Purpose The objective of this study is to explore the structural attributes of the pharmaceutical industry before the onset of the COVID-19 pandemic by examining the relationship between inventory and firm performance and developing a taxonomy of pharmaceutical firms based on the earns-turns matrix. Design/methodology/approach This study examines the inventory–firm performance linkage, considering both total inventory and its discrete inventory components in pharmaceutical firms. In addition, this research develops a new taxonomy of pharmaceutical firms based on the earns-turns matrix. A large panel dataset of firms in the US pharmaceutical industry was collected for the period 2000–2019. Findings The results reveal that strategic groups identified based on this taxonomy show different levels of profitability and inventory turns in the earns-turns matrix. Most pharmaceutical firms moved from the low-right to the top-left section in the earns-turns matrix, indicating that these firms have generally pursued profitability rather than effective inventory management. Research limitations/implications This study explores the structural attributes of the pharmaceutical industry using the earns-turns matrix. This two-dimensional analysis may not, however, capture the full complexity of inventory–firm performance dynamics. Practical implications The mapping of strategic groups on the earns-turns matrix provides a useful tool for visual representations of the dynamics of strategic groups in terms of financial performance and inventory management performance. Practitioners can use the earns-turns matrix to benchmark their firm's position against their competitors. Originality/value This study broadens the scope of operations management research by introducing the earns-turns matrix as an empirical validation tool for operational and strategic management theories. This study emphasizes the effectiveness of the earns-turns matrix in analyzing strategic groups of pharmaceutical firms.

We consider the tactical problem of optimizing the capacity of a block appointment system with time-varying no-show and random service duration. The problem characteristics are motivated by a real-life case study of two outpatient specialty clinics in the US, where time-of-day variation in appointment attendance was observed. We aim to determine the block size (patients to be assigned in each time period) such that the weighted sum of block-wise waiting- and idle-times (or total cost) are minimized. First, we consider optimizing the capacity of a single block appointment system (SBAS) as a special case of the inverse newsvendor problem, and develop an analytical closed-form solution under the assumption of normally distributed service time. Also, a stochastic integer programming (SIP) model is developed to solve SBAS for any service time distribution. Subsequently, the SIP model is extended to determine the block size of the variable-sized multi-block appointment system (VSMBAS) by treating it as a sequential inverse newsvendor problem. Owing to the computational complexity of the SIP, we employ sample average approximation to estimate the expected total cost. Numerical studies considered several realistic clinic settings, and the results demonstrated that integrating time-varying no-shows for block size determination will considerably improve schedule efficiency as opposed to ignoring it. We also found the cost ratios (waiting-time to idle-time penalty), service time variation, and no-show pattern have a substantial influence on the block size of VSMBAS. Finally, we provide several practical implications based on our analysis.

The newsvendor model deals with a single-period capacity allocation problem under uncertainty. The real world examples include perishable products (e.g., fish, vegetable), holiday-related products (e.g., Easter, Christmas, Halloween), seasonal products (e.g., fashion), and promotional products. This section addresses three newsvendor models: traditional newsvendor, inverse newsvendor, and sequential newsvendor models. The main decision under the traditional newsvendor setting is capacity allocation (i.e., how much to order), whereas the main decision under the inverse newsvendor setting is demand allocation (i.e., how many customers to be served) under the fixed capacity. This section demonstrates how to compare profit maximization approach to customer-oriented approach under the traditional newsvendor. The inverse newsvendor applies to revenue management for the hospitality industry. The sequential newsvendor model determines the optimal sequence when the number of customers to be served (determined by the inverse newsvendor model) is given. Normal distribution is considered for analytical solution and numerical studies. In addition, a discrete distribution is considered for numerical studies.

This paper investigates an appointment system with deterministic arrival times and non-identical exponential service times with the objective of minimizing the expected costs of customer-waiting and server-idle times (WIT). For two customers, this paper shows analytically that the smallest-variance-first-rule (SV) and, equivalently, the smallest-mean-first-rule (SM) minimize WIT when the second customer arrives at either optimal or arbitrary arrival time. For three customers, this paper shows analytically that either SV or SM minimize WIT, assuming that each customer arrives at the cumulative sum of expected service times of prior customers. Based on numerical evaluation, this paper recommends that the exponential distribution parameter, which determines either SV or SM sequences, be used for a general number of customers. (C) 2020 Elsevier Ltd. All rights reserved.

Countries in the Middle East are requiring larger and more complex shipping operations for the growing downstream sectors of the hydrocarbon industry, such as refining and petrochemistry. These shipping operations generally entail a closed-loop system, in which the vessels travel back and forth from a manufacturing plant to various international ports for exporting the hydrocarbon products. The current shipping systems only focus on feasible, smooth, and repeatable operations with a first-available-first-use (FAFU) policy, owing to the lack of a rigorous and systematic optimal scheduling method for maritime transportation systems. This study presents an integrated short-term scheduling model formulated as a mixed integer linear programming (MILP) model, for dispatching vessels from a petrochemical plant to international ports and economically operating a plant-side port in the closed-loop shipping system. Further, the MILP model considers varying vessel capacities and other related complex restrictions. A constructive heuristic algorithm combined with an optimization solver is adopted to solve the MILP model with sufficient speed for practical use. Numerical studies using a real data set demonstrate the effectiveness of the developed model compared to the current scheduling policy, FAFU. Moreover, we analyze the economic and computational performances of the developed solution method. Our method solves the test scenarios effectively, presenting a cost saving opportunity of 11.3% on average, which is equivalent to 439,829 USD per year for the studied operation.

ABSTRACT Operations and Supply Chain Management (OSCM) courses may cover supply chain strategies, supply chain classification, and supply chain performance. Familiarity with various manufacturing and logistics firms would help students to better understand such topics. Information on the Dow Jones Industrial Average indexed firms and top 50 supply chain firms by Gartner is easily accessible and typically covers a variety of industries from chemical, food/beverage, high‐tech to retail, to name a few. Instructors of OSCM courses can take advantage of this kind of information to discuss industry characteristics and supply chain classification. We present how to collect financial data, calculate supply chain metrics (e.g., inventory turns, profit margin, and cash‐to‐cash cycle) by building a spreadsheet model and creating an earns‐turns matrix, which prescribes supply chain classification. We also show how to analyze supply chain performance and describe industry characteristics based on the earns‐turns matrix. We provide vital questions and takeaways for instructors to lead and wrap‐up discussions. Students claim that they appreciated learning about industry characteristics and different supply chain strategies through the earns‐turns matrix analysis.

The inverse newsvendor problem is a variant of the traditional newsvendor problem where the decision of interest is to select the number of customers that could be served in the available capacity, measured in units of time. In essence, the traditional newsvendor problem maps demand into capacity, whereas with the inverse newsvendor problem capacity is mapped into demand. First, we provide an analysis of the problem under the assumptions of normally and exponentially distributed service times. We also numerically show that approximations of the lognormal and the gamma distributions to the normal distribution are relevant and valid. For normally distributed service times, we take into accounts both identical and nonidentical distributions. We propose three heuristics to decide who to be served rather than the number of customers when service times are nonidentically and normally distributed. We conduct extensive numerical studies to show the efficacy of the heuristics.

Appointment-based service systems admit limited number of customers at a specific time interval to make service providers more accessible by reducing customers’ waiting time and make the costly resources more productive. A traditional approach suggests the Bailey rule, which assigns one or more customers at the initial block and only one customer at remaining blocks. We prescribe two heuristic approaches and variations of the traditional Bailey rule to appointment scheduling systems with the objective of minimizing total expected costs of delay and idle times between blocks. The first heuristic adopts a branch-and-bound approach using forward dynamic programming and tries to fully enumerate with some restrictions. The second heuristic uses a sequential-inverse newsvendor approach using a starting solution. We conduct numerical tests, which show that both heuristics get near-optimal solutions in a quicker time than a commercial solver, CPLEX and that the second approach gives near-optimal solutions far faster than the first approach. In addition, we suggest the use of a periodic Bailey rule, which can be implemented easily in practice, and provides a close solution to the best result of both heuristics, depending upon cost parameters and service-time variances.

We provide an approach to optimize a block surgical schedule (BSS) that adheres to the block scheduling policy, using a new type of newsvendor-based model. We assume that strategic decisions assign a specialty to each Operating Room (OR) day and deal with BSS decisions that assign sub-specialties to time blocks, determining block duration as well as sequence in each OR each day with the objective of minimizing the sum of expected lateness and earliness costs. Our newsvendor approach prescribes the optimal duration of each block and the best permutation, obtained by solving the sequential newsvendor problem, determines the optimal block sequence. We obtain closed-form solutions for the case in which surgery durations follow the normal distribution. Furthermore, we give a closed-form solution for optimal block duration with no-shows. (C) 2013 Elsevier B.V. All rights reserved.

This paper studies capacity planning decisions that allocate surgical specialties to operating-room (OR) days with the objective of minimizing total expected costs due to penalties for any patients who are not accommodated and for under- (i.e., idleness) and over- (i.e., overtime) usage of OR capacity. It presents a prototypical non-linear, stochastic programming model to structure relevant and practical features of the problem and four adaptations, along with associated solution approaches, with the goal of facilitating solution by overcoming the computational disadvantages of the prototype. Each of these adaptations offers advantages but is also attended by disadvantages. Computational tests compare the four adaptations and solution approaches with respect to solution quality and run time.

The smallest-variance-first-rule (SV) is generally accepted as the optimal policy for sequencing two surgeries, although it has been proven formally only for several restricted cases. We extend prior work, studying three distributions as models of surgery duration (the lognormal, gamma, and normal) and including overtime in a total-cost objective function comprising surgeon-and patient-waiting-, operating-room-idle-, and staff overtimes. We specify expected waiting and idle time as functions of the parameters of surgery duration to identify the best rule to sequence two surgeries. We compare the relative values of expected waiting and idle times numerically with that of expected overtime. Results recommend that the SV rule be used to minimize total expected cost of waiting, idle and overtime. We find that gamma and normal distributions with the same mean and variance as the lognormal give nearly the same expected waiting and idle times, observing that the lognormal in combination with either the gamma or normal gives a similar result. We extend to the three-surgery case, showing that sequencing the first surgery is most important. We demonstrate how our results can be applied by using them as a basis for a heuristic that assigns surgeries to multiple operating rooms and then sequences them.