DMAIC and VSM is an integrated leans six sigma tool, which plays a vital role for realizing the lean six sigma manufacturing. The results come out by comparing processing time, productionleadtime; WIP and value added ratio.The research results indicate that an overall reduction of 19.87% was achieved in the processing time, 39.85% in the leadtime and 33.33% reduction in inventory. The increase in value added ratio was 33.65 %. The results are shown in table 6.1.
Both the causes ‘too many parts on the car’ and ‘car is not optimal structured for the employees of FA’ have the same underlying cause ‘the process of FA is not standardised’. It is an underlying reason for the cause ‘too many parts on the car’, because the warehouse cannot reduce the number of parts on the car. The warehouse cannot reduce it because each employee decides his own order of assembly and therefore all parts must be present at the start of the FA. The other cause is quite remarkable, because the warehouse department has done a research to find the best possible solution to structure the car. The outcome of their research was that the best solution is to structure the car to material of the parts. But with the current best structure of the car the employees of FA still cannot find the parts easily on the car. The question that arises is why is it not possible to structure the car such that the employees of FA can find the parts the quickest. According to Medbo (2003, p.267) the ideal structure to gain the lowest searching time for parts is “that the components are displayed next to the operator in the correct assembly sequence”. Faccio (2014) gives different good parts-feeding policies for just-in-time (JIT) assembly system like FA at ZGEU, which are all based on the set assembly sequence. Thus, there are different policies to structure the car other than to the material of the parts. But in the current situation the process of FA does not have a fixed sequence. Therefore, for the current situation structuring the car with parts to materials is the best solution. To gain another better structure for the car for the employees of FA the process of FA needs to be standardised. Thus, the cause ‘the process of FA is not standardised’ is an underlying reason of ‘the car is not optimal structured for the employees of FA’.
The paper reports and demonstrates the spreadsheet simulation that used for production planning and inventory control in a composite based product manufacturer. It covers the development of the spreadsheet simulation template and the application. The findings show that the application of the spreadsheet simulation positively improve the time to estimate the productionleadtime, and monitoring activities on material usage included raw material, chemical and ancillary. Besides, it provides well-managed documentations for production planning and control.
Capacity flexibility is used as an effective way to hedge against demand variability in short- term (Bish et al., 2005). Higher demand uncertainty motivates newsvendor to invest more on flexibility (Goyal and Netessine, 2007). Simchi-Levi, Wang and Wei (2017) proposed a two- stage robust optimization problem to choose flexibility decision strategy under uncertain demand. Chatzikontidou, Longinidis, Tsiakis and Georgiadis (2017) proposed a flexible supply chain network design (SCND) model that uses generalized production/warehousing nodes under demand uncertainty, and they used a scenario-based approach to solve it. Fan, Schwartz and Voß (2017) investigated the application of diverse transportation modes for flexible global supply chain (SC) in stochastic environments. Goyal and Netessine (2007) studied the effect of demand uncertainty and competitive pressures on newsvendor’s decision of technology investment. They insisted that demand uncertainty is the most important driver of technology choice in flexible manufacturing decision. Rodriguez et al. (2014) developed a non-linear programming with stochastic demand to find optimal inventory level in supply chain. Kulkarni and Francas (2017) investigated on capacity investment strategies and the optimal value of flexibility in food and chemical industry in the presence of uncertainty of input materials. Yongheng et al. (2013) developed Lagrangian decomposition algorithm to decide the optimal capacity in electric motor industry under stochastic demand and constant lead-time. Kaya et al. (2013) developed a robust optimization method for optimal capacity planning under stochastic demand and return in a closed loop supply chain. Ho and Fang (2013) studied the capacity allocation of multiple products under uncertain demand, they resulted that inventory holding cost, shortage cost, loss of excess production, and market demands should be considered in an effort to discover the optimal capacity allocation, concerning multiple products. The review of literature reveals that many capacity optimization models ignore stochastic characteristics of productiontime and just focus on stochastic demand in order to hedge against variability (Dangl, 1999; Bish et al., 2005; Birge and Louveaux, 2011; Rodriguez et al., 2014; Sting and Huchzermeier, 2014). Moreover, literature in capacity investment strategies take c 1 , c 2 < c f <
Productivity is a core issue in manufacturing sector which every company wants it to be growing at higher rate than the past. Productivity is the rates of output that comes from the unit of input. One of the way to achieve the growth of productivity by getting closer with the of time of a process, the cost effective, effective use of available area to reduce the cost and increase the profit. Error that occur during production run is one of the cause of lower productivity. Error can come from man power, machine (breakdown) or from the part manufactured by supplier. Manufacturing industries highlights on dealing with this mistakes especially human mistake.
At the beginning Value Stream Mapping (VSM) was used as a methodology which identifies waste time and unneeded actions occurring in the process. But now a day's VSM is being used as a re-engineer business for identifying the unnecessary work and resources being used for the process of the operation. This study is about the Akshaya Patra Foundation, a non-profit organization with the vision that no child shall be deprived of education because of hunger, runs the largest mid-day meal program in the world. The main objective is to find process cycle efficiency as well as the process leadtime of production by using VSM. The Problem definition is that, since 2016 the product cost is increasing above Rs.3.06 per meal which is effecting the operating cost of the company. Due to this increase, the strategy to monitor the performance by using VSM method becomes crucial, the design of the System of Core Competencies has to be adapted to provide the Scalabililt and Service to inturn ensure its Sustainabililty. The main goal of this is eliminating the rigorous impact of the waste by using the strategy of Muda, Muri and Mura.
When we insert the 2017 input data in our model, we should receive the same or at least similar outputs. For this reason, we run our system without additional PPC methods. We compare the results of our models in Table 4-2 and Table 4-3, to the properties of the real system as mentioned in Section 2.3.2 and Section 2.3.4 We notice that our lead times correspond quite well to the times between release and first finish. When comparing the waiting times, we notice that our repair shop, consisting of Troubleshoot (which is passed twice) and Repair, is a bit more efficient than the actual system, with a total waiting time of 1.52 days compared to 3.2 days. This is due to the fact that limited details of the repair shop are available, mostly with RMAs. This is probably also the cause of our lead times matching the first finish lead times instead of the last finish, which is often skewed as it includes multiple repair cycles, while we assume only a single repair cycle per test station is allowed. It is important to note that while we can replicate the simulation data, real data is only available of a single half year. Our model on the other hand, used the data of more than 60 half years. This means, that where the output data of our model is consistent when averaged over all runs, the real data might not be consistent. With these remarks in mind, we accept the output as reasonable and foremost usable. For the 2018 data, we added the waiting times to same table as the 2017 data. We don’t have accurate 2018 data on the waiting times. Regarding the lead times, we do have some data, especially from Q3 2018. This data however is probably not really accurate due to the holiday period, which might explain the differences we notice in the data. When comparing 2017 to 2018, we notice a smoother SMD process, where especially is loaded less. We checked our data on this and it was valid. This is thus due to a change in order properties between 2017 and 2018. It is important to mention that between 2017 and 2018 we increase the number of fte at HMT production to 17. This explains why the average waiting time at this station has gone down. This is done in correspondence to the situation in practice.
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Leadtime has been a topic of interest for many authors Ben-daya (1994), Das (1975), Foote (1988), Magson (1979), Naddor (1966), Chung, and Ting (1993), Fujiwara (1993). Almost all authors assume leadtime as prescribed in all cases, i.e. deterministic as well as probabilistic. However, in many practical situations leadtime can be reduced at an added cost. By reducing the leadtime, customer service and responsiveness to production schedule changes can be improved and reduction in safety stocks can be achieved. The added cost of reducing leadtime consists mainly of administrative costs, transportation cost as the item's transit time from the supplier is a major component of leadtime, and supplier's speed-up cost.
A Gantt chart is useful to represent this critical path. For large or complex projects, specialised project management software is much preferred.. the most popular example of such specialized software –is– Microsoft Project (Heizer & Render, 2011). The complete project schedule can be viewed as a Gantt chart. We can easily add or remove –or change– activities.. Microsoft Project automatically updates all start dates, finish dates, and the critical path(s). Perhaps the biggest advantage of using software to manage projects is that it can track progress of the project. The Critical Path Method lets us focus on the most relevant part of the radar system for leadtime reduction. However we will see in chapter 4 we will not need the whole method of CPM for this research. A Gantt chart already represents the necessary information when we determine from each item the name, successor and the leadtime. Because a radar faces an assembly network structure, where each part has at most one successor and one or more predecessors, the critical path can easily be seen.
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CSE-/- mice would exhibit increased hemoglobin levels dur- ing hypoxia as compared to normoxia. This phenomenon remained true for wild-type mice, however, hypoxic CSE-/- mice interestingly exhibited lower hemoglobin levels than their normoxic counterparts. In combination with other results from this study, this suggests that H 2 S is both needed and beneficial during hypoxia in order to produce this hypoxic response. Interestingly, excess H 2 S was found to decrease hemoglobin levels in normal mice during normoxia, sug- gesting that high levels of H 2 S may, in fact, be detrimental to erythrogenesis in a resting state. This indicates that the role of H 2 S in erythrogenesis is likely dependent on oxygen avail- ability, thus resulting in differing effects of exogenous H 2 S treatment observed during normoxia and hypoxia. We also demonstrate, for the first time, that CKD patients have lower urinary H 2 S levels compared to non-CKD patients, and that patients who require ESAs for anemia associated with CKD produce even less urinary thiosulfates (an active metabolite of H 2 S) compared to non-anemic CKD patients who do not require ESAs. This further strengthens the argument that H 2 S is an essential modulator of erythrogenesis, and that this phe- nomenon may be intrinsic to the kidney.
The idea that time compression (i.e. the collapsing of all cycle times within a supply chain) would enhance business competitiveness to the advantage of all members in the chain has been around for some time. However, the gap between theory and practice has taken several decades to narrow sufficiently so as to make a major impact on industrial performance. What is now clear is that time compression is a performance driver which initially enhances the competitiveness of individual work processes and then of complete industrial businesses. Particularly when coupled with open information flow, time compression can multiply to have an even greater effect on supply chain competitiveness. Today, it is no longer sufficient to be a competent business in isolation: it is also necessary to be associated with world-class supply chains if we are to survive, let alone win.
As explained in Section 1.3, a reverse parcel is delivered at a PUDO point and transported back to the warehouses around Place A. The logistic process from PUDO point to the hub of the carrier is outside the scope of the model, since the leadtime of this part depends on the service of the carrier. So, Company X does not have influence on this leadtime. To be able to compute the leadtime of the whole logistic process, which is PUDO point to the warehouses, we use the average values of the leadtime of the clients from Section 2.2. We use an average value of the leadtime of the carrier in the simulation model, because we found that minimal 90 percent of these lead times are within plus or minus 0.2 day from the actual average. This applies to all carriers. Therefore, we assume that an average value is sufficient here. Multiple clients make use of the same carrier, for example clients Client 1, Client 3, and Client 4 use Carrier A. The leadtime of the carrier is calculated proportionally to the number of parcels for each client. As a result, Table 13 shows that the leadtime PUDO to hub Carrier A is 1.3 days. These calculations are done for each carrier.
Boosting(,) method follows 2 steps: first, uses subsets of the original data to produce a series of averagely performing models and then, second step, it ”boosts” their performance by combining them together using a particular cost function (a majority vote). Unlike bagging (), in the classical boosting, the subset creation is not random and depends upon the perfor- mance of the previous models: every new subsets contains the elements that were (likely to be) misclassified by previous models. Boosting is calling a “weak” or “base” algorithm many times, each time trained on a different subset (each subset has different weighting over the training examples). Every time the ”weak” algorithm is called, it generates a new weak prediction rule and after many iterations, the boosting algorithm, must combine these weak rules into one single prediction rule.
Bhim Singh etal: They have Researched in their research paper that the original concepts and definitions about value stream mapping (VSM) demonstrated that it is necessary to map both inter-company and intra- company value-adding streams. Value stream refers to those specifics of the firm that add value to the product or service under consideration. VSM was initially developed in 1995 with an underlying rationale for the collection and use of the suite of tools as being ‘‘to help researchers or practitioners to identify waste in individual value streams and, hence, find an appropriate route to its removal’’. The process itself is very simple and straightforward. It usually starts with customer delivery and work its way back through the entire process documenting the process graphically and collecting data along the way. Finally it results in a single page map called ‘‘Value stream’’, these maps contains data such as cycle time, work-in-process (WIP) levels, quality levels, and equipment performance data. Joseph C. Chen etal: They have cleared in their research that Although Lean was initially introduced by the automobile industry, its principles have more recently spread into other industries. There are a variety of companies that have experienced the advantages of applying Lean in their manufacturing area. For instance, Lean was applied by Boeing to eliminate waste and make its products more cost-competitive. Lean is a systematic approach for identifying and eliminating waste through continuous improvement by ‘flowing’ the product at the pull of the customer in pursuit of perfection.