Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.Manufacturing;Revenue Management;Software;Advanced Planning Systems;Demand Fulfillment

Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs

A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company

[EN] Order promising in manufacturing systems that produce non-uniform units of the same finished good becomes a more complex process when customer orders need to be served with homogeneous units. To facilitate this task, we propose a mathematical model-based decision tool to support the order promising process according to product homogeneity requirements in hybrid Make-To-Stock (MTS) and Make-To-Order (MTO) contexts. In these manufacturing environments, the comparison of Available-To-Promise (ATP) and/or Capable-To-Promise (CTP) quantities with homogeneous ones ordered by customers is necessary during the order commitment. To properly deal with customers' product uniformity requirements, different ATP consumption rules are implemented by defining a novel objective function. CTP modelling in these systems also entails having to address new aspects, such as estimating future homogeneous quantities in additional lots to the master plan, accomplishing minimum lot sizes and saving in setups when programming new lots. By including CTP in the order promising model, a closer integration with the master production schedule is achieved. The resulting mathematical model was applied to a ceramic tile company in different supply scenarios and execution modes, and at several availability levels (ATP and ATP&CTP). The results validate model performance and provide insights into the impact of ATP consumption rules on the profits made from committed customer orders in different scenarios for the specific ceramic tile company.This work was supported by the Spanish Ministry of Economy and Competitiveness with Grant DPI2011-23597 and the Universitat Polito cnica de Valencia with Grant Ref. PAID-06-11/1840.Alemany Díaz, MDM.; Ortiz Bas, Á.; Fuertes-Miquel, VS. (2018). A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers & Industrial Engineering. 122:219-234. https://doi.org/10.1016/j.cie.2018.05.040S21923412

A Digital Twin Framework for Production Planning Optimization: Applications for Make-To-Order Manufacturers

In this dissertation, we develop a Digital Twin framework for manufacturing systems and apply it to various production planning and scheduling problems faced by Make-To-Order (MTO) firms. While this framework can be used to digitally represent a particular manufacturing environment with high fidelity, our focus is in using it to generate realistic settings to test production planning and scheduling algorithms in practice. These algorithms have traditionally been tested by either translating a practical situation into the necessary modeling constructs, without discussion of the assumptions and inaccuracies underlying this translation, or by generating random instances of the modeling constructs, without assessing the limitations in accurately representing production environments. The consequence has been a serious gap between theory advancement and industry practice. The major goal of this dissertation is to develop a framework that allows for practical testing, evaluation, and implementation of new approaches for seamless industry adoption. We develop this framework as a modular software package and emphasize the practicality and configurability of the framework, such that minimal modelling effort is required to apply the framework to a multitude of optimization problems and manufacturing systems. Throughout this dissertation, we emphasize the importance of the underlying scheduling problems which provide the basis for additional operational decision making. We focus on the computational evaluation and comparisons of various modeling choices within the developed frameworks, with the objective of identifying models which are both effective and computationally efficient. In Part 1 of this dissertation, we consider a class of Production Planning and Execution problems faced by job shop manufacturing systems. In Part 2 of this dissertation, we consider a class of scheduling problems faced by manufacturers whose production system is dominated by a single operation

Management of Stochastic Demand in Make-to-Stock Manufacturing

Up to now, demand fulfillment in make-to-stock manufacturing is usually handled by advanced planning systems. Orders are fulfilled on the basis of simple rules or deterministic planning approaches not taking into account demand fluctuations. The consideration of different customer classes as it is often done today requires more sophisticated approaches explicitly considering stochastic influences. This book reviews current literature, presents a framework that addresses revenue management and demand fulfillment at once and introduces new stochastic approaches for demand fulfillment in make-to-stock manufacturing based on the ideas of the revenue management literature

Collaborative Procurement and Due Date Management in Supply Chains

In this thesis we analyze the procurement process of buyers and supply decisions of manufacturers. Companies are looking for ways to decrease their procurement costs, which account for a large percentage of the supply chain costs. We study the effects of demand aggregation and collaborative procurement on buyers' profitability. First, we make a high-level analysis and consider a market with multiple buyers and suppliers where multi-unit transactions for multiple items take place. The procurement costs are effected by economies of scale in the suppliers' production costs and by economies of scope in transportation. We design buyer strategies that model different collaboration levels and assess the role of collaboration under varying market conditions. Next, we analyze the procurement process at a lower level and identify benefits of inter-firm collaboration among buyers who are potential competitors in the end market. We adopt a game-theoretic approach to explore the economics of the basic mechanism underlying collaborative procurement, and determine the conditions that makes it beneficial for the participants. Besides low procurement costs, important considerations in supplier selection are responsiveness and the reliability of the suppliers in meeting demand. Hence, manufacturers face the pressure for quoting short and reliable lead times. We cover several aspects of the manufacturer's problem, such as quoting reliable due-dates based on current workload in the system, maximizing profit considering the lateness cost incurred due to late deliveries, and deciding on the level of inventory to increase responsiveness. We employ a model where demand arrival and manufacturing processes are stochastic, and obtain insights on the optimal due-date quotation policy and on the optimal inventory level.Ph.D.Committee Chair: Keskinocak, Pinar; Committee Co-Chair: Griffin, Paul M.; Committee Member: Ammons, Jane C.; Committee Member: Ayhan, Hayriye; Committee Member: Chang, Yih-Lon

Lead-Time Quotation When Customers are Sensitive to Reputation

Firms consider a variety of factors when making lead-time promises, including current shop status and the size of the incoming order. The profit-maximising model presented in this paper is the first to include reputation effects explicitly in a lend-time optimisation model. Reputation is considered to be the lasting effect on the market of a firm\u27s delivery performance over time, and so it affects the future as well as the current profits. The model is complicated, and a counter-example demonstrates that qualitative monotonicity results are not obtainable. A computational study explores the relationships between shop status, order size, reputation, market characteristics and the lead-time decision. Regression analysis sheds light on these relationships and suggests three heuristics, which provide near-optimal solutions with relatively short running times

An agent-based heuristics optimisation model for production scheduling of make-to-stock connector plates manufacturing systems

The manufacturing systems' success directly relates to their accurate, reliable and flexible schedules, including how production is planned and scheduled and which constraints are considered in generating the schedules. The study's objective arises from the need to generate an optimal production scheduling system in a connecting plates manufacturing company that works on a Make-To-Stock basis. This research investigates the impact of demand and operational constraints on production schedules, including the facility capacity, operators and machines availability, raw materials availability, inventory level and warehouse capacity. A multi-agent-based optimisation model is developed to face the complexity of considering demand and operational constraints and reflects their impact on generating a reliable production schedule. This model involves a proposed heuristic algorithm that considers demand and operations constraints in such a manufacturing environment and optimises the production schedule based on these restrictions/requirements. A real-life case study based on a connecting plates manufacturer company is used as a test bench of the proposed agent-based heuristic optimisation model. The proposed algorithm is compared with other related approaches to check its superiority based on key criteria, including inventory levels, missed/unsatisfied orders and total production time. Results show that the proposed heuristics algorithm reduced the number of missed orders by 34% compared with similar approaches

Integrated Production and Distribution planning of perishable goods

Tese de doutoramento. Programa Doutoral em Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201