only one class provides advance demand information and early order fulfilment is acceptable. We propose a new approach for production replenishment and order fulfilment in such systems that uses advance demand information for performance improvement. The approach combines the benefits of early fulfilment and Kanban-based pull systems. Simulation is used to establish the performance of the resulting policy vs. two existing policies, for a variety of production scenarios and cost structures. A lower bound on total cost is also established using a simulation-based procedure. In general, early fulfilment with one-for-one replenishment is shown to provide greater benefit than making items to order. Neither of the existing policies, however, was found to use advance demand information as effectively as the proposed approach, which outperformed the existing policies in every case considered. Additionally, the proposed policy has the advantage of both retaining its benefit at high levels of system utilisation and increasing benefit up to the maximum level of advance demand information provided.

A framework to engineer production control strategies and its application in electronics manufacturing

One of the most challenging tasks of companies with complex and discrete production systems is to determine an appropriate production control strategy (PCS). The PCS is crucial, as it influences inventory, costs, and service. We propose a framework to address the most important questions of PCS engineering: limiting work in process, positioning the order penetration point (OPP), and coping with demand uncertainty upstream the OPP. Thereby, uncertain advance demand information in the form of forecasts is considered and desirable characteristics of push and pull strategies are combined. A serial manufacturing system with multiple products, variable processing times, and uncertain advance demand information is examined. The model is formulated as a queuing network model and solved numerically by discrete-event simulation. The framework and a resulting hybrid control strategy are applied to a case study from electronics manufacturing, where a significant improvement potential is identified.

Inventory Control with Advance Demand Information When Demand is Intermittent

In this paper we study a manufacturer-retailer relationship from the perspective of the manufacturer. The problem is managing the inventory of the manufacturer when Advance Demand Information (ADI) is available from the retailer. ADI allows the retailer to signal the manufacturer that there will be a demand, of the signaled size, in a future period. This is coupled with the situation where the manufacturer gets intermittent orders from the retailer. This intermittence may be because of the ordering policy of the retailer, or the nature of the product. Because of this demand structure the manufacturer's replenishment policy is assumed to be a “High-Low” base stock policy. A parsimonious Markov chain model of the intermittent demand with ADI is presented. We observe that a high-low policy is less costly than a one-parameter base-stock policy and that ADI increases the supply chain efficiency by reducing the cost of the manufacturer.

The Promise of Strategic Customer Behavior: On the Value of Click Tracking

Click tracking is gaining in popularity and the practice of web analytics is growing fast. Whether strategic customers are willing to visit a website when they know their clicks may be tracked is an important yet complex problem which depends on various factors. Using a newsvendor framework, we examine this problem by focusing on the operational factor: how product availability induces strategic customers to voluntarily provide advance demand information. We find that a strong Nash equilibrium exists where every customer is willing to click, and customer incentives to click are robust to noise. Hence, we demonstrate the promise of strategic customer behavior in the context of click tracking, contrary to the conventional wisdom that it is typically a peril for the firm. Notably, click tracking is typically advantageous to both the firm and its customers, compared with other strategies such as advance selling, quantity commitment, availability guarantees, and quick response. Lastly, we extend to two settings by including marketing decisions: price-sensitive demand and markdown pricing, and discuss how operations and marketing decisions interact in influencing the value of click tracking.

The Value of Clickstream Tracking: Advance Demand Information, Product Personalization and Personalized Pricing

Motivated by the fast growing practice of web analytics generated by the Internet clickstream tracking technology, I study the value of using this technology to collect advance demand information (ADI) and advance preference information (API). API can be used for product personalization and price personalization. I show that collecting ADI and conducting product personalization simultaneously typically generates significant complementarity effects. However, when consumers are strategic in markdown pricing settings, such complementarity benefit can be weakened by strategic consumer behavior and they may become substitutes. These findings provide managerial guidelines regarding how different firms (e.g., Amazon.com and Burberry) should use clickstream tracking in collecting ADI and API. I also show that ADI and price personalization may be complements: consumers are willing to reveal more accurate preference information in the presence of endogenous availability. Moreover, such endogenous availability may make price discrimination based on click behavior profitable. My results unearth the value of collaboration between Operations and Marketing in the context of clickstream tracking.

A Two Stage Solution Procedure for Production Planning System with Advance Demand Information

A Two Stage Solution Procedure for Production Planning System with Advance Demand Information