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The purpose of this study is to identify, characterize and assess supplier flexibility measurement practices in the order-to-delivery process.

The study involved a survey; participants were 224 purchasing managers at Swedish manufacturing companies that had more than 20 employees.

Scrutiny of the details of measurement practices revealed that most respondents actually do not specifically measure supplier flexibility. Instead they measure other measures like delivery reliability, conduct qualitative follow-ups, or cannot specify how supplier flexibility is measured. It was acknowledged that they measure different supplier flexibility aspects, and the applied measures were characterized, e.g. in terms of which flexibility dimension they represent.

Conceptual clarifications and adaptations to measuring supplier flexibility in the order-to-delivery process are provided. The identified measures can be a contribution in further developing literature on flexibility performance measurement.

Purchasing, logistics and supply chain managers in search of supplier flexibility performance measurement can find ways to measure and an extended flexibility vocabulary. This has the potential to improve flexibility in the supply chain.

Even though flexibility is claimed as being an important competitive advantage, few empirical studies and operationalized measures exist, particularly in the order-to-delivery process.

Results have shown that the actual delivery performance of a company is often significantly worse than management's own assessment of its performance.

The present work seeks to determine the optimal delivery schedule of equipment at a project site in the backdrop of limited storage space, at a minimum cost, and without disturbing the overall project schedule. In addition, the optimized delivery schedule helps in minimizing the fluctuating requirements of space at the project site across the entire project lifespan.

The study is carried out at a Steel plant operating in a constrained space but undergoing a production capacity expansion. The problem motivated us to explore the possibility of postponing the delivery dates of certain equipment closer to the erection dates without compromising on the project schedule. Given the versatility of linear programming models in dealing with such schedule optimization problems, the authors formulated the above problem as a Zero-One Integer Linear Programming problem.

The model is implemented for all the new equipment arriving for two major units – the Hot Strip Mill (HSM) and the Blast Furnace (BF). It generates an optimized delivery schedule by delaying the delivery of some equipment by a certain number of periods, without compromising the overall project schedule and at a minimum storage cost. The average space utilization increases by 25.85 and 14.79% in HSM and BF units respectively. The fluctuations in space requirements are reduced substantially in both units.

The study shows a timeline in the form of a Gantt chart for the delivery of equipment, storage of equipment across different periods, and the number of periods for which the delivery of certain equipment needs to be postponed. The study uses linearly increasing storage costs with the increase in the number of periods for storage of the equipment in the temporary shed.

1. Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

2. Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

3. The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

4. The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

5. The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

Determined the optimal delivery schedule of the equipment in a project environment in the backdrop of limited storage space in the project site.

Formulated the above problem as a Zero-One Integer Linear Programming (ILP) problem.

The average space utilization has increased by 25.85 and 14.79% in HSM and BF units respectively.

The optimized delivery schedule helps in reducing the fluctuations in space requirements substantially across the entire lifespan of the project.

The timeline of delivery of equipment, storage of equipment across different periods and periods of postponement of the equipment are shown in the form of a Gantt Chart.

The author investigate the effect which the delivery performance of a company's suppliers have upon the company's own delivery assurance and reputation. Alternative methods of evaluating supplier delivery performance are studied. An analysis is made of the extent to which the suppliers' poor delivery performance was adversely affected by factors under the control of the company's own sales, design, production and purchasing departments in requesting unrealistic dates from suppliers.

The service industry is a major component of the economy. Raw material, components, assemblies, and finished products are shipped between suppliers, manufacturers, distributors, and retailers. Accordingly, timely receipt of shipped goods is crucial in maintaining the efficiency and effectiveness of such service processes. A service provider offers an incentive to the customer by specifying a competitive target time for delivery of goods. Further, if the delivery time is deviant from the target value, the provider offers to reimburse the customer for an amount that is proportional to the value of the goods and the degree of deviation from the target value. The service provider may set the price to be charged as a function of product value. This price is in addition to the operational costs of logistics that are not considered in the formulated model. For protection against deviation from target due dates, the service provider agrees to reimburse the customer. The reimbursement could be based on an asymmetric loss function influenced by the degree of deviation from the target due date as well as product value. The penalties could be different for early and late deliveries since the customer may experience different impact and consequences accordingly. The chapter develops a model to determine the amount (price) that the provider should add to the cost estimate of the delivery contract for protection against delivery deviations. Such a cost estimate will include the operational costs (fixed and variable) of the shipment, to which an amount is added to cover the expected payout to customers when the delivery time deviates from the target value. The optimal price should be such that the expected revenue will at least exceed the expected payout.

The purpose of this paper is to generate a performance model for an order‐to‐delivery (OTD) process in delivery scheduling environments. It aims to do this with a triadic approach, encompassing a customer, a supplier and a logistics service provider.

The paper takes the form of a conceptual analysis and a triadic case study on performance measurement requirements in an OTD process characterized by delivery scheduling, and generating performance models.

Two OTD process performance models, one for the supplier's delivery sub‐process and one for the customer's delivery scheduling, the logistics service provider's transportation and the customer's good receipt sub‐process, in delivery scheduling environments are generated.

A single case study limits the levels of external validity and reliability to analytical generalization.

The generated performance models include definitions of four sub‐processes and outline ten performance dimensions that should be of relevance for several companies to apply.

This is the first approach that generates performance models for a triadic OTD process for use in delivery scheduling environments.

When approaching a stock‐out situation, a company should be able to actively manage the allocation of available products on the basis of customer requirements and priorities as well as contractual relationships. The purpose of this paper is to describe different order promising mechanisms and analyze how well they can contribute to the effective management of stock‐out situations.

The paper provides a formal description and analysis of alternative order promising mechanism applicable in make to stock systems. Numerical analysis is conducted based on the data of a pharmaceutical company.

The paper clearly points out the potential of alternative order promising mechanisms to alleviate the negative consequences associated with a temporary stock‐out situation.

The paper does not consider implications of inventory pre‐allocation to customer classes. Further research should address the interplay between pre‐allocation and different order allocation mechanisms.

The results obtained from this analysis provide guidelines for manufacturers, retailers, and vendors of supply chain software on how to design and utilize order promising systems.

The paper provides a consistent formal approach to modelling order promising mechanisms, introduce new and innovative order promising mechanisms and provide valuable insight into their performance through numerical analysis.

Presents a case study of a production seat booking system for the photographic color film manufacturing industry. This manufacturing process is a hybrid flow shop, which consists of two manufacturing stages: first, processing of the film bulk‐rolls in batches, and second, packing of the final product specifications in a continuous‐process line. A production seat booking system is a new scheduling system, which reduces planning lead‐time significantly, by simplifying the time‐for‐delivery adjustment work. It was found possible to shorten planning lead‐time sharply based on this booking system. Additionally, it became possible simultaneously to reduce inventory levels and logistic‐related costs through such management innovation as shortening planning lead‐time and other entry delivery systems and such improvement activities as making small lot‐size.

Untimely delivery of goods and services, especially in a post-COVID landscape, is a critical harbinger of end-to-end fulfillment. Existing literature in supplier delivery modeling is focused on penalizing suppliers for late deliveries built into a contractual transaction, which eventually erodes trust. As such, a holistic modeling technique focused on long-term relationship building is missing. This study aims to design a supplier evaluation model that analytically equates supplier delivery performance to cost realization while replicating a core attribute of successful supply chains – alignment, leading to long-term supplier relationships.

The supplier evaluation model designed in this paper uses delivery deviation as a unit of measure as opposed to delivery duration to enhance consistency with enterprise resource planning protocols. A one-sided modified Taguchi-type quality loss function (QLF) models delivery lateness to construct a multinomial probability penalty cost function for untimely delivery. Prescriptive analytics using simulation and optimization of the proposed mathematical model supports buyer–supplier alignment.

The supplier evaluation model designed herein not only optimizes likelihood parameters for early and late deliveries for competing suppliers to enhance total landed cost comparisons for on-shore, near-shore and off-shore suppliers but also allows for the creation of an efficient frontier toward supply base optimization.

At a time of systemic disruptions such as the COVID pandemic, global supply chains are at risk of business continuity. Supplier evaluation models need to focus on long-term relationship modeling as opposed to short-term contractual penalty-based modeling to enhance business continuity. The model offered in this paper is grounded in alignment – a cornerstone of successful supply chain integration, and offers an interesting departure from traditional modeling techniques in this genre.

The results from this analytical approach offer flexibility to a supply manager toward building redundancies in the supply chain using an efficient frontier within the supply landscape, which also helps to manage disruption and maintain end-to-end fulfillment.

The model offered in this paper is grounded in alignment – a cornerstone of successful supply chain integration, and offers an interesting departure from traditional modeling techniques in this genre. The authors offer a rational solution by creating an evaluation model that uses penalty cost modeling as an internal quality measure to rate suppliers and uses the outcome as a yardstick for negotiations instead of imposing penalties within contracts.

Two distinct models of delivery reliability versus delivery speed are tested. On the basis of data from a survey of 193 manufacturing firms, factors associated with the “planning” systems of firms, such as production‐plan goals achieved, inventory goals achieved, and master schedule performance, were found to have a significant effect on delivery reliability. In follow‐up interviews with 13 plant managers; it was found that “process”‐related factors were associated with delivery speed capabilities. Specifically, the biggest inroads to be made into delivery speed are first on the design/manufacturing interface, secondly on the subsequent “translation” of these designs to supplier requirements, and lastly on the production floor in terms of process layout.