Order Acceptance Policy Research Articles

Integrated order acceptance and inventory policy optimization in a multi-period, multi-product hybrid production system

Integrated order acceptance and inventory policy optimization in a multi-period, multi-product hybrid production system

Advancing the marketing‐operations interface in omnichannel retail

Advancing the marketing‐operations interface in omnichannel retail

Order Fulfillment Under Pick Failure in Omnichannel Ship-From-Store Programs

Problem definition: We consider the setting where a retailer with many physical stores and an online presence seeks to fulfill online orders using an omnichannel fulfillment program, such as buy-online ship-from-store. These fulfillment strategies try to minimize cost while fulfilling orders within acceptable service times. We focus on single-item orders. Typically, all online orders for the item are sent to a favorable set of locations to be filled. Failed trials are sent back for further stages of trial fulfillment until the process times out. The multistage order fulfillment problem is thus an interplay of the pick-failure probabilities at the stores where they may be shipped from and the picking, shipping, and cancellation costs from these locations. Methodology: We model the problem as one of sequencing the stores from which an order is attempted to be picked and shipped in the most cost-effective way over multiple stages. We solve the fulfillment problem optimally by taking into account the changing pick-failure probabilities as a result of other online order fulfillment trials by casting it as a network flow problem with convex costs. We incorporate this as the second stage of a two-stage online order acceptance problem and generalize earlier results to the case with pick failures at stores. Results: We investigate the real-world performance of our methods and models on real order data of several of the top U.S. retailers that use our collaborating e-commerce solutions provider to optimize their fulfillment strategies. Academic/Practical Relevance: Our work enables retailers to incorporate pick failure in their order management systems for ship-from-store programs. Our new online order-acceptance policies that take into account pick failures can thus create significant savings for omnichannel retailers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1164 .

Tactical-operational coordination of a divergent production system with coproduction: the sawmilling challenge

Various optimization tools have been used in industry to facilitate production planning at different levels of aggregation. Choosing the interoperability mechanisms of these systems, such as the planning frequencies, the information passed between them and the interpretation that other systems must make of them, has always been a challenge. This work focusses on production planning at the tactical and operational levels in North American sawmills, a commodity industry characterized by volatile prices and a divergent production process with coproduction. In this context, tactical planning produces aggregated plans, and information from these plans can be used as targets and/or constraints at the operational level (e.g., quantities to be produced/kept in stock per product and per period, sales targets, etc.). A simulation of this production system was therefore developed, encompassing the planning process and the market dynamic, to compare and evaluate the impact of different coordination approaches on business economic performance. Results showed that the type of information which should be shared from the tactical level to the operational level varies according to several factors, including the company’s order acceptance policy, price seasonality, and the presence or absence of overcapacity on the market. Highlights A simulation approach is used to evaluate coordination between tactical and operational planning The context of North American sawmills is the one investigated The production and the planning process as well as the market behavior is considered Results show that the information shared between the two levels impact the income The order acceptance policy chosen also has an influence on the revenue generated

Effective Online Order Acceptance Policies for Omni-Channel Fulfillment

Effective Online Order Acceptance Policies for Omni-Channel Fulfillment

Evaluating order acceptance policies for divergent production systems with co-production

The impacts of using different order acceptance policies in manufacturing sectors are usually well known and documented in the literature. However, for industries facing divergent processes with co-production (i.e. several products produced at the same time from a common raw material), the evaluation, comparison and selection of policies are not trivial tasks. This paper proposes a framework to enable this evaluation. Using a simulation model that integrates a custom-built ERP, we compare and evaluate different order acceptance policies in various market conditions. Experiments are carried out using a case from the forest products industry. Results illustrate how and when different market conditions related to divergent/co-production industries may call for available-to-promise (ATP), capable-to-promise (CTP), and other known strategies. Especially, we show that advanced order acceptance policies like CTP may generate a better income for certain types of market and, conversely to typical manufacturing industries, ATP performs better than other strategies for a specific demand patterns.

Order Admission and Optimal Pricing for Regular Jobs and Big Deals at a Service Company

This paper studies optimal pricing and demand management policies for a firm that faces two streams of order types: one is composed of recurring regular jobs with pre-determined prices (exogenous prices) and the other involves big deals that require pricing proposals (endogenous prices). The probability to secure the big deals diminishes with the quoted price. The authors develop and compare optimization models for different demand management settings. Specifically, we consider two distinct strategies: a pure strategy in which the firm commits to bid for deals only and a mixed strategy where the firm switches its allocation of capacity between regular jobs and deals. The authors compare optimal pricing strategies under two demand management strategies that differ in how they allocate capacity across regular jobs and deals, and order acceptance policies that they adopt. The authors observe that the differences between two strategies in terms of pricing and average gain are in accord. Under any given set of systems parameters, one of the strategies leads to both higher prices and average gains. Typically, the preferable strategy depends on the exogenous price of regular orders and the price sensitivity of the deals. The authors conclude that the threshold values for these two parameters are determined primarily by the demand rate of the deals and the service rate of the standard jobs.

Influence of order acceptance policies on optimal capacity investment with stochastic customer required lead times

Abstract The influence of applying queue state dependent order acceptance policies, where either decision is with customer or with manufacturer, on optimal capacity investment is discussed. Therefore, three order acceptance policies are developed where either the customer has a certain service level threshold for each order or the manufacturer has an overall service level threshold. The third policy, modeling queue state independent order acceptance, is used to identify performance gains of including queue state knowledge into this decision. Equations for state probabilities, order acceptance rate, work-in-process, finished-goods-inventory, backorders and service level are developed for a system with stochastic customer-required lead times applying queuing methodology. An optimization problem minimizing capacity, work-in-process, finished-goods-inventory, backorder and lost sales cost (for rejected orders) in a single stage MTO production system is presented. The system is modeled as an M/M/1 queue with input rates depending on queue length and random customer required lead time. For the optimization problem, which cannot be solved explicitly, a solution heuristic is developed and a broad numerical study is conducted. The numerical study shows that allowing the customer to know the expected production lead time and—based on this knowledge—decide whether or not to place an order can have positive or negative influences on the overall costs, depending on the customer's service level target. Furthermore, the study shows that a high cost reduction potential exists for simultaneously optimizing capacity investment and order acceptance policy if the production system can decide whether or not to accept an order.

Revenue management for operations with urgent orders

This article is motivated by the case of a company manufacturing industrial equipment that faces two types of demand: on the one hand there are the so-called regular orders for installations or refurbishing of existing facilities, these orders have a relatively long lead time; on the other hand there are urgent orders mostly related to spare parts when a facility has a breakdown, the delay in such case is much shorter but higher margins can be obtained. We study the order acceptance problem for a firm that serves two classes of demand over an infinite horizon. The firm has to decide whether to accept a regular order (or equivalently how much capacity to set aside for urgent orders) in order to maximize its profit. We formulate this problem as a multi-dimensional Markovian Decision Process (MDP). We propose a family of approximate formulations to reduce the dimension of the state space via aggregation. We show how our approach can be used to compute bounds on the profit associated with the optimal order acceptance policy. Finally, we show that the value of revenue management is commensurate with the operational flexibility of the firm.

Order acceptance policy in Make-to-Order manufacturing based on average-reward reinforcement learning

From the perspective of revenue management,a new approach for order acceptance under uncertainty in Make-to-Oder(MTO) manufacturing using average-reward reinforcement learning was proposed.In order to maximize the average expected revenue,the proposed approach took order types and different combinations of price and leadtime as criteria for the classification of the system states based on multi-level pricing mechanism.The simulation results show that the proposed algorithm has learning and selective ability to accept the order.Comparisons made with other order acceptance policies show the effectiveness of the proposed algorithm in average revenue,accepted order types,and adaptability.

Order acceptance and scheduling policies for a make-to-order environment with family-dependent lead and batch setup times

In many make-to-order production situations with batch setup times, customer orders are grouped into family-dependent batches to limit the loss of capacity due to setups. These batches, however, cannot be too large, since the make-to-order character requires that orders have to be produced in time. This trade-off between setup time efficiency and due-date adherence creates a challenging scheduling problem referred to in the literature as the Customised Stochastic Lot Scheduling Problem. Typically, suppliers reduce the complexity of the production problem by quoting lead times that are equal for all customer families. This choice, however, is in many cases too restrictive. In this paper, we show quantitatively by means of Markov decision processes (MDPs) that using family-dependent lead times can result in a significant gain in profit as compared with using standard lead times. We develop a simple heuristic acceptance/scheduling policy, and demonstrate that this heuristic performs very well compared with the optimal policy of the MDP for a wide range of parameters.

Managing an Available-to-Promise Assembly System with Dynamic Short-Term Pseudo-Order Forecast

We study an order promising problem in a multiclass, available-to-promise (ATP) assembly system in the presence of pseudo orders. A pseudo order refers to a tentative customer order whose attributes, such as the likelihood of an actual order, order quantity, and confirmation timing, can change dynamically over time. A unit demand from any class is assembled from one manufactured unit and one inventory unit, where the manufactured unit takes one unit of capacity and needs a single period to produce. An accepted order must be filled before a positive delivery lead time. The underlying order acceptance decisions involve trade-offs between committing resources (production capacity and component inventory) to low-reward firm orders and reserving resources for high-reward orders. We develop a Markov chain model that captures the key characteristics of pseudo orders, including demand lumpiness, nonstationarity, and volatility. We then formulate a stochastic dynamic program for the ATP assembly system that embeds the Markov chain model as a short-term forecast for pseudo orders. We show that the optimal order acceptance policy is characterized by class prioritization, resource-imbalance-based rationing, and capacity-inventory-demand matching. In particular, the rationing level for each class is determined by a critical value that depends on the resource imbalance level, defined as the net difference between the production capacity and component inventory levels. Extensive numerical tests underscore the importance of the key properties of the optimal policy and provide operational and managerial insights on the value of the short-term demand forecast and the robustness of the optimal policy. This paper was accepted by Martin Lariviere, operations management.

Management Insights

Management Insights

Simulation-based tool to analyse the effect of order acceptance policy in a make-to-order manufacturing system

Order acceptance decision is an interface between the customer relationship management and the production planning activities of the manufacturing system. The decision of accept/reject an order is a critical decision in an uncertain market environment. This paper proposes an order acceptance policy in a market with three order profit classes and volume uncertainty. The policy proposed is integrated with production planning activity; the policy is used to take the decision of accept/rejection using the information provided by the production planning model. A simulation environment based on an open source tool JAVA has been developed to test the proposed methodology in several environment conditions. The simulation results show the robustness of the proposed approach and provide the information to set the threshold value of the proposed approach.

Learning and adaptation of a policy for dynamic order acceptance in make-to-order manufacturing

Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel approach for simulation-based development and on-line adaptation of a policy for dynamic order acceptance under uncertainty in make-to-order manufacturing using average-reward reinforcement learning is proposed. Locally weighted regression is used to generalize the gain value of accepting or rejecting similar orders regarding attributes such as product mix, price, size and due date. The order acceptance policy is learned by classifying an arriving order as belonging either to the acceptance set or to the rejection set. For exploitation, only orders in the acceptance set must be chosen for shop-floor scheduling. For exploration some orders from the rejection set are also considered as candidates for acceptance. Comparisons made with different order acceptance heuristics highlight the effectiveness of the proposed ARLOA algorithm to maximize the average revenue obtained per unit cost of installed capacity whilst quickly responding to unknown variations in order arrival rates and attributes.

Scheduling and order acceptance for the customised stochastic lot scheduling problem

This paper develops and analyses several customer order acceptance policies to achieve high bottleneck utilisation for the customised stochastic lot scheduling problem (CSLSP) with large setups and strict order due dates. To compare the policies, simulation is used as the main tool, due to the complicated nature of the problem. Also, approximate upper and lower bounds for the utilisation are provided. It is shown that a greedy approach to accept orders performs poorly in that it achieves low utilisation for high customer order arrival rates. Rather, good acceptance and scheduling policies for the CSLSP should sometimes reject orders to create slack even when there is room in the schedule to start new production runs. We show that intelligently introducing slack enables the achievement of high utilisation. One particularly simple policy is to restrict the number of families present.

The Usage of Opportunity Cost to Maximize Performance in Revenue Management*

ABSTRACTTo meet customer requirements efficiently, a manager needs to supply adequate quantities of products, capacity, or services at the right time with the right prices. Revenue management (RM) techniques can help firms use differential pricing strategies and capacity allocation tactics to maximize revenue. In this article, we propose a marginal revenue‐based capacity management (MRBCM) model to manage stochastic demand in order to create improved revenue opportunities. The new heuristic employs opportunity cost estimation logic that is unique and is the reason for the increased performance. The MRBCM model generates order acceptance policies that allocate available capacity to higher revenue generating market segments in both service and manufacturing environments. To evaluate these models, we design and conduct simulation experiments for 64 scenarios using a wide range of operating conditions. The experimental results show that the MRBCM model generates significantly higher revenues over the first come, first served rule when capacity is tight. In addition, we also show that the MRBCM model generally performs better than a recent RM model published in the literature.

Inventory control with Markovian capacity and the option of order rejection

We consider a stochastic inventory control problem with Markovian capacity and the option of order rejection. We show its optimal policy to be the combination of a capacity-dependent modified base-stock production policy and a capacity-dependent critical-point order acceptance policy. When capacity is stochastically monotone, we show that the policy parameters change over the current capacity level in an intuitive way. All these results can be extended to the infinite-horizon case, with or without cost discounting. Our computational study substantiates the benefits from both exploiting the Markovian behavior of the capacity and rejecting orders when necessary.

Makespan estimation and order acceptance in batch process industries when processing times are uncertain

Batch process industries are characterized by complex precedence relationships between operations, which renders the estimation of an acceptable workload very difficult. A detailed schedule based model can be used for this purpose, but for large problems this may require a prohibitive large amount of computation time. We propose a regression based model to estimate the makespan of a set of jobs. We extend earlier work based on deterministic processing times by considering Erlang-distributed processing times in our model. This regression-based model is used to support customer order acceptance. Three order acceptance policies are compared by means of simulation experiments: a scheduling policy, a workload policy and a regression policy. The results indicate that the performance of the regression policy can compete with the performance of the scheduling policy in situations with high variety in the job mix and high uncertainty in the processing times.