Using Frontier Portfolios to Improve Make‐to‐Order Operations

Make‐to‐order (MTO) manufacturers face a common problem of maintaining a desired service level for delivery at a reasonable cost while dealing with irregular customer orders. This research considers a MTO manufacturer who produces a product consisting of several custom parts to be ordered from multiple suppliers. We develop procedures to allocate orders to each supplier for each custom part and calculate the associated replenishment cost as well as the probability of meeting the delivery date, based on the suppliers' jobs on hand, availability, process speed, and defective rate. For a given delivery due date, a frontier of service level and a replenishment cost frontier are created to provide a range of options to meet customer requirements. This method can be further extended to the case when the delivery due date is not fixed and the manufacturer must “crash” its delivery time to compete for customers.     

Integrated Order Scheduling and Packing

We consider an integrated production–distribution scheduling model in a make‐to‐order supply chain consisting of one supplier and one customer. The supplier receives a set of orders from the customer at the beginning of a planning horizon. The supplier needs to process all the orders at a single production line, pack the completed orders to form delivery batches, and deliver the batches to the customer. Each order has a weight, and the total weight of the orders packed in a batch must not exceed the capacity of the delivery batch. Each delivery batch incurs a fixed distribution cost. The problem is to find jointly a schedule for order processing and a way of packing completed orders to form delivery batches such that the total distribution cost (or equivalently, the number of delivery batches) is minimized subject to the constraint that a given customer service level is guaranteed. We consider two customer service constraints—meeting the given deadlines of the orders; or requiring the average delivery lead time of the orders to be within a given threshold. Several problems of the model with each of those constraints are considered. We clarify the complexity of each problem and develop fast heuristics for the NP‐hard problems and analyze their worst‐case performance bounds. Our computational results indicate that all the heuristics are capable of generating near optimal solutions quickly for the respective problems.     

TIME‐BASED PRICING AND LEADTIME POLICIES FOR A BUILD‐TO‐ORDER MANUFACTURER

We studied time‐based policies on pricing and leadtime for a build‐to‐order and direct sales manufacturer. It is assumed that the utility of the product varies among potential customers and decreases over time, and that a potential customer will place an order if his or her utility is higher than the manufacturer's posted price. Once an order is placed, it will be delivered to the customer after a length of time called “leadtime.” Because of the decrease in a customer's utility during leadtime, a customer will cancel the order if the utility falls below the ordering price before the order is received. The manufacturer may choose to offer discounted prices to customers who would otherwise cancel their orders. We discuss two price policies: common discounted price and customized discounted price. In the common discounted price policy, the manufacturer offers a single lower price to the customers; in the customized discounted price policy, the manufacturer offers the customers separately for individual new prices. Our analytical and numerical studies show that the discounted price policies results in higher revenue and that the customized discounted price policy significantly outperforms the common discounted price policy when product utility decreases rapidly. We also study two leadtime policies when production cost decreases over time. The first uses a fixed leadtime, and the second allows the leadtime to vary dynamically over time. We find that the dynamic leadtime policy significantly outperforms the fixed leadtime policy when the product cost decreases rapidly.     

Pareto‐Improving Contracts for Express Package Delivery Services

We address the problem of an express package delivery company in structuring a long‐term customer contract whose terms may include prices that differ by day‐of‐week and by speed‐of‐service. The company traditionally offered speed‐of‐service pricing to its customers, but without day‐of‐week differentiation, resulting in customer demands with considerable day‐of‐week seasonality. The package delivery company hoped that using day‐of‐week and speed‐of‐service price differentiation for contract customers would induce these customers to adjust their demands to become counter‐cyclical to the non‐contract demand. Although this usually cannot be achieved by pricing alone, we devise an approach that utilizes day‐of‐week and speed‐of‐service pricing as an element of a Pareto‐improving contract. The contract provides the lowest‐cost arrangement for the package delivery company while ensuring that the customer is at least as well off as he would have been under the existing pricing structure. The contract pricing smoothes the package delivery company's demand and reduces peak requirements for transport capacity. The latter helps to decrease capital costs, which may allow a further price reduction for the customer. We formulate the pricing problem as a biconvex optimization model, and present a methodology for designing the contract and numerical examples that illustrate the achievable savings.     

A Capacitated Multi‐echelon Inventory Placement Model under Lead Time Constraints

We develop an inventory placement model in the context of general multi‐echelon supply chains where the delivery lead time promised to the customer must be respected. The delivery lead time is calculated based on the available stocks of the different input and output products in the different facilities and takes into account the purchasing lead times, the manufacturing lead times, and the transportation lead times. We assume finite manufacturing capacities and consider the interactions of manufacturing orders between time periods. Each facility manages the stocks of its input and output products. The size of customer orders and their arrival dates and due dates are assumed to be known as in many B2B situations. We perform extensive computational experiments to derive managerial insights. We also derive analytical insights regarding the manufacturing capacities to be installed and the impacts of the frequency of orders on the system cost.     

The Impact of Capacity Costs on Product Differentiation in Delivery Time,Delivery Reliability,and Price

We develop an analytical framework for studying the role capacity costs play in shaping the optimal differentiation strategy in terms of prices, delivery times, and delivery reliabilities of a profit‐maximizing firm selling two variants (express and regular) of a product in a capacitated environment. We first investigate three special cases. The first is an existing model of price and delivery time differentiation with exogenous reliabilities, which we only review. The second focuses on time‐based (i.e., length and reliability) differentiation with exogenous prices. The third deals with deciding on all features for an express variant when a regular product already exists in the marketplace. We subsequently address the integrative framework of time‐and‐price‐based differentiation for both products in a numerical study. Our results shed light on the role that customer preferences towards delivery times, reliabilities and prices, and the capacity costs (absolute and relative) have on the firm's optimal product positioning policy.     

Impact of Customer Traffic and Service Process Outsourcing Levels on e‐Retailer Operational Performance

Many service firms deliver services via a mix of internally developed and delivered (i.e., insourced) and externally developed and delivered (i.e., outsourced) service processes. Service process outsourcing is especially common in e‐retailing. Portions of e‐retail customer ordering processes and delivery processes can be digitized and contracted to third‐party vendors. Via outsourcing, service systems change from dyadic to triadic. Prior research examines consumer perceptions of dyadic (consumer to e‐retailer) outcomes, but little research considers service co‐delivery with outsourcing partners (i.e., triadic systems). Literature also does not focus on joint associations of service process outsourcing and customer traffic with e‐retailer operations. We analyze several years of data on North American e‐retailers. We first examine factors associated with e‐retailer outsourcing levels, for front‐end and back‐end service processes. We observe customer traffic is positively associated with future outsourcing. We then examine how outsourcing moderates associations between contemporaneous customer traffic and e‐retailer operational performance, as measured by numbers of processed orders, website response times, and customer satisfaction. Results suggest outsourcing levels are associated with operational outcomes, yet surprisingly, high outsourcing and high traffic jointly may not benefit e‐retailers.     

An Exploratory Study of the Impact of e‐Service Process on Online Customer Satisfaction

Although extensive academic research has examined the dynamics of interpersonal interactions between service providers and customers, much less research has investigated customer service encounters through technological interfaces such as the Web in electronic commerce transactions. Corporate websites have become an important point of contact with customers for many companies. Service has been described as one of the most important attributes for online business to influence traffic and sales. However, more research is needed to understand how Web‐based technological capabilities of services affect customer satisfaction. In this paper, we propose viewing the interface between online buyers and sellers through the lens of service management to identify possible determinants of online customer satisfaction. A company's website is considered its electronic service delivery system. We look at this electronic service delivery system from its process point of view. Our findings indicate that as the electronic service delivery system process improves, a customer's perception of the website's ease of use increases, leading to increased service value and perceived control over the process, which increases customer satisfaction. The research provides evidence that the technological capabilities embedded in the website processes are an important factor in determining service quality and ultimately online customer satisfaction.     

Analyzing the Effect of Express Orders on Supply Chain Costs and Delivery Times

Delivery time differentiation is a supply chain concept that has been implemented in various industries, but not yet in the automotive industry. One reason is that the effects of delivery time differentiation on the supply chain are not well understood. The BMW Group, for instance, has considered offering an express order option, where express orders bypass standard orders in the supply chain processes to achieve short delivery times. Express orders distort planning processes, increase operations cost, and increase the delivery times of standard orders, however the effects have not been quantified yet. This study analyzes the impact of express orders on the supply chain, when express orders are built‐to‐order. To understand the supply chain consequences of express orders better, we analyzed the relevant supply chain processes at BMW Group. We determine the effect that built‐to‐order express orders have on delivery times and on component demand. To analyze the effect of introducing express orders on expected delivery times and expected cost, we use queuing theory and derive expressions for the transient behavior of a discrete time batch queue. Our analyses indicate that many supply chain processes are only marginally affected. However, the orders to the suppliers become considerably more uncertain, which must be compensated by additional safety stock. Our results indicate that express orders can be an attractive option for BMW and other automotive companies. If the fraction of express orders stays at a reasonable level, express orders can be delivered within about two weeks.     

Demand and Production Management with Uniform Guaranteed Lead Time

Recently, innovation‐oriented firms have been competing along dimensions other than price, lead time being one such dimension. Increasingly, customers are favoring lead time guarantees as a means to hedge supply chain risks. For a make‐to‐order environment, we explicitly model the impact of a lead time guarantee on customer demands and production planning. We study how a firm can integrate demand and production decisions to optimize expected profits by quoting a uniform guaranteed maximum lead time to all customers. Our analysis highlights the increasing importance of lead time for customers, as well as the tradeoffs in achieving a proper balance between revenue and cost drivers associated with lead‐time guarantees. We show that the optimal lead time has a closed‐form solution with a newsvendor‐like structure. We prove comparative statics results for the change in optimal lead time with changes in capacity and cost parameters and illustrate the insights using numerical experimentation.     

A Stochastic Inventory Model With Fast‐Ship Commitments

We present a multiperiod model of a retail supply chain, consisting of a single supplier and a single retailer, in which regular replenishment occurs periodically but players have the option to support fast delivery when customers experience a stockout during a replenishment period. Because expedited shipments increase the supplier's transportation cost, and possibly production/inventory costs, the supplier typically charges a markup over and above the prevailing wholesale price for fast‐shipped items. When fast shipping is not supported, items are backordered if customers are willing to wait until the start of the next replenishment period. We characterize the retailers and the supplier's optimal stocking and production policies and then utilize our analytical framework to study how the two players respond to changes in supply chain parameters. We identify a sufficient condition such that the centralized supply chain is better off with the fast‐ship option. We find a range of markups for fast‐ship orders such that the fast‐ship option is preferred by both the supplier and the retailer in a decentralized chain. However, a markup that is a win–win for both players may not exist even when offering fast‐ship option is better for the centralized chain. Our analysis also shows that depending on how the markup is determined, greater customer participation in fast‐ship orders does not necessarily imply more profits for the two players. For some predetermined markups, the retailer's profit with the fast‐ship option is higher when more customers are willing to wait. However, the retailer may not be able to benefit from the fast‐ship option because the supplier may choose not to support the fast‐ship option when fast‐ship participation increases due to the fact that the fast‐ship participation rate adversely affects the initial order size.     

Lean Control for Make‐to‐Order Companies: Integrating Customer Enquiry Management and Order Release

A lead time that is short, predictable, and reliable is an increasingly important criterion in supplier selection. Although many companies may achieve this through lean implementation, high‐variety manufacturers, for example, small and medium‐sized make‐to‐order companies, have found that lean's planning and control techniques do not apply. This article outlines a planning and control concept known as workload control (WLC) that integrates customer enquiry management, including a due‐date setting rule, with order release control. Simulation is then used to assess its impact on shop performance. Results demonstrate that an integrated WLC concept can reduce the percentage of tardy jobs—so short lead times can be realistically quoted—while also reducing and stabilizing workloads. WLC can level demand and production over time when work is not standardized and it is not possible to synchronize flows on the shop floor. Results are shown to be robust to changes in routing characteristics, the mix of orders with due dates specified by the customer and proposed internally, and the strike rate (or order‐winning probability). Hence, an integrated approach to WLC represents an important step toward achieving lean in make‐to‐order companies.     

Single‐Period Two‐Product Assemble‐to‐Order Systems with a Common Component and Uncertain Demand Patterns

We consider a single‐period assemble‐to‐order system that produces two types of end products to satisfy two independent and stochastic customer orders. Each type of product is used to fulfill a particular customer order and these two products share a common component. Furthermore, one customer may confirm her order before the other one, and the manufacturer needs to make a commitment immediately upon the receipt of each customer order on how many products to be delivered. We propose a model for optimizing the inventory and production decisions under the above ATO environment. We also extend our model to the situation where the manufacturer can fulfill the unsatisfied low‐priority demand using the left‐over inventories after fulfilling the high‐priority demand, in case the low‐priority customer arrives first. Numerical experiments are conducted, which provide some interesting insights on the impact of uncertain demand pattern.     

Optimizing Customer Forecasts for Forecast‐Commitment Contracts

We study a “Forecast‐Commitment” contract motivated by a manufacturer's desire to provide good service in the form of delivery commitments in exchange for reasonable forecasts and a purchase commitment from the customer. The customer provides a forecast for a future order and a guarantee to purchase a portion of it. In return, the supplier commits to satisfy some or all of the forecast. The supplier pays penalties for shortfalls of the commitment quantity from the forecast, and for shortfalls of the delivered quantity from the customer's final order (not exceeding the commitment quantity). These penalties allow differential service among customers. In Durango‐Cohen and Yano (2006), we analyzed the supplier's problem for a given customer forecast. In this paper, we analyze the customer's problem under symmetric information, both when the customer is honest and when he strategically orders more than his demand when doing so is advantageous. We show that the customer gains little from lying, so the supplier can use his control over the contract parameters to encourage honesty. When the customer is honest, the contract achieves (near‐)coordination of the supply chain in a great majority of instances, and thus provides both excellent performance and flexibility in structuring contracts.     

The Impact of Revenue‐Maximizing Priority Pricing on Customer Delay Costs

Speed is an increasingly important determinant of which suppliers will be given customers' business and is defined as the time between when an order is placed by the customer and when the product is delivered, or as the amount of time customers must wait before they receive their desired service. In either case, the speed a customer experiences can be enhanced by giving priority to that particular customer. Such a prioritization scheme will necessarily reduce the speed experienced by lower‐priority customers, but this can lead to a better outcome when different customers place different values on speed. We model a single resource (e.g., a manufacturer) that processes jobs from customers who have heterogeneous waiting costs. We analyze the price that maximizes priority revenue for the resource owner (i.e., supplier, manufacturer) under different assumptions regarding customer behavior. We discover that a revenue‐maximizing supplier facing self‐interested customers (i.e., those that independently minimize their own expected costs) charges a price that also minimizes the expected total delay costs across all customers and that this outcome does not result when customers coordinate to submit priority orders at a level that seeks to minimize their aggregate costs of priority fees and delays. Thus, the customers are better off collectively (as is the supplier) when the supplier and customers act independently in their own best interests. Finally, as the number of priority classes increases, both the priority revenues and the overall customer delay costs improve, but at a decreasing rate.     

A MODEL FOR BATCH ADVANCED AVAILABLE‐TO‐PROMISE

The available‐to‐promise (atp) function is becoming increasingly important in supply chain management since it directly links production resources with customer orders. In this paper, a mixed integer programming (mip) ATP model is presented. This model can provide an order‐promising and ‐fulfillment solution for a batch of orders that arrive within a predefined batching interval. A variety of constraints, such as raw material availability, production capacity, material compatibility, and customer preferences, are considered. Simulation experiments using the model investigate the sensitivity of supply chain performance to changes in certain parameters, such as batching interval size and customer order flexibility.     

Single vs. multiple objective supplier selection in a make to order environment

The problem of allocation of orders for custom parts among suppliers in make to order manufacturing is formulated as a single- or multi-objective mixed integer program. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order. The selection of suppliers is based on price and quality of purchased parts and reliability of on time delivery. The risk of defective or unreliable supplies is controlled by the maximum number of delivery patterns (combinations of suppliers delivery dates) for which the average defect rate or late delivery rate can be unacceptable. Furthermore, the quantity or business volume discounts offered by the suppliers are considered. Numerical examples are presented and some computational results are reported.     

Real‐Time Revision of Order Quantities with Capacity Constraint: A Single Period Model

Manufacturing firms would like to maximize customer satisfaction by providing them with what they need when they need it. This, however, would mean continual variations in production quantities, and component orders from suppliers. A flexible manufacturing system can help alleviate costs related to modification of production quantities. The capacity of such a system, however, has to be limited because of high investment cost. Further, unless there is a long‐term relationship, suppliers may levy a high surcharge for last minute changes in order size. We model a hybrid control policy comprising an advance (pre‐production) order size agreed upon with suppliers, and a provision for real‐time order revision at a given rate of surcharge. We show that a rank‐order of products can be used for real‐time revisions, and that a strong buyer‐supplier relationship that keeps these surcharges low can actually help increase profits for both parties. We study issues such as compatibility between JIT and flexibility, and the impact of market conditions on overall profitability.     

Does a Supplier's Operational Competence Translate into Financial Performance? An Empirical Analysis of Supplier–Customer Relationships

We conduct an empirical investigation of how a supplier's operational competence, as reflected by outcomes in the areas of quality, cost, delivery, flexibility, and new product development, translates into financial gains from a key customer. In contrast to previous research directed at the firm level, this study focuses on the supplier–customer relationship level. Using survey data from 158 suppliers in the manufacturing industry, we perform structural equation modeling to map out the paths from operational competence to financial performance—via dependencies and cooperative behaviors between suppliers and their customers. This study is the first scholarly attempt to examine the link between suppliers’ operational competencies and financial performance in interorganizational relationships. It is also an early investigation into operational competence as a source of bi‐lateral dependence. Our findings show that the supplier's operational competences increase its customer's dependence by enhancing the value of its products/services. However, the resulting increase in the supplier's power is not leveraged to shape relationship behaviors or capture value from its customer. In contrast, the customer's existing power as a major buyer plays an important role in shaping cooperative behaviors and affecting the supplier's financial performance from the customer relationship.     

Effective Design of Products/Services: An Approach Based on Integration of Marketing and Operations Management Decisions*

This paper presents an integrated framework for designing profit‐maximizing products/ services, which can also be produced at reasonable operating difficulty levels. Operating difficulty is represented as a function of product and process attributes, and measures a firm's relative ease or difficulty in meeting customer demand patterns under specified operating conditions. Earlier optimum product design procedures have not considered. operational difficulty. We show that optimum profit, market share, cost, and product profiles are dependent on operating difficulty level. Empirical results from the pizza delivery industry demonstrate the value of the proposed Effective Product/Service Design approach.