Z Corporation Case Study Analysis Paper

Case Study: UPD Manufacturing

1304 WordsApr 28th, 20136 Pages

Case: UPD Manufacturing


Demand, d = 6
Ordering Interval, OI = 89
Ordering cost, S = $32
Holding Cost/Carrying Cost, H = $.08

As there is no demand variability, the formula for quantity is: Q = d (LT + OI) – A
(as there is no safety stock) ------- A - ROP (Reorder point) We know, A = d * LT, so the fixed order interval order quantity equation Q becomes

Q = (d * LT) + (d * OI) – (d * LT) * Q = d * OI = (6) (89) = 534 units Therefore, ordering at six-week intervals requires an order quantity of 534 units. Now, the optimal order quantity is determined by using EOQ equation. Q = sqrt(2dS/H) = sqrt[(2*89*32)/.08] = 266.833 (or) 267

The weekly total cost based on optimal…show more content…

Balancing these competing requirements leads to optimal inventory levels, which is an on-going process as the business needs shift and react to the wider environment. | | | | | | | | | | |


What project durations should Smitty include in the proposal for these risks of not delivering the project on time: 5%, 10%, and 15% ?

PATH | EXPECTED DURATION | STANDARD DEVIATION | A | 10 | 4 | B | 14 | 2 | C | 13 | 2 |

Z= X – t (path) Standard deviation

Z = (X – 10) / 4 = 0.05 Z = (X – 10) = 0.05 x 4
Z = (X – 10) = 0.2 Z = X = 0.2 + 10
Z = X = 10.2
→ The project duration for path A is 10 weeks 2 days
Z= X – t (path) Standard deviation

Z = (X – 14) / 2 = 0.1 Z = (X – 14) = 0.1 x 2
Z = (X – 14) = 0.2 Z = X = 0.2 + 14
Z = X = 14.2
→ The project duration for path B is 14 weeks 2 days

Z= X – t (path) Standard deviation

Z = (X – 13) / 2 = 0.15 Z = (X – 13) = 0.15 x 2
Z = (X – 13) = 0.3 Z = X = 0.3 + 13
Z = X = 13.2 → The project duration for path C is 13 weeks 2 days

What are the pros and cons of quoting project times aggressively?
Project time management is a special process used to plan, operate and monitor projects. Its aim is to efficiently achieve project goals and objectives or solve a particular problem. It is different from general management, in part, because of its focus on completing well-defined goals within

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As buildings are cantilever structures, there is generation of base moment whenever it is under lateral load. The magnitude of the moment increases considerably with slenderness, because the moment is proportional to the square of the height of the building, just like a cantilever beam under varying loads. Because of the scarcity of land these days, vertical construction is given due importance and the buildings are much higher than before, making them highly susceptible to horizontal loading like wind load. In addition to this, if the plan of the building is unconventional, then wind analysis is a task of great complexity because of the many flow situations arising from the interaction of the wind with the structures. There are several different phenomena giving rise to dynamic response of tall structures under wind such as buffeting, vortex shedding, galloping and flutter. Simple quasi-static analysis of wind loading, which is globally applied to the design of low- to medium-rise structures, can be unacceptably conservative for the design of very tall buildings. At present, the wind tunnel model experiment and numerical simulation using computational fluid dynamics (CFD) are the available research tools to get deeper insight into the behavior of gigantic structures subjected to turbulent wind load.

In the definition of the overall strength, durability and risk of failure of structures, extreme wind speed is an important factor, mostly reliant on the general weather pattern over many years and local environmental and topographical conditions.

The precise evaluation of the extreme wind is mainly connected with the quality of statistical data of wind velocity which is associated with performance and calibration of measuring instruments, common averaging time, same height above the ground, roughness of the terrain, etc. The predicted wind speed is usually enumerated as the maximum wind speed which is surpassed, on average, once in every N year (return periods); for example, I.S: 875 (Part-3) (1987) requires that ordinary structures be designed for an annual exceed probability of 2 % which is equivalent to 50 years of return periods.

Past studies have been carried out by researchers with the help of model analysis to get more accurate information regarding wind structure interaction. Kareem (1986) deliberated the details of the interference and proximity effects on the dynamic response of prismatic bluff bodies. Lin et al. (2004) discussed the findings of a widespread wind tunnel study on local wind forces on isolated tall buildings based on the experimental outcome of nine square and rectangular models (1:500). Liang et al. (2004) proposed the empirical formulae for different wind-induced dynamic torsional responses through the analytical model. Gomes et al. (2005) enumerated the results from the studies of L- and U-shaped models of 1:100 scale. Lam and Zhao (2006) examined in detail wind flow around a row of three square-plan tall buildings closely arranged in a row at a wind angle θ = 30°. The computational domain was digitized into 2.1 × 106 finite volumes. Irwin (2007) reviewed a number of bluff body aerodynamic phenomena and their effect on the structural safety and occupant comfort. Zhang and Gu (2008) correlated the numerical simulation and experimental investigations of wind-induced interference effects. Fu et al. (2008) enumerated the field measurements of the characteristics of the boundary layer and storm response of two super tall buildings. Irwin (2009) centered on the subject of determining and controlling the structural response under wind action for super tall buildings which demand much more pragmatically modeled wind engineering, since building codes and standards are not practical enough for dealing with such soaring structures. Tse et al. (2009) discussed the general concept to determine the wind loadings and wind-induced responses of square tall buildings with different sizes of chamfered and recessed corners while maintaining the total usable floor area of the building by escalating the number of stories which is directly associated with the economics of the building. Bhatnagar et al. (2012) presented the results of a wind tunnel study in an open circuit boundary layer flow condition, carried out on a model of low-rise building with sawtooth roof. Tominaga and Stathopoulos (2012) modeled turbulent scalar flux in computational fluid dynamics (CFD) for near-field dispersion around buildings. Raj et al. (2013) carried out an experimental boundary layer wind tunnel study to observe the effect of base shear, base moment and twisting moment developed due to wind load on a rigid building model having the same floor area, but different cross-sectional shapes with the variation of wind incidence angle. Muehleisen and Patrizi (2013) developed parametric equations to find out the values of pressure coefficients (Cp) on the surfaces of rectangular low-rise building models from experimental wind tunnel data. Amin and Ahuja (2013) investigated through wind tunnel studies rectangular building models of different side ratios (ratio of building’s depth to width) ranging from 0.25 to 4, keeping the area and height the same for all models, while the wind angle changes at an interval of 15° from 0° to 90°. Kushal et al. (2013) recognized that the plan shape of the building affects the wind pressure to a great degree. Verma et al. (2013) described the effects of wind incidence angle on wind pressure distribution on square-plan tall buildings. Bhattacharyya et al. (2014) investigated the mean pressure distribution on various faces of ‘E’ plan-shaped tall building through experimental and analytical studies for a wide range of wind incidence angle. Chakraborty et al. (2014) enumerated the results of a wind tunnel study and numerical studies on ‘+’ plan-shaped tall building and compared the results for 0° and 45° wind incidence angles. Kheyari and Dalui (2015) have conferred the results of a case study to estimate wind load on a tall building under interference effects. They used a CFD simulation tool to create a ‘virtual’ wind tunnel to predict the wind characteristics and wind response.

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