Multiple Product Families Research Articles

First Movers in Molecular Detection: Case Comparison on Harnessing Research and Development, Industry, and Entrepreneurship.

The current unprecedented COVID-19 pandemic underscores the importance of diagnostic assays in health security preparedness and readiness. Advancing new technologies for rapid molecular detection of high consequence infectious pathogens is an ongoing challenge that requires ingenuity and vision. Sustainment of a robust supply chain for materials and the logistics of timely product delivery further challenge diagnostic kit and device manufacturers. Business economists often characterize technology companies that discover unique breakthroughs in their field and are first to bring related products to market as first movers. From a market perspective, three first mover characteristics include: having the knowledge and capability to address a unique breakthrough, excellent technological leadership, and the ability to capitalize on the opportunity. Current mainstays for molecular detection include using Taq DNA Polymerase enzyme and fluorescent chemistry for quantitative PCR (qPCR). A newer and promising technology uses CRISPR-Cas proteins for nucleic acid detection. Our panel discussion from the 2020 ASM Biothreats conference, which included members from two prototypical first mover companies, explored their respective corporate experiences. Both companies were selected for the discussion based on their revolutionary innovations and similarities in their research and development, corporate culture and trajectory. One company, established over 20 years ago, became a market leader in the biothreat detection market by advancing air thermocycling qPCR across multiple product families. The second company is a rapidly growing start-up and a scientific pioneer in establishing next generation CRISPR technologies. Here we discuss their technology development, product deployment, and customer markets to draw lessons learned for researchers, end users, and funders.

A note on: “A hybrid Benders approach for coordinated capacitated lot-sizing of multiple product families with set-up times,” by T. Bayley, H. Süral, and J. Bookbinder

In a recent paper by Bayley et al. [2018. “A Hybrid Benders Approach for Coordinated Capacitated Lot-Sizing of Multiple Product Families with Set-Up Times.” International Journal of Production Research 56 (3): 1326–1344] on a capacitated lot-sizing problem, a Benders-based approach is described. Strengthening the master problem is based on inequalities that should be valid. We provide insight indicating that this is not the case for those inequalities proposed by Bayley et al. (2018).

Effective dynamic selection of smart products scheduling rules in FMS

The present paper addresses the dynamic scheduling problem of smart products in a Flexible Manufacturing System (FMS). Unrelated machines, multiple product families, operation setups and dynamic job arrivals are considered. The goal is to propose a dynamic selection of smart products scheduling rules enriched with a context perception capability that is optimized using a hyper heuristic. A proof of concept with preliminary results is given.

Simulation of mixed-load testing process in an electronic manufacturing company

The automatic testing machine, called by mixed-load tester, has ability to load and test multiple product families in different testing durations simultaneously. However, the high product mixes for each product family undergoes a different process flow. In addition, the capability of the robot inside tester used for loading and unloading a product to each slot makes the capacity planning problem more complicated. It effects low tester utilization. This paper developed simulation models of capacity planning scenarios under demand and testing time uncertainty. These scenarios are built by robust optimization to handle worst case condition. The result shows the proposed solutions gives better tester utilization and improves the decision making process by providing more detailed and precise information about capacity planning under uncertainties that was not available in company`s current method. To the best of our knowledge, this developed model is the first one considering the mixed–load tester under uncertainties.

A Transformable Manufacturing Concept for Low-Volume Aerospace Assembly

This paper presents a vision for a transformable manufacturing system based on reconfigurable and adaptive intelligent technologies, anchored in the description of a real automation cell demonstrating the assembly of various aircraft structures. This production environment developed by the University of Nottingham is a new reconfigurable production environment for the complete manufacturing of high-accuracy high-complexity low-volume aerospace products. This agile reconfigurable cell can accommodate multiple product variants and families which ensures that the capital invested in fixturing is not tied to one application. By compressing the assembly of an aircraft wing structure into a single automated cell, the Future Automated Aircraft Assembly Demonstrator features a flexible, holistic, and context-aware solution that includes automated positioning, drilling, and fastening processes. The assembly cell is based on industrial robots for the handling of aircraft components while an intelligent metrology and control system monitors the cell and ensures that the operations carried out are safe and adhere to the tight tolerances required. These modules are integrated into a single, standardised interface requiring only one operator to control the cell. Performance analyses have shown that, using the reconfigurable production environment described hereafter, an assembly part location accuracy better than ±0.1 mm can be achieved.

A hybrid Benders approach for coordinated capacitated lot-sizing of multiple product families with set-up times

We examine a coordinated capacitated lot-sizing problem for multiple product families, where demand is deterministic and time-varying. The problem considers set-up and holding costs, where capacity constraints limit the number of individual item and family set-up times and the amount of production in each period. Using a strong reformulation and relaxing the demand constraints, we improve both the upper and lower bounds using a combination of Benders decomposition and an evolutionary algorithm, followed by subgradient optimisation. Through computational experiments, we show that our method consistently achieves better bounds, reducing the duality gap compared to other single-family methods studied in the literature.

Design and simulation of CONWIP in the complex flexible job shop of a Make-To-Order manufacturing firm

This paper presents a methodology for the design and integration of CONWIP in a make-to-order firm. The approach proposed was applied directly to the flexible job shop of a real manufacturing firm in order to assess the validity of the methodology. After the description of the whole plant layout, attention was focused on a section of the shop floor (21 workstations). The CONWIP system deals with multiple-product families and is characterized by path-type cards and a pullfrom-the-bottleneck scheme. The cards release strategy and a customized dispatching rule were created to meet the firm’s specific needs. After the simulation model of the present state was built and validated, the future state to be implemented was created and simulated (i.e. the CONWIP system). The comparison between the two systems achieved excellent results, and showed that CONWIP is a very interesting tool for planning and controlling a complex flexible job shop.

Setting planned lead times for a make-to-order production system with master schedule smoothing

This article considers a make-to-order manufacturing environment with fixed guaranteed delivery lead times and multiple product families, each with a stochastic demand process. The primary challenge in this environment is how to meet the quoted delivery times subject to fluctuating workload and capacity limits. The tactical planning parameters are considered, namely, the planning windows and planned lead times. The planning process that is modeled is the one in which the demand represents a dynamic input into the master production schedule. A planning window for each product family controls how the schedule of each product family is translated into a job release. It can be thought of as the slack that exists when the fixed quoted delivery lead time is longer than the total planned production lead time. Furthermore, the planned lead time of each station regulates the workflow within a multi-station shop. The model has underlying discrete time periods to allow the modeling of the planning process that is typically defined in time buckets; within each time period, the intra-period workflow that permits multiple job movements within the time period is modeled. The presented model characterizes key performance measures for the shop as functions of the planning windows and planned lead times. An optimization model is formulated that is able to determine the values of these planning parameters that minimize the relevant production-related costs. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for additional results of the simulation study in Section 6 of this manuscript.]

A production scheduling heuristic for an electronics manufacturer with sequence-dependent setup costs

In this paper, we develop a three-step heuristic to address a production scheduling problem at a high volume assemble-to-order electronics manufacturer. The heuristic provides a solution for scheduling multiple product families on parallel, identical production lines so as to minimize setup costs. The heuristic involves assignment, sequencing, and time scheduling steps, with an optimization approach developed for each step. For the most complex step, the sequencing step, we develop a greedy randomized adaptive search procedure (GRASP). We compare the setup costs resulting from the use of our scheduling heuristic against a heuristic previously developed and implemented at the electronics manufacturer that assumes approximately equal, sequence-independent, setup costs. By explicitly considering the sequence-dependent setup costs and applying GRASP, our empirical results show a reduction in setups costs for an entire factory of 14–21% with a range of single production line reductions from 0% to 49%.

Solution strategies for multi-stage wafer probing scheduling problem with reentry

The multi-stage wafer probing scheduling problem (M-WPSP) with reentry is a practical variation of the parallel-machine scheduling problem. Since the M-WPSP involves multiple product families, to be processed on multiple stages, with various job due dates, ready times, reentry, serial and batch operations, sequential-dependent setup time, it is more difficult to solve than the classical parallel-machine scheduling problems. In this paper, we consider two strategies to solve the M-WPSP with reentry, where the total machine workload must be minimized. These two strategies incorporate a global planning mechanism, in advance, to determine the required stage due date of job at each process stage to prevent the due date problems occurring at the final stage. The sequential strategy schedules the jobs at the required stages according to the sequence of manufacturing process. The parallel strategy is designed specifically for the reentrant characteristic. To evaluate the efficiency of the proposed strategies, a set of test problems involving four critical factors, the product family ratio, the temperature-change consideration, the tightness of due dates, and the ready time, are designed to test the quality of solutions under two levels of workload.

An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design

This paper addresses an evaluation of new heuristics solution procedures for the location of cross-docks and distribution centers in supply chain network design. The model is characterized by multiple product families, a central manufacturing plant site, multiple cross-docking and distribution center sites, and retail outlets which demand multiple units of several commodities. This paper describes two heuristics that generate globally feasible, near optimal distribution system design and utilization strategies utilizing the simulated annealing (SA) methodology. This study makes two important contributions. First, we continue the study of location planning for the cross-dock and distribution center supply chain network design problem. Second, we systematically evaluate the computational performance of this network design location model under more sophisticated heuristic control parameter settings to better understand interaction effects among the various factors comprising our experimental design, and present convergence results. The central idea of the paper is to evaluate the impact of geometric control mechanism vis-a-vis more sophisticated ones on solution time, quality, and convergence for two new heuristics. Our results suggest that integrating traditional simulated annealing with TABU search is recommended for this supply chain network design and location problem.

Impact of worker and shop flexibility on assembly cells

Flexibility can counter the negative effects of the loss of pooling synergy in cellular systems. In this study we define flexibility as the ability of assembly cells to reallocate resources to accommodate changes in family assignments (i.e. shop flexibility) and the ability of workers to move between cells (i.e. worker flexibility). We investigate the impact of shop and worker flexibility on different assembly cells faced with product mix variability over a wide range of experimental conditions. Three types of cellular shops are considered: strict cell shops (where each cell is dedicated to producing only one product family), flexible cell shops (where each cell can produce multiple product families), and hybrid cell shops (where some of the cells are strict and the rest are flexible). Results indicate that there is no cell shop that outperforms others, under all experimental environments. However, flexible cell shops showed significant advantages when the setup times were low, while hybrid cell shops provided an excellent alternative when setup times and the product mix unbalance were at ‘moderate to high’ levels. The strict cell shops demonstrated excellent performance when setup times were high, and the product mix unbalance was ‘minor’. Finally, the results suggest that in most cases, the implementation of only worker flexibility resulted in the majority of improvements with respect to the average percentage of jobs tardy.

The impact of information sharing and forecasting in capacitated industrial supply chains: A case study

This paper models and analyses the effect of information sharing and forecasting on the performance parameters of an actual industrial supply chain consisting of Small-To-Medium sized enterprises. The paper reports on the industrial supply chain studied, which was undergoing a Business Process Re-engineering (BPR) exercise. The aim of the BPR exercise was the streamlining of existing unstructured processes, ultimately culminating in the introduction of an ERP system into the organisation to improve information sharing between the supply chains echelons. The paper reviews previous work in this area and expands this work to address the issues posed by a more complex real industrial example. The model itself has been developed for a complex supply chain structure. This supply chain has multiple customers, distributors and product families, with customers and distributors face differing demand patterns. This model and its associated experimentation highlights the significant benefits achievable through the use of improved information sharing and forecasting techniques on the supply chain performance parameters. Potential total supply chain cost savings of up to 9.7% have been shown, with increased savings occurring with reduced system capacity. The model also quantifies the impact of collaboration between all partners in the study and shows that gains are achievable by all parties in the supply chain.

A continuous‐time strategic capacity planning model

AbstractCapacity planning decisions affect a significant portion of future revenue. In the semiconductor industry, they need to be made in the presence of both highly volatile demand and long capacity installation lead‐times. In contrast to traditional discrete‐time models, we present a continuous‐time stochastic programming model for multiple resource types and product families. We show how this approach can solve capacity planning problems of reasonable size and complexity with provable efficiency. This is achieved by an application of the divide‐and‐conquer algorithm, convexity, submodularity, and the open‐pit mining problem. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

Optimal capacity expansion for multi-product, multi-machine manufacturing systems with stochastic demand

We consider a discrete-time capacity expansion problem involving multiple product families, multiple machine types, and non-stationary stochastic demand. Capacity expansion decisions are made to strike an optimal balance between investment costs and lost sales costs. Motivated by current practices in the semiconductor and other high-tech industries, we assume that only minimal amounts of finished-goods inventories are held, due to the risk of obsolescence. We assume that when capacity is in short supply, management desires to ensure that a minimal service level for all product families is obtained. Our approach uses a novel assumption that demand can be approximated by a distribution whose support is a collection of rays emanating from a point and contained in real multi-dimensional space. These assumptions allow us to solve the problem as a max-flow, min-cut problem. Computational experiments show that large problems can be solved efficiently.

A simulated annealing methodology to distribution network design and management

This paper describes the PLOT (Production, Logistics, Outbound, Transportation) design system. The system addresses a class of distribution network design problems, which is characterized by multiple product families, a central manufacturing plant site, multiple distribution center and cross-docking sites, and retail outlets (customer zones) which demand multiple units of several commodities. The resulting system focuses on two key stages: the planning stage where we incorporate a strategic based decision-making process of selecting the “best” set of distribution centers and cross-docks to operate. The second phase consists of the execution stage that includes an operational based decision-making process. In this phase, the model decides the required quantity of product families that need to be transported from the plant to distribution centers and transshipped to cross-docks from warehouses, and later distributed to customer outlets. The distribution system design considered here is derived from current operations of a major retailing organization that manages products for nationwide distribution. The PLOT system developed to implement the model provides for a high degree of user interaction in the generation of solutions. The overall system generates globally feasible, near optimal distribution system design and utilization strategies utilizing the simulated annealing (SA) methodology. This study makes two important contributions to the SA literature. First, we extend the breadth of applications by studying a new combinatorial problem that incorporates cross-docking in a supply chain environment. Second, we systematically evaluate the computational performance under a variety of problem scenarios and SA control parameter settings.

A Continuous-Time Strategic Capacity Planning Model Based on the Minimum-Cut Problem

The semiconductor industry has been one of the driving forces of the “new” economy. It boasts of the exponentially growing performance of semiconductor devices, coupled with rapidly decreasing chip prices; however, it faces highly volatile demands, and copes with astronomical fab costs, most of which can be attributed to tool costs. The leadtime for purchasing tools is between 6 and 18 months, upon which tools quickly become obsolete. Thus, semiconductor companies need to recover their capital investment in the tools over a short period of time. We develop models and algorithms for strategic capacity planning, which is to determine the sequence and timing of acquiring tools. Strategic planning decisions are made in the presence of high uncertainty. Uncertainty comes from factors such as technology, the market, and its products, and becomes amplified by long leadtimes. Although capacity planning decisions need to be made in the presence of high uncertainty, early research and even some current practices overlook the stochastic nature of planning, with the exception of simple case analyses. An extensive review of literature can be found in Cakanyildirim et al. (1999) and Roundy et al. (2000). Typical methods of stochastic optimization include stochastic-linear programming, stochastic-integer programming, and Markov decisions processes; yet, they have not been able to solve real-world capacity planning problems on the scale faced by the semiconductor industry. This paper takes the stochastic-optimization approach, which explicitly incorporates randomness in the model. We assume nonstationary stochastic demand, with the expected demand for product families increasing over time. We also assume lost sales and no finished good inventory. As in Cakanyildirim et al. (1999), we continue to explore alternative approaches based on continuous-time models. The time at which a machine is purchased becomes a continuous-decision variable. These models are more compact than traditional stochastic-programming methods based on discrete-time models. It is hoped that the small dimensionality of continuous-time models will make the strategic capacity planning problem computationally tractable. In this paper, we model multiple resource types used for multiple product families. The resulting problem is related to the continuous relaxation of the lot-sizing problem. We present an efficient divideand-conquer algorithm that will find a locally optimal solution of this problem. A subroutine to this algorithm is the parametric minimum-cut problem.

A reusable enterprise model

Enterprise activities are intricate, as companies often produce multiple product families which require a complex mixture of production modes. Modelling techniques are commonly applied to expose the behaviour of an enterprise and to investigate the feasibility of achieving business objectives. However, enterprise modelling can be a slow process and models become invalid if business objectives are changed, because their information content may be overtaken by fast changing market conditions. The modelling process is accelerated if new models can use part or all of the information from previous models. This paper addresses these issues by introducing a reusable enterprise model, called a factory data model (FDM). It has been implemented using an object‐oriented approach, and contains five key class hierarchies. The model can be specialised to meet the requirements of individual enterprises; hence, less time and effort are involved in creating new models to satisfy changes in enterprise requirements.

Software for dynamic reconfigurable order picking systems

Abstract This paper describes an approach, conceptual framework, and software architecture for dynamic reconfiguration of the order picking system. The research and development project was sponsored by the Material Handling Research Center (MHRC), a National Science Foundation sponsored Cooperative Industry/University Research Center. The storage configuration is assumed to be an in-the-aisle order picking system in which stockkeeping units (SKUs) can occupy variable capacity storage locations and stock-splitting is allowed among zones (clusters). The product mix may include multiple product families with different life cycles, correlated demand within families and commonality of demand across families.