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Operations strategy
Published in Andrew Greasley, Absolute Essentials of Operations Management, 2019
Flexibility is the ability of the organization to change what it does. Flexibility is needed so that the organization can adapt to changing customer needs in terms of product range and varying demand and to cope with capacity shortfalls due to equipment breakdown or component shortage. The following types of flexibility can be identified: Product or service – to be able to introduce new product or services.Mix – to be able to change the proportion (mix) between the different products or services offered.Volume – to be able to decrease or increase overall product/service output.Delivery – to be able to change the timing of a delivery. Flexibility can be measured in terms of range (the amount of the change) and response (the speed of the change). The range and response dimensions are connected in the sense that the more something is changed (range), the longer it will take (response). In general, the benefit of flexibility from the customer’s point of view is that it means that the organisation is able to adapt to customer needs.
Flexibility
Published in Jenny Rinkinen, Elizabeth Shove, Jacopo Torriti, Energy Fables, 2019
Flexibility refers to the quality of bending easily without breaking and the ability to be easily modified. For a long time, energy demand was considered to be inflexible – non-negotiable and difficult to modify. Alongside the introduction of renewable energy technologies (which are widely seen as inflexible forms of generation), this view has recently changed: rather than being treated as something fixed, demand is now thought of as something that can and should be flexed, with the right kind of intervention. In exploring these themes, the chapter is in two parts. The first suggests that the recent history of energy provision supposes that demand is not flexible, while the second interrogates ongoing narratives according to which more and more flexibility is needed.
Resistive Switching Phenomenon for Flexible and Stretchable Memories
Published in Run-Wei Li, Gang Liu, Flexible and Stretchable Electronics, 2019
Xiaohui Yi, Shuang Gao, Jie Shang, Bin Chen, Gang Liu, Run-Wei Li
The structure of a generic resistive switching memory is composed of (1) a substrate, (2) bottom electrodes and top electrodes, and (3) an information storage medium. The mechanical stability of flexible RRAMs is usually estimated by bending tests as a function of bending radius and the number of bending cycles. Thus, the key performance parameters of flexible resistive switching memories include minimum bending radius, maximum bending strain, bending endurance, switching endurance under bending, and data retention time under bending. Under a given bending radius R (Fig. 4.1b), the real bending strain on memory cells can be roughly calculated to be d/2R, where d represents substrate thickness [41]. This is because the thickness of memory cells (mostly <1 μm) is usually much smaller than that of the substrate (mostly >10 μm) and, thus, can be reasonably ignored for strain calculation [20, 26]. To make the structure flexible, all components must comply with bending to some degree without losing their function. Three types of substrate materials are available for flexible applications: metals, organic polymers (plastics), and flexible glass; while for the electrodes, metallic materials, metal oxide (such as ITO), organic conducting polymers, and carbon-based materials are commonly employed due to their flexibility under certain bending condition. On the other hand, the insulator sandwiched between the two electrodes plays a key role in RRAM since the resistive switching behavior for information storage takes place in the insulator or at the insulator-electrode interface. In this section, we only focus on the design consideration of flexible RRAM from the point of view of storage media.
Designing a multi-objective model for a hazardous waste routing problem considering flexibility of routes and social effects
Published in Journal of Industrial and Production Engineering, 2020
Elham Araee, Neda Manavizadeh, Soroush Aghamohammadi Bosjin
Flexibility is defined as the ability of a system to respond to changes in an efficient way. Flexibility for an engineering system is the Simplicity with which the system can respond to uncertainty in a way to hold or increase its value delivery. Routing flexibility is the ability of considering several paths to deliver the items [1]. At this end, preparing different depots to respond to the requests of customers provides different options for decision maker for the delivery of items. Vehicle Routing problem (VRP) has a close relationship with location-allocation problem (LAP), and Location-Routing problem (LRP). If we exclude customer service provision over a closed path (the tour), that is, each customer directly relates to distribution centers, this becomes a locational assignment problem. Also, if we take into account the location of the facility in a locator-routing problem (predetermined), this will turn into the problem of routing the vehicles. Generally, VRP is an NP-Hard problem as proved in the previous researches.
Stochastic modelling of maintenance flexibility in Value for Money assessment of PPP road projects
Published in Construction Management and Economics, 2021
Flexibility is a property embedded in an engineering entity to adapt to future uncertain changes proactively. The engineering entity may be a physical object (e.g. a highway pavement), an engineering management process (e.g. a PPP contract), or an integrated system of both. For a long-term investment, it is rational to incorporate flexibility into the entity in order to reduce the downside risks while harnessing opportunities arising from future uncertainties (De Neufville and Scholtes 2011). While the value of flexibility is an old wisdom in engineering, explicit valuation of flexibility through analytical models did not happen until the 1970s when the real options approach took the root in project evaluation. There has been a rich body of literature in the real options; refer to Alonso-Conde et al. (2007) and Martins et al. (2015) for two interesting reviews. Machiels et al. (2020) presented an interesting bibliometric study of recent real options applications in the planning of large transportation projects. Among the 42 papers they reviewed, only 3 considered asset conditions as the uncertainty source in the real options analysis, whereas 39 papers dealt with market uncertainty. In addition, their study also indicated that the majority of real options applications had been focussing on the use of delay to invest, scale (expand or contract), abandon, growth option, stage, switch use, and other risk mitigation, whereas the study of maintenance flexibility has been limited. Since our work is concentrated on maintenance flexibility and its impact on lifecycle cost risk, the following review focuses on the valuation of maintenance flexibility.
Flexible FPGA 1D DCT hardware architecture for HEVC
Published in Automatika, 2023
Hrvoje Mlinarić, Alen Duspara, Daniel Hofman, Josip Knezović
In HEVC, after the frame residual signal is divided into N × N blocks, where N = 2M, N ∈ 4,8,16,32, and M is in the range [9,12], the two-dimensional integer forward DCT (2D DCT) is applied to each block. The 2D DCT is a separable transform and can be divided into two N-point one-dimensional DCT (1D DCT) applied to each row and each column, respectively. Smaller kernel matrices are embedded into the 32 × 32 transform kernel matrix. This property enables efficient hardware sharing between different transform sizes (4 × 4, 8 × 8 and 16 × 16). It defines that for , where the are the smaller transform matrices (N ∈ 4,8,16), the next expression is valid. The embedded structure of the kernel matrices enables creating the flexible DCT design that efficiently reuses hardware. Flexibility can be defined as the ability of the design which enables multiple working modes. Also, reusability can be defined as the design feature which enables the hardware submodule units of the design to have more than one function. To enable runtime flexibility of the DCT with efficient hardware reusability, the next mathematical expressions can be used: if N = 4 for is the input vector, and is N-point DCT of X. CN is N-point integer DCT kernel matrix of size N × N.