Walmart Debacle of Germany - 2078 Words

Wal-Mart in Europe Table of Contents Executive Summary 2 Wal-Mart Background 2 Wal-Mart’s Culture 2 Wal-Mart’s Strategy 3 Problems/Criticism faced by Wal-Mart 3 Wal-Mart in Germany 4 Key Issue: Wal-Mart’s Failure in Germany 4 Situation Analysis 5 Porter’s 5 Forces Model 5 Wal-Mart: Germany vs. Britain 6 Challenges in Germany 7 Evaluation of Alternatives 10 Recommendations 10 Executive Summary The world economy has undergone a drastic revolution in the last three decades through globalization. This has made the world economy more efficient and competitive, by enhancing product quality, increasing the product variety and lowering price. With successful expansion in locations like Mexico and Canada, in†¦show more content†¦Key Issue: Wal-Mart’s Failure in Germany Wal-Mart’s strategy to replicate the successful U.S. formula of Everyday Low Price (EDLP) guarantee, efficient inventory control and distribution system resulted in significant cross-cultural issues with Germans consumers and suppliers. Moreover, the top management failed to anticipate the cultural differences between the German culture and the Wal-Mart culture. The key issues faced by Wal-Mart were: * Unable to recognize the German consumer behaviour and culture in comparison with US * Entry to German market by acquisition strategy * Laws and regulations – zoning and limited stores hours * Failure to deliver on its legendary â€Å"every-day low prices† and â€Å"excellent service† value proposition. * Underdeveloped supply chain relationship * Absence of local brand name * Poor relationship with suppliers Situation Analysis Porter’s 5 Forces Model Bargaining Power of Customers – Very High * German customers not accustomed to friendly atmosphere * Presence of strong German competitors * Low cost from competitors Bargaining Power of Suppliers – Very High * Poor suppliers network * Centralized distribution system not accepted by German suppliers * Suppliers with strong connection to local players * Low switching cost due to abundance of retail companies Competitive Rivalry – High * Huge competition from local players like Metro, Aldi etc. *Show MoreRelatedCase Study on Walmart681 Words   |  3 PagesUnited States, WalMart pulled the plug on its German operations and left that market with its business tail stuck between its legs. Headlined in the Times was the fact that WalMart, the worlds largest retailer, abruptly pulled out of Germany yesterday (Times, 2006). WalMart sold the 85 stores it owned and operated to a competitor writing off approximately $1 billion. This is an interesting case study in that there are a variety of reasons behind WalMarts failure in Germany; not least amongRead MoreBest Buy S Turn Around Strategy13959 Words   |  56 Pagesas the best buy.7 As Joly saw it, Best Buy had a lot of strengths on which to build, in spite of its disappointing financials. It sold far more consumer electronics than either of its largest competitors ($50 billion compared to ~$30 billion for Walmart and $14 billion for Amazon), and dominated the PC, camera, and tablet categories in terms of market share. It had state-of-the-art logistics, inventory, and support systems that enabled it to make same-day deliveries for online orders. Meanwhile,Read MoreWalmart Case Study4489 Words   |  18 PagesSeptember 30, 2005, the Company had 1,253 Wal-Mart stores, 1,876 Supercenters, 555 Sam’s Clubs and 95 Neighborhood Markets in the United States. Internationally, the Company operated units in Argentina (11), Brazil (151), Canada (261), China (49), Germany (88), South Korea (16), Mexico (730), Puerto Rico (54) and the United Kingdom (295). Executive Summary Wal-Mart has grown into one of the largest discount retail stores in the world and has proven that the type of operation that they have isRead MoreMonsanto: Better Living Through Genetic Engineering96204 Words   |  385 Pagesof a link between cochlear implants and bacterial meningitis (a potentially fatal infection of the lining of the surface of the brain). There were 43 such cases and 11 people died. There were reports that implants had been withdrawn from sale in Germany, France and Spain. On 25 July the FDA updated its warning and said it had now learned of 118 cases.12 Cochlear responded to the crisis quickly. Graeme Clark claimed that the infection was related to a design change by their competitor, Advanced BionicsRead MoreManaging Information Technology (7th Edition)239873 Words   |  960 PagesCity CASE STUDY I-5 Data Governance at InsuraCorp CASE STUDY I-6 H.H. Gregg’s Appliances, Inc.: Deciding on a New Information Technology Platform CASE STUDY I-7 Midsouth Chamber of Commerce (B): Cleaning Up an Information Systems Debacle CASE STUDY II-1 Vendor-Managed Inventory at NIBCO CASE STUDY II-2 Real-Time Business Intelligence at Continental Airlines CASE STUDY II-3 Norfolk Southern Railway: The Business Intelligence Journey CASE STUDY II-4 Mining Data

Barley (Hordeum vulgare) - The History of Domestication

Barley (Hordeum vulgare ssp. vulgare) was one of the first and earliest crops domesticated by humans. Currently, archaeological and genetic evidence indicates barley is a mosaic crop, developed from several populations in at least five regions: Mesopotamia, the northern and southern Levant, the Syrian desert and, 900–1,800 miles (1,500–3,000 kilometers) to the east, in the vast Tibetan Plateau. The earliest domestication was long thought to be that of southwest Asia during the Pre-Pottery Neolithic A about 10,500 calendar years ago: but the mosaic status of barley has thrown a wrench into our understanding of this process. In the Fertile Crescent, barley is considered one of the classic eight founder crops. A Single Wild Progenitor Species The wild progenitor of all of the barleys is thought to be Hordeum spontaneum (L.), a winter-germinating species which is native to a very wide region of Eurasia, from the Tigris and Euphrates river system in Iraq to the western reaches of the Yangtze River in China. Based on evidence from Upper Paleolithic sites such as Ohalo II in Israel, wild barley was harvested for at least 10,000 years before it was domesticated. Today, barley is the fourth most important crop in the world after wheat, rice and maize. Barley as a whole is well-adapted to marginal and stress-prone environments, and a more reliable plant than wheat or rice in regions which are colder or higher in altitude. The Hulled and the Naked Wild barley has several characteristics useful to a wild plant that arent so useful to humans. There is a brittle rachis (the part that holds the seed to the plant) that breaks when the seeds are ripe, scattering them to the winds; and the seeds are arranged on the spike in a sparsely seeded two rows. The wild barley always has a tough hull protecting its seed; the hull-less form (called naked barley) is only found on domestic varieties. The domestic form has a non-brittle rachis and more seeds, arranged in  a six-rowed spike. Both hulled and naked seed forms are found in domesticated barley: during the Neolithic period, both forms were grown, but in the Near East, naked barley cultivation declined beginning in the Chalcolithic/Bronze Ages about 5000 years ago. Naked barleys, while easier to harvest and process, are more susceptible to insect attack and parasitic disease. Hulled barleys have higher yields; so within the Near East anyway, keeping the hull was a selected-for trait. Today hulled barleys dominate in the west, and naked barleys in the east. Because of the ease of processing, the naked form is used primarily as a whole-grain human food source. The hulled variety is used mainly for animal feed and the production of malt for brewing. In Europe, the production of barley beer dates at least as long ago as 600 B.C. Barley and DNA British archaeologist Glynis Jones and colleagues completed a phylogeographic analysis of barley in the northern fringes of Europe and in the Alpine region and found that cold adaptive gene mutations were identifiable in modern barley landraces. The adaptations included one type that was non-responsive to day length (that is, the flowering was not delayed until the plant got a certain number of hours of sunlight during the day): and that form is found in northeast Europe and high altitude locations. Alternatively, landraces in the Mediterranean region were predominantly responsive to day length. In central Europe, however, day length is not a trait which (apparently) had been selected for. Jones and colleagues were unwilling to rule out the actions of possible bottlenecks but suggested that temporary climate changes might have affected the selection of traits for various regions, delaying the spread of barley or speeding it, depending on the adaptability of the crop to the region. How Many Domestication Events!? Evidence exists for at least five different loci of domestication: at least three locations in the Fertile Crescent, one in the Syrian desert and one in the Tibetan Plateau. Jones and colleagues have reported additional evidence that in the region of the Fertile Crescent, there may have been up to four different domestication events of Asian wild barley. The differences within groups A-D are based on the presence of alleles which are differently adapted to day length; and the adaptive ability of barley to grow in a wide variety of locations. It could be that the combination of barley types from different regions created increased drought resistance and other beneficial attributes. U.S. botanist Ana Poets and colleagues identified a genome segment from the Syrian desert variety in Asian and Fertile Crescent barleys; and a segment in northern Mesopotamia in Western and Asian barleys. We do not know, said British archaeology Robin Allaby in an accompanying essay, how our ancestors produced such genetically diverse crops: but the study should kick off an interesting period towards a better understanding of the domestication processes in general. Evidence for barley beer making as early as Yangshao Neolithic (ca 5000 years ago) in China was reported in 2016; it seems most likely to have been from the Tibetan Plateau, but that has yet to be determined.   Sites Greece: Dikili TashIsrael: Ohalo IIIran: Ali Kosh, Chogha GolanIraq: JarmoJordan: Ain GhazalCyprus: Klimonas, Kissonerga-MylouthkiaPakistan: MehrgarhPalestine: JerichoSwitzerland: Arbon Bleiche 3Syria: Abu HureyraTurkey: Çatalhà ¶yà ¼kTurkmenistan: Jeitun Selected Sources Allaby, Robin G. Barley Domestication: The End of a Central Dogma? Genome Biology 16.1 (2015): 176. Dai, Fei, et al. Transcriptome Profiling Reveals Mosaic Genomic Origins of Modern Cultivated Barley. Proceedings of the National Academy of Sciences 111.37 (2014): 13403–08. Jones, G., et al. DNA Evidence for Multiple Introductions of Barley into Europe Following Dispersed Domestications in Western Asia. Antiquity 87.337 (2013): 701–13. Jones, Glynis, et al. Phylogeographic Analysis of Barley DNA as Evidence for the Spread of Neolithic Agriculture through Europe. Journal of Archaeological Science 39.10 (2012): 3230–38.Mascher, Martin, et al. Genomic Analysis of 6,000-Year-Old Cultivated Grain Illuminates the Domestication History of Barley. Nature Genetics 48 (2016): 1089. Pankin, Artem, et al. Targeted Resequencing Reveals Genomic Signatures of Barley Domestication. New Phytologist 218.3 (2018): 1247–59. Pankin, Artem, and Maria von Korff. Co-Evolution of M ethods and Thoughts in Cereal Domestication Studies: A Tale of Barley (Hordeum Vulgare). Current Opinion in Plant Biology 36 (2017): 15–21. Poets, Ana M., et al. The Effects of Both Recent and Long-Term Selection and Genetic Drift Are Readily Evident in North American Barley Breeding Populations. G3: Genes|Genomes|Genetics 6.3 (2016): 609–22.

Review of New Types of Relation Extraction Methods Free Essays

This is explained by the fact that patterns do not tend to uniquely identify the given relation. The systems which participated in MUCH and deal with relation extraction also rely on rich rules for identifying relations (Fought et al. 1 998; Gargling et al. We will write a custom essay sample on Review of New Types of Relation Extraction Methods or any similar topic only for you Order Now 1998; Humphreys et al. 1998). Humphreys et al. 1998) mention that they tried to add only those rules which were (almost) certain never to generate errors in analysis; therefore, they had adopted a low recall and high precision approach. However, in this case, many relations may be missed due to the lack of unambiguous rules to extract them. To conclude, knowledge-based methods are not easily portable to other domains and involve too much manual labor. However, they can be used effectively if the main aim is to get results quickly in well-defined domains and document collections. 5 Supervised Methods Supervised methods rely on a training set where domain-specific examples eave been tagged. Such systems automatically learn extractors for relations by using machine-learning techniques. The main problem of using these methods is that the development of a suitably tagged corpus can take a lot of time and effort. On the other hand, these systems can be easily adapted to a different domain provided there is training data. There are different ways that extractors can be learnt in order to solve the problem of supervised relation extraction: kernel methods (Shoo and Grossman 2005; Bunches and Mooney 2006), logistic regression (Kamala 2004), augmented parsing (Miller et al. 2000), Conditional Random Fields CRY) (Calcutta et al. 2006). In RE in general and supervised RE in particular a lot of research was done for IS-A relations and extraction of taxonomies. Several resources were built based on collaboratively built Wisped (YOGA – (Issuance et al. 2007); Depended – (Rue et al. 2007); Freebase – (Blacker et al. 2008); Wicking (Instates et al. 2010)). In general, Wisped is becoming more and more popular as a source for RE. E. G. (Opponent and Strobe 2007; Unguent et al. AAA, b, c). Query logs are also considered a valuable source of information for RE and their analysis is even argued to give better results than other suggested methods in the field (Passes 2007, 2009). 5. 19 Weakly-supervised Methods Some supervised systems also use bootstrapping to make construction of the training data easier. These methods are also sometimes referred to as â€Å"huckleberries information extraction†. Bring (1998) describes the DIPPER (Dual Iterative Pattern Relation Expansion) method used for identifying authors of the books. It uses an initial small set of seeds or a set of hand- constructed extraction patterns to begin the training process. After the occurrences of needed information are found, they are further used for recognition of new patterns. Regardless of how promising bootstrapping can seem, error propagation becomes a serious problem: mistakes in extraction at the initial stages generate more mistakes at later stages and decrease the accuracy of the extraction process. For example, errors that expand to named entity recognition, e. G. Extracting incomplete proper names, result in choosing incorrect seeds for the next step of bootstrapping. Another problem that can occur is that of semantic drift. This happens when senses of the words are not taken into account and therefore each iteration results in a move from the original meaning. Some researchers (Korea and How 2010; Hove et al. 2009; Korea et al. 2008) have suggested ways to avoid this problem and enhance the performance of this method by using doubly- anchored patterns (which include both the class name and a class member) as well as graph structures. Such patterns have two anchor seed positions â€Å"{type} such as {seed} and *† and also one open position for the terms to be learnt, for example, pattern â€Å"Presidents such as Ford and {X}† can be used to learn names of the presidents. Graphs are used for storing information about patterns, found words and links to entities they helped to find. This data is further used for calculating popularity and productivity of the candidate words. This approach helps to enhance the accuracy of bootstrapping and to find high-quality information using only a few seeds. Korea (2012) employs a similar approach for the extraction Of cause-effect relations, where the pattern for bootstrapping has a form of â€Å"X and Y verb Z†, for example, and virus cause Human-based evaluation reports 89 % accuracy on 1500 examples. Self-supervised Systems Self-supervised systems go further in making the process of information extraction unsupervised. The Knolling Web II system (Edition et al. 2005), an example of a self-supervised system, learns â€Å"to label its own training examples using only a small set of domain-independent extraction patterns†. It uses a set of generic patterns to automatically instantiate relation-specific extraction rules and then learns domain-specific extraction rules and the whole process is repeated iteratively. The Intelligence in Wisped (IPP) project (Weld et al. 2008) is another example of a self-supervised system. It bootstraps from the Wisped corpus, exploiting the fact that each article corresponds to a primary object and that any articles contain infusions (brief tabular information about the article). This system is able to use Wisped infusions as a starting point for training 20 the classifiers for the page type. IPP trains extractors for the various attributes and they can later be used for extracting information from general Web pages. The disadvantage of IPP is that the amount of relations described in Wisped infusions is limited and so not all relations can be extracted using this method. . 1 Open Information Extraction Edition et al. (2008) introduced the notion of Open Information Extraction, which is opposed to Traditional Relation Extraction. Open information extraction is â€Å"a novel extraction paradigm that tackles an unbounded number of relations†. This method does not presuppose a predefined set of relations and is targeted at all relations that can be extracted. The Open Relation extraction approach is relatively a new one, so there is only a small amount of projects using it. Texturing (Bank and Edition 2008; Bank et al. 2007) is an example of such a system. A set of relinquishment’s lexicon-syntactic patterns is used to build a relation- independent extraction model. It was found that 95 % Of all relations in English can be described by only 8 general patterns, e. G. â€Å"El Verb E â€Å". The input of such a system is only a corpus and some relation-independent heuristics, relation names are not known in advance. Conditional Random Fields (CRY) are used to identify spans of tokens believed to indicate explicit mentions of relationships between entities and the whole problem of relation extraction is treated as a problem of sequence labeling. The set of linguistic features used in this system is similar to those used by other state of-the-art relation extraction systems and includes e. G. Part-of-speech tags, regular expressions for detection of capitalization and punctuation, context words. At this stage of development this system â€Å"is able to extract instances of the four most frequently observed relation types: Verb, Noun+Prep, Verb+Prep and Infinitive†. It has a number of limitations, which are however common to all RE systems: it extracts only explicitly expressed relations that are primarily word-based; relations should occur between entity names within the same sentence. Bank and Edition (2008) report a precision of 88. 3 % and a recall of 45. 2 Even though the system shows very good results the relations are not pacified and so there are difficulties in using them in some other systems. Output Of the system consists Of tepees stating there is some relation between two entities, but there is no generalization of these relations. Www and Weld (2010) combine the idea of Open Relation Extraction and the use of Wisped infusions and produce systems called Weepers and Weeps . Weepers improves Texturing dramatically but it is 30 times slower than Texturing. However, Weeps does not have this disadvantage and still shows an improved F-measure over Texturing between 1 5 % to 34 % on three corpora. Fader et al. 201 1) identify several flaws in previous works in Open Information Extraction: â€Å"the learned extractors ignore both â€Å"holistic† aspects of the relation phrase (e. G. , is it contiguous? ) as well as lexical aspects (e. G. , how many instances of this relation are there? )†. They target these problems by introducing syntactic constraints (e. G. , they require the relation phrase to match the POS tag 21 pattern) and lexical constraints. Their system Revere achieves an AUK which is 30 % better than WOE (Www and Weld 201 0) and Texturing (Bank and Denton 2008). Unshackles et al. (AAA) approach this problem from another angle. They try to mine for patterns expressing various relations and organism then in hierarchies. They explore binary relations between entities and employ frequent items mining (Augural et al. 1993; Syrians and Augural 1 996) to identify the most frequent patterns. Their work results in a resource called PATTY which contains 350. 69 pattern sunsets and substitution relations and achieves 84. 7 % accuracy. Unlike Revere (Fader et al. 201 1) which constrains patterns to verbs or verb phrases that end with prepositions, PATTY can learn arbitrary patterns. The authors employ so called syntactic- ontological-lexical patterns (SOL patterns). These patterns constitute a sequence of words, POS-tags, wildcats, and ontological types. For example, the pattern â€Å"persons [ads] voice * song† would match the strings my Heinousness soft voice in Rehab and Elvis Presley solid voice in his song All shook up. Their approach is based on collecting dependency paths from the sentences where two named entities are tagged (YACHT (Hoffa et al. 2011) is used as a database of all Ones). Then the textual pattern is extracted by finding the shortest paths connecting two entities. All of these patterns are transformed into SOL (abstraction of a textual pattern). Frequent items quinine is used for this: all textual patterns are decomposed into n-grams (n consecutive words). A SOL pattern contains only the n-grams that appear frequently in the corpus and the remaining word sequences are replaced by wildcats. The support set of the pattern is described as the set of pairs of entities that appear in the place Of the entity placeholders in all strings in the corpus that match the pattern. The patterns are connected in one sunset (so are considered synonymous) if their supporting sets coincide. The overlap of the supporting sets is also employed to identify substitution relations between various sunsets. . 2 Distant Learning Mint et al. (2009) introduce a new term â€Å"distant supervision†. The authors use a large semantic database Freebase containing 7,300 relations between 9 million named entities. For each pair of entities that appears in Freebase relation, they identify all sentences containing those entities in a large unlabeled corpus. At the next step textual features to train a relation classifier are extracted. Even though the 67,6 % of precision achieved using this method has room for improvement, it has inspired many researchers to further investigate in this direction. Currently there are a number of papers ring to enhance â€Å"distant learning† in several directions. Some researchers target the heuristics that are used to map the relations in the databases to the texts, for example, (Takeouts et al. 01 2) argue that improving matching helps to make data less noisy and therefore enhances the quality of relation extraction in general. Hay et al. (2010) propose using an undirected graphical model for relation extraction which employs â€Å"distant learning’ but enforces selection preferences. Ridded et al. (2010) reports 31 % error reduction compared to (Mint et al. 2009). 22 Another problem that has been addressed is language ambiguity (Hay et al. 01 1, 2012). Most methods cluster shallow or syntactic patterns of relation mentions, but consider only one possible sense per pattern. However, this assumption is often violated in reality. Hay et al. (201 1) uses generative probabilistic models, where both entity type constraints within a relation and features on the dependency path between entity mentions are exploited. This research is similar to DIRT (Line and Panatela 2001 ) which explores distributional similarity of dependency paths in order to discover different representations of the same semantic relation. However, Hay et al. (2011) employ another approach and apply IDA (Belie et al. 2003) with a slight modification: observations are relation tepees and not words. So as a result of this modification instead of representing semantically related words, the topic latent variable represents a relation type. The authors combine three models: Reel-LAD, Reel-LDAP and Type-LAD. In the third model the authors split the features of a duple into relation level features and entity level features. Relation level features include the dependency path, trigger, lexical and POS features; entity level features include the entity mention itself and its named entity tag. These models output clustering of observed relation tepees and their associated textual expressions. 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Conclusion Of An Inspector Calls Essay Example For Students

Conclusion Of An Inspector Calls Essay JB Priestleys Play of An Inspector Calls is about a family called the Birlings. They are spending a happy evening celebrating the engagement of Sheila Birling to Gerald Croft, a marriage that will result in the merging of two successful local businesses. In addition, just when everything seems to be going so well, they receive a surprise visit from an Inspector Goole who is investigating the suicide of a young girl. The questions he asks to each character are relating to the case, this reveals that they all have secrets that link them to the tragedy. The main core of the play is about the investigation of the Inspector into the death of Eva smith. Moreover, it is really a way of putting across the authors political thoughts. Priestley has put his own political message across and he has used the characters in the play to do this. Priestly has also used the Birling family and the investigation into their part in her downfall, to make it less like a straight forward political speech, and a way to engage the audience and win their empathy. The most distinguishable dramatic technique used is the way the characters show the authors way of thinking, in which they act. This can be seen through each member of the family, therefore each one has a special role representing to us, something that Priestley is trying to destroy. To embark on, the first character is the man of the house, Mr Arthur Birling. Mr Birling seems to be a rich, irritable, self-centred person. Birling is very much confident in himself; he feels that his success from his small business is due to the fact hes a hard-headed practical man of business. He is also confident that strikes and labour troubles would not be a problem in the future and refers to fears of war as silly little war scares. However, this play was first performed in 1946 after WW2, so Birlings predictions would have sounded pretty daft to the audience. As for the wider world, Birling thinks it is every man for himself, a man has to make his own way and look after the family. It is survival of the fittest. The other thing that is wrong with Birling is that he always thinks of himself first, and honestly believes that it is the only way to get through life. That makes it hard for him to understand other peoples opinions If we were all responsible for everything that happened to everybody wed ever had anything to do with, it would be very awkward, wouldnt it? Birling is obsessed with how things appear to people. His main concern is his public image is going to be affected. He does not want the story to come out publicly and ruin him for good.