Frederick Douglass Essay Example for Free

Frederick Douglass Essay Frederick Douglass was brought into the world a slave in 1818, when slaves were prohibited to have instruction he prevailing with regards to instructing himself to peruse and compose. In Frederick Douglass’ Learning to Read, the crowd was given a fantastic view that permitted a brief look inside the genuine profundity and degree of bondage. Douglass communicated accentuation on proficiency and the effect it had on bondage by uncovering how subjugation was hindering not exclusively to slaves however slave proprietors, how the way to instruct himself caused mental anguish, and how education turned into his key to opportunity. In the first place, the master’s spouse saw Frederick as her equivalent and didn’t see anything amiss with teaching him. Douglass said of his first instructor â€Å"She from the outset did not have the degeneracy basic to quieting me down in mental obscurity (346), at that point she understood that teaching a slave implied giving them a voice. Bondage had the ability to transform a sort and caring individual into an insensitive and barbarous savage. â€Å"Under its impact, the delicate heart got stone, and the lamblike air offered approach to one of tiger like fierceness† (Douglass 346). She stopped to teach him and ensured no one else would. â€Å"Mistress, in showing me the letter set, had offered me the bit of leeway, and no safeguard could keep me from taking ell† (347). Frederick Douglass was a splendid man and resolved to figure out how to peruse. Douglass transformed kids into educators and through a trade of bread effectively figured out how to peruse. In Learning to Read, Douglass needed to name the young men who helped him as â€Å"a tribute of the appreciation and friendship I bear them†(347), however rather expressed where they lived. Douglass expounds on the means he took when figuring out how to peruse and goes as far to incorporate where the youngsters experienced that assist him with succeeding sets up exact rationale. The way Frederick Douglass went to seek after his training was an exciting ride of feelings. Douglass was twelve when he ran over the book The Columbian Orator, it contained material that revolted against subjugation, and with trust readily available he encountered reality. â€Å"behold! That very dissatisfaction which Master Hugh had anticipated would follow my figuring out how to peruse has just come, to torment and sting my spirit to unutterable anguish. †(Douglass 348). He was as yet a slave, not, at this point uninformed of reality yet at the same time without the appropriate response. â€Å"I frequently got myself lamenting my own reality, and wishing myself dead; and yet for the desire for being free† Slavery was awful to such an extent that he begrudged the confused slaves and even mulled over death, yet it was trust that spared him. Douglass’ utilization of stacked language advances to the feelings of the crowd. In Learning to Read, Douglass is anxious to hear the word abolitionists, in spite of the fact that he didn’t recognize what it implied he connected the word with trust. â€Å"If a slave fled and prevailing with regards to getting clear, or if a slave executed his lord, put a match to a horse shelter, or did anything extremely wrong in the psyche of a slaveholder, it was talked about as the product of abolition†(348,349). From a city paper he finds out about the request to abrogate servitude in the District of Columbia, and at the dock he is urged to out of control toward the north, where he could be free. Douglass composed â€Å"I supported myself with trust that I should one day locate a decent possibility. In the mean time, I would figure out how to compose. †(349) A chunk of chalk, any strong surface and another sharp strategy would give Douglass the apparatuses important to figure out how to compose. Frederick Douglas was a slave who prevailing with regards to figuring out how to peruse and compose sets up his validity and authority. Douglass’ sees on the significance of proficiency and the effect it had on servitude was compelling by precisely utilizing rationale, speaking to feelings, and building up moral believability In Learning to Read, Frederick Douglass gives a direct record of the battles he looked to free himself, intellectually and genuinely, from subjugation. Through his tirelessness to figure out how to peruse and compose he finds that information is the way to opportunity.

Coptic Egyptian and Christian Nubian painting Essay

Coptic Egyptian and Christian Nubian painting - Essay Example The exposition Coptic Egyptian and Christian Nubian painting contrasts Egyptian artistic creation and Nubian painting and investigates what do their topics enlighten us regarding the financial existence of these social orders and their ideological standpoint. An investigation of the region’s history and ancient rarities uncover its experiences with Pharaoh’s Egypt, the Nubian Kingdoms’ obvious change into Christendom, and the arrangement of Muslim and Arab characters in the later past. Researchers devoted to the investigation of Egyptian legislative issues and history have a considerable amount to derive from canvases found in Nubian and Coptic places of worship of antiquated occasions. The areas that follow are devoted to the examination of Coptic and Nubian Christian pictures and culture during the period somewhere in the range of 500 and 1000 AD. Nubia alludes to the district that lies in northern Sudan and south of Egypt along the Nile. With quarter of its re gion lying in Egypt, and the greater part of itself lying in Sudan, old Nubia was officially a self-overseeing realm. In 373 AD, Bishop Athanasius sanctified as diocesan of Philae Marcus in a show that denoted the infiltration of Christianity in the fourth century. In 545, a Monophysite cleric, Julian, is recorded to have prompted the King’s change along with a few of his aristocrats. Around the same time, different records propose, the Makuria Kingdom was changed over to Catholism by Byzantine evangelists. As time passed by, Arab brokers acquainted Islam with Nubia which gradually superseded Christianity. It is noticed that though there could have been a diocesan.

How to Optimize Supply Chain Management with Big Data

How to Optimize Supply Chain Management with Big Data It has been said that Big Data has applications at all levels of a business. This is definitely true of supply chain management the optimization of a firm’s supply-side business activities, such as new product development, production, and product distribution, to maximize revenue, profits, and customer value. Big Data management has tremendous implications for supply chain management. Firms that can aggregate, filter, and analyze internal data, as well as external consumer and market data, can use the insights generated to optimize decision-making at all levels of the supply chain.However, while many firms have noted the tremendous potential of Big Data for supply chain management yet not integrated it into their operations because they lack the financial, technological or human resources to do so. While these are clearly challenges, it is estimated that the digital universe will be over 40 trillion gigabytes by 2020 â€" a significant portion of that being data that can be leverag ed to generate business insights. As time passes, those firms who have integrated Big Data into their supply chains, and both scale and refine that infrastructure will likely have a decisive competitive advantage over those that do not. © Shutterstock.com | Mascha TaceIn this article, we will cover 1) the benefits of Big Data for supply chain management, including its role in 2) real-time delivery tracking, 3) optimized supplier chain management, 4) automatic product sourcing, 5) customized production and service, and 6) optimized pricing, as well as 7) building a Big Data supply chain, and 8) the future of Big Data and supply chain management.BENEFITS OF BIG DATA FOR SUPPLY CHAIN MANAGEMENTBy strengthening its supply chain, a firm can get the products and services a consumer wants to them quickly and efficiently. Firms that demonstrate such value to consumers can increase repeat purchase behavior, deepen consumer brand loyalty, and derive more value (purchases and referrals) from the customer over his or her lifetime.To leverage this opportunity fully requires the firm to analyze internal and external data for decision-making efficiently. The management tools and techniques that have evolved for use with Big Data such as real-time business intelligence systems, data mining, and predictive analytics, can be leveraged to make fulfillment more efficient and profitable; optimize both supply costs and pricing to maximize profits; automate product sourcing; and deploy mass customization product strategies.REAL-TIME DELIVERY TRACKINGBig Data’s management systems include real-time analytics solutions that can be used to strengthen fulfillment. These systems include both Big Data hardware/software for warehousing and processing and inputs from bar-codes, radio frequency identification (RFID) tags, global positioning systems (GPS) devices, among others. Such systems can capture traffic sensor data, road network data, and vehicle data, in real-time to allow logistics managers the capacity to optimize delivery scheduling. They can address unforeseen events (such as accidents and inclement weather) effectively; track packages and vehicles in real-time no matter where they are; automate notices sent to customers in the event of a delay; and provide customers with real-time delivery status updates. Firms can also aggregate and filter relevant unstructured data from sources, such as social networking sites for insights on the delivery process, and respond to issues in real-time.Further, vehicle sensor information can be used for predictive maintenance â€"maximizing the life of business equipment (in this case, vehicles and transportation-related equipment such as forklifts) by scheduling preventive maintenance based on current and historical data.Transportation data, when integrated into a commercial or in-house implementation of a distributed file system, such as Hadoop, a network-based one like Gluster, or other similar system, can be leveraged by other strategic business units. For example, a firm can configure its transportation business intelligence system to route notification of delivery delays to customer service centers automatically; customer service representatives can th en anticipate, and respond to, customer complaints appropriately.OPTIMIZED SUPPLIER MANAGEMENTTo maximize profits, firms want to sell the most products at the lowest costs. Cost determinations become increasingly complex the more raw materials used to produce a product, the greater the variability in the price of those inputs, the more products the firm offers, and the larger the geographical distribution area. The supplier relationship management process â€" which once, for many firms, had more to do with drinks, golf games, and other shared social experiences â€" these days, must incorporate more quantitative measures to determine whether the firm is receiving the most bang for its buck.Big Data allows firms to develop complex mathematical models that forecast margins if different mixes of suppliers are chosen. These models can take into account a wide range of variables, such as the additional costs due to variations in the speed with which different suppliers can deliver their g oods; one-time switching costs, such as long-term contract cancellations; and even estimates of supplier reliability, which firms can use to generate performance predictions of various supplier mixes. Managers can then select those with the highest return on the lowest investment to maximize profits.OPTIMIZED PRICINGSimilar to supplier selection, Big Data has many benefits for pricing. Firms can use consumer data, from both internal and external sources, to develop pricing models that maximize profit margins, and use predictive analytics tools to forecast demand for a particular product at different price points. Firms can then test these price points with soft launches, and incorporate consumer behavior and feedback â€" both quantitative and qualitative â€" into their pricing strategies. Further, firms can develop models to determine which combinations of related products consumers are likely to buy together, and use this information to develop and refine upselling strategies.Anoth er application of Big Data management and analysis to pricing involves sales forecasting. Firms can use predictive analytics to make real-time predictions about the firm’s sales performance overall, in a region, or even a specific location; they can adjust pricing to ensure that they meet those projections when necessary. Dynamic pricing can also be used to maximize revenue during times of increased market demand and/or supply shortages. Common in ground and air transportation during the holidays, dynamic pricing allows operators to increase prices for empty bus, plane, and train tickets when empty seats are scarce. However, industries ranging from hotels to sports entertainment to retail employ dynamic pricing to increase revenue.CUSTOMIZED PRODUCTION AND SERVICEBig Data collected to optimize supply chain management often holds key insights about consumer needs and wants. Firms can leverage these insights to develop new product and/or brand extensions, where sufficient consumer d emand warrants. In many cases, economies of scale reduce the costs of product extensions to the point where the additional costs are negligible. For example, a firm might introduce a jacket in three different colors, but through an analysis of aggregated social media mentions, customer service feedback, and online reviews, release the product in a fourth color. This is known as cosmetic customization.Many firms also leverage economies of scale to employ a mass customization strategy â€" one where customers provide firms with product features for common products, and the firm builds the product to the customer’s specifications. Auto manufacturers often employ this strategy, manufacturing large volumes of common components, and then allowing users to “build” their car by inputting desired features on the corporate website. However, many firms, from eyewear designers to toy companies, use this strategy, known as collaborative customization.Other firms, such as software firms, emp loy adaptive customization, which provides users with products that consumers can then customize themselves, according to their changing needs and desires. Still others employ transparent customization, wherein customers do not know that firms have customized products specifically for them. Often, this is employed not only with product manufacturing but also with fulfillment: firms analyze consumers’ usage patterns of commodities, and produce and offer, and distribute replacements when needed.In addition to adding value for the consumer, mass customization enhances a personalized purchase experience considerably, deepening both brand engagement and loyalty. Firms often use Big Data, including supply chain data to personalize their customer service experience. Firms with effective customer service departments integrate all available data about a consumer, including relevant supply chain data (such as a history of on-time and delayed deliveries, for example) into files available to customer service representatives. Having that data at their fingertips helps customer service reps address customer inquiries received.Firms can even use this data to anticipate such inquiries and respond proactively. For example, a firm might face greater demand for a particular product than they have inventory to meet. In such a case where the product has a lengthy manufacturing and/or distribution time, the firm can reach out to those who have placed orders with an explanation and apology for the delay; they can also update their website to notify new customers of the delay.AUTOMATIC PRODUCT SOURCINGIn late 2013, Amazon filed a patent in the U.S. for the process of predictive shipping â€" a distribution method wherein a firm uses predictive analytics to forecast future sales based on historical data; they then source and ship products to local and/or regional distribution centers in advance of those orders. It remains to be seen how successful this method may be, yet given Amazon ’s pioneering success in the online retail space, driven in no small part by its embrace of Big Data management tools, techniques and technologies, it would be tough to bet against them.Twelve years earlier, the firm filed a patent for automated product sourcingâ€" a process and its related technologies that played no small part in Amazon’s success; it has since been replicated by many other online retailers to varying degrees of success. Automated process sourcing refers to a firm’s ability to, upon receipt of a customer order, analyze inventory at multiple fulfillment centers, estimate delivery times, and return multiple delivery options (at different price points) to the customer in real-time. This enhances value for the customer, and allows Amazon to optimize distribution, as well as inventory management. Many other firms, from Best Buy to eBay, have either developed their own automated product sourcing systems or purchased software and process management solutions from ve ndors.BUILDING A BIG DATA SUPPLY CHAINThe benefits of paring Big Data with supply chain management make it an obvious choice; the ever-accelerating volume, velocity, and variety of data make it a necessary one. However, integrating Big Data into a firm’s supply chain is more involved than releasing a management directive or signing a purchase order.It is often advisable to start with individual links on the supply chain â€" such as departments, build Big Data into their operations, and replicate their successes across the organizations. The buy-in from this approach will help managers mitigate internal resistance to an innovation many find abstract or overwhelming. Executives and managers must review (and where needed update) the strategic business goals that drive the specific operational unit.For example, a corporate fleet might count as KPIs on-time deliveries, cost per delivery measured in fuel, wear and tear, and other measures, delivery times, positive customer feedback, lac k of negative customer feedback, and other similar indicators. Internal data scientist leads should work with must work with executives and managers (in this case the management team of the corporate fleet) to create operational goals and insights that drive these goals. For example, such insights might include the optimal time by which deliveries must be made to elicit positive customer feedback, optimal delivery routes that minimize cost per delivery and delivery times in real-time, and others that can allow the corporate fleet to add value to the organization as a whole. Data scientists then must work with I.T. (and vendors where necessary) to develop a Big Data infrastructure that allows them to meet these goals.Fundamentally, such architecture would include hardware/software and internal procedures and protocols for collecting, processing, and storing existing and new data, in real-time where possible and necessary. This architecture would also allow data scientists to clean, s earch, and filter data pre-analysis, analyze it as necessary, generate useful reports, and share actionable insights across the organization, and in some cases, to consumers. Further, this architecture must be scalable â€" as the volume of data will only grow, and secure, as a failure to maintain the privacy of consumer data can be a tremendously expensive mistake. Such architecture should communicate with existing (or new) customer relationship management systems and provide real-time intelligence to provide the most value for internal and external stakeholders.THE FUTURE OF BIG DATA AND SUPPLY CHAIN MANAGEMENTSeveral innovations and trends will not only accelerate the volume of data as a whole, but also the volume of data relevant to supply chain management. Mobile will continue to provide a major source of supply-chain relevant data, driven by the GPS technology in mobile devices, as well as the proliferation of social networks specializing in social discovery, which allows users to discover people and events of interest based on location. Deep analysis of consumer location information can afford firms even greater efficiency at getting products to consumers, whether through optimizing the locations of regional fulfillment centers or even distribution of products at those events and venues well frequented by its consumers.The Internet of Things â€" the attachment of sensors and other digital technologies to traditionally non-digital products to capture data, are currently, and will continue to be a major source of data of use to data scientists working on supply chain optimization. For example, a smart device can be built to send messages to the manufacturer when they are broken, which can generate production on a replacement part or full device, before its owner calls customer service. If the device is outmoded, its signal to the manufacturing firm can provide the customer service representative (and/or sales staff) with the information to prepare for an u psell.Cloud computing itself has driven Big Data’s growth significantly, as its inherent digitization of a firm’s operational data demands new methods to leverage it. As more firms take advantage of the benefits of cloud computing (such as reduced capital costs, economies of scale, and increased flexibility), adoption of Big Data’s management tools and techniques will grow. Moreover, as it grows, firms will demand increasingly sophisticated business intelligence systems, methods of predictive analysis, and tools for data mining, which the market will provide.

How to Optimize Supply Chain Management with Big Data

How to Optimize Supply Chain Management with Big Data It has been said that Big Data has applications at all levels of a business. This is definitely true of supply chain management the optimization of a firm’s supply-side business activities, such as new product development, production, and product distribution, to maximize revenue, profits, and customer value. Big Data management has tremendous implications for supply chain management. Firms that can aggregate, filter, and analyze internal data, as well as external consumer and market data, can use the insights generated to optimize decision-making at all levels of the supply chain.However, while many firms have noted the tremendous potential of Big Data for supply chain management yet not integrated it into their operations because they lack the financial, technological or human resources to do so. While these are clearly challenges, it is estimated that the digital universe will be over 40 trillion gigabytes by 2020 â€" a significant portion of that being data that can be leverag ed to generate business insights. As time passes, those firms who have integrated Big Data into their supply chains, and both scale and refine that infrastructure will likely have a decisive competitive advantage over those that do not. © Shutterstock.com | Mascha TaceIn this article, we will cover 1) the benefits of Big Data for supply chain management, including its role in 2) real-time delivery tracking, 3) optimized supplier chain management, 4) automatic product sourcing, 5) customized production and service, and 6) optimized pricing, as well as 7) building a Big Data supply chain, and 8) the future of Big Data and supply chain management.BENEFITS OF BIG DATA FOR SUPPLY CHAIN MANAGEMENTBy strengthening its supply chain, a firm can get the products and services a consumer wants to them quickly and efficiently. Firms that demonstrate such value to consumers can increase repeat purchase behavior, deepen consumer brand loyalty, and derive more value (purchases and referrals) from the customer over his or her lifetime.To leverage this opportunity fully requires the firm to analyze internal and external data for decision-making efficiently. The management tools and techniques that have evolved for use with Big Data such as real-time business intelligence systems, data mining, and predictive analytics, can be leveraged to make fulfillment more efficient and profitable; optimize both supply costs and pricing to maximize profits; automate product sourcing; and deploy mass customization product strategies.REAL-TIME DELIVERY TRACKINGBig Data’s management systems include real-time analytics solutions that can be used to strengthen fulfillment. These systems include both Big Data hardware/software for warehousing and processing and inputs from bar-codes, radio frequency identification (RFID) tags, global positioning systems (GPS) devices, among others. Such systems can capture traffic sensor data, road network data, and vehicle data, in real-time to allow logistics managers the capacity to optimize delivery scheduling. They can address unforeseen events (such as accidents and inclement weather) effectively; track packages and vehicles in real-time no matter where they are; automate notices sent to customers in the event of a delay; and provide customers with real-time delivery status updates. Firms can also aggregate and filter relevant unstructured data from sources, such as social networking sites for insights on the delivery process, and respond to issues in real-time.Further, vehicle sensor information can be used for predictive maintenance â€"maximizing the life of business equipment (in this case, vehicles and transportation-related equipment such as forklifts) by scheduling preventive maintenance based on current and historical data.Transportation data, when integrated into a commercial or in-house implementation of a distributed file system, such as Hadoop, a network-based one like Gluster, or other similar system, can be leveraged by other strategic business units. For example, a firm can configure its transportation business intelligence system to route notification of delivery delays to customer service centers automatically; customer service representatives can th en anticipate, and respond to, customer complaints appropriately.OPTIMIZED SUPPLIER MANAGEMENTTo maximize profits, firms want to sell the most products at the lowest costs. Cost determinations become increasingly complex the more raw materials used to produce a product, the greater the variability in the price of those inputs, the more products the firm offers, and the larger the geographical distribution area. The supplier relationship management process â€" which once, for many firms, had more to do with drinks, golf games, and other shared social experiences â€" these days, must incorporate more quantitative measures to determine whether the firm is receiving the most bang for its buck.Big Data allows firms to develop complex mathematical models that forecast margins if different mixes of suppliers are chosen. These models can take into account a wide range of variables, such as the additional costs due to variations in the speed with which different suppliers can deliver their g oods; one-time switching costs, such as long-term contract cancellations; and even estimates of supplier reliability, which firms can use to generate performance predictions of various supplier mixes. Managers can then select those with the highest return on the lowest investment to maximize profits.OPTIMIZED PRICINGSimilar to supplier selection, Big Data has many benefits for pricing. Firms can use consumer data, from both internal and external sources, to develop pricing models that maximize profit margins, and use predictive analytics tools to forecast demand for a particular product at different price points. Firms can then test these price points with soft launches, and incorporate consumer behavior and feedback â€" both quantitative and qualitative â€" into their pricing strategies. Further, firms can develop models to determine which combinations of related products consumers are likely to buy together, and use this information to develop and refine upselling strategies.Anoth er application of Big Data management and analysis to pricing involves sales forecasting. Firms can use predictive analytics to make real-time predictions about the firm’s sales performance overall, in a region, or even a specific location; they can adjust pricing to ensure that they meet those projections when necessary. Dynamic pricing can also be used to maximize revenue during times of increased market demand and/or supply shortages. Common in ground and air transportation during the holidays, dynamic pricing allows operators to increase prices for empty bus, plane, and train tickets when empty seats are scarce. However, industries ranging from hotels to sports entertainment to retail employ dynamic pricing to increase revenue.CUSTOMIZED PRODUCTION AND SERVICEBig Data collected to optimize supply chain management often holds key insights about consumer needs and wants. Firms can leverage these insights to develop new product and/or brand extensions, where sufficient consumer d emand warrants. In many cases, economies of scale reduce the costs of product extensions to the point where the additional costs are negligible. For example, a firm might introduce a jacket in three different colors, but through an analysis of aggregated social media mentions, customer service feedback, and online reviews, release the product in a fourth color. This is known as cosmetic customization.Many firms also leverage economies of scale to employ a mass customization strategy â€" one where customers provide firms with product features for common products, and the firm builds the product to the customer’s specifications. Auto manufacturers often employ this strategy, manufacturing large volumes of common components, and then allowing users to “build” their car by inputting desired features on the corporate website. However, many firms, from eyewear designers to toy companies, use this strategy, known as collaborative customization.Other firms, such as software firms, emp loy adaptive customization, which provides users with products that consumers can then customize themselves, according to their changing needs and desires. Still others employ transparent customization, wherein customers do not know that firms have customized products specifically for them. Often, this is employed not only with product manufacturing but also with fulfillment: firms analyze consumers’ usage patterns of commodities, and produce and offer, and distribute replacements when needed.In addition to adding value for the consumer, mass customization enhances a personalized purchase experience considerably, deepening both brand engagement and loyalty. Firms often use Big Data, including supply chain data to personalize their customer service experience. Firms with effective customer service departments integrate all available data about a consumer, including relevant supply chain data (such as a history of on-time and delayed deliveries, for example) into files available to customer service representatives. Having that data at their fingertips helps customer service reps address customer inquiries received.Firms can even use this data to anticipate such inquiries and respond proactively. For example, a firm might face greater demand for a particular product than they have inventory to meet. In such a case where the product has a lengthy manufacturing and/or distribution time, the firm can reach out to those who have placed orders with an explanation and apology for the delay; they can also update their website to notify new customers of the delay.AUTOMATIC PRODUCT SOURCINGIn late 2013, Amazon filed a patent in the U.S. for the process of predictive shipping â€" a distribution method wherein a firm uses predictive analytics to forecast future sales based on historical data; they then source and ship products to local and/or regional distribution centers in advance of those orders. It remains to be seen how successful this method may be, yet given Amazon ’s pioneering success in the online retail space, driven in no small part by its embrace of Big Data management tools, techniques and technologies, it would be tough to bet against them.Twelve years earlier, the firm filed a patent for automated product sourcingâ€" a process and its related technologies that played no small part in Amazon’s success; it has since been replicated by many other online retailers to varying degrees of success. Automated process sourcing refers to a firm’s ability to, upon receipt of a customer order, analyze inventory at multiple fulfillment centers, estimate delivery times, and return multiple delivery options (at different price points) to the customer in real-time. This enhances value for the customer, and allows Amazon to optimize distribution, as well as inventory management. Many other firms, from Best Buy to eBay, have either developed their own automated product sourcing systems or purchased software and process management solutions from ve ndors.BUILDING A BIG DATA SUPPLY CHAINThe benefits of paring Big Data with supply chain management make it an obvious choice; the ever-accelerating volume, velocity, and variety of data make it a necessary one. However, integrating Big Data into a firm’s supply chain is more involved than releasing a management directive or signing a purchase order.It is often advisable to start with individual links on the supply chain â€" such as departments, build Big Data into their operations, and replicate their successes across the organizations. The buy-in from this approach will help managers mitigate internal resistance to an innovation many find abstract or overwhelming. Executives and managers must review (and where needed update) the strategic business goals that drive the specific operational unit.For example, a corporate fleet might count as KPIs on-time deliveries, cost per delivery measured in fuel, wear and tear, and other measures, delivery times, positive customer feedback, lac k of negative customer feedback, and other similar indicators. Internal data scientist leads should work with must work with executives and managers (in this case the management team of the corporate fleet) to create operational goals and insights that drive these goals. For example, such insights might include the optimal time by which deliveries must be made to elicit positive customer feedback, optimal delivery routes that minimize cost per delivery and delivery times in real-time, and others that can allow the corporate fleet to add value to the organization as a whole. Data scientists then must work with I.T. (and vendors where necessary) to develop a Big Data infrastructure that allows them to meet these goals.Fundamentally, such architecture would include hardware/software and internal procedures and protocols for collecting, processing, and storing existing and new data, in real-time where possible and necessary. This architecture would also allow data scientists to clean, s earch, and filter data pre-analysis, analyze it as necessary, generate useful reports, and share actionable insights across the organization, and in some cases, to consumers. Further, this architecture must be scalable â€" as the volume of data will only grow, and secure, as a failure to maintain the privacy of consumer data can be a tremendously expensive mistake. Such architecture should communicate with existing (or new) customer relationship management systems and provide real-time intelligence to provide the most value for internal and external stakeholders.THE FUTURE OF BIG DATA AND SUPPLY CHAIN MANAGEMENTSeveral innovations and trends will not only accelerate the volume of data as a whole, but also the volume of data relevant to supply chain management. Mobile will continue to provide a major source of supply-chain relevant data, driven by the GPS technology in mobile devices, as well as the proliferation of social networks specializing in social discovery, which allows users to discover people and events of interest based on location. Deep analysis of consumer location information can afford firms even greater efficiency at getting products to consumers, whether through optimizing the locations of regional fulfillment centers or even distribution of products at those events and venues well frequented by its consumers.The Internet of Things â€" the attachment of sensors and other digital technologies to traditionally non-digital products to capture data, are currently, and will continue to be a major source of data of use to data scientists working on supply chain optimization. For example, a smart device can be built to send messages to the manufacturer when they are broken, which can generate production on a replacement part or full device, before its owner calls customer service. If the device is outmoded, its signal to the manufacturing firm can provide the customer service representative (and/or sales staff) with the information to prepare for an u psell.Cloud computing itself has driven Big Data’s growth significantly, as its inherent digitization of a firm’s operational data demands new methods to leverage it. As more firms take advantage of the benefits of cloud computing (such as reduced capital costs, economies of scale, and increased flexibility), adoption of Big Data’s management tools and techniques will grow. Moreover, as it grows, firms will demand increasingly sophisticated business intelligence systems, methods of predictive analysis, and tools for data mining, which the market will provide.

The Legal Treatment Of Interns - 1179 Words

Over the past five years there have been major developments in the legal treatment of interns which have driven significant changes in the way companies approach internship programs. In 2010, the Department of Labor issued Fact Sheet #71, which provided guidelines as to the application of the Fair Labor Standards Act to internships. Most notable was the requirement that a sponsor of an unpaid internship may derive â€Å"no immediate advantage from the activities of the intern† (United Stated Department of Labor, 2010). These standards were subsequently adopted by courts in litigation over unfair labor practices in internship programs. According to Lorenz Thomas (2015), there is widespread opinion that this means â€Å"an unpaid intern cannot†¦show more content†¦Internships can no longer be considered a laissez-faire practice. The practical effect is that there is little meaningful distinction in today’s environment between an intern who performs product ive work, and an employee. These external legal factors suggest that internships in the future may be, in part, a way of managing what Ivancevic, Konopaske, Matteson refer to as the â€Å"psychological contract† (Ivancevich, Konopaske, Matteson, 2014, p. 133). In other words, interns may be treated as employees in the current legal environment, but remain interns for the purposes of employer/employee expectations as to things like long term employment prospects and compensation. Employers can no longer view interns as a source of free labor. Employers must be prepared to commit more resources to their internship programs if they want to keep them, without being sued. At the same time, employers can use internship programs as a way of recruiting and training potential employees without the same level of commitment as full-time employees. Even so, the increased risk and resource requirements attached to internship programs are likely to lead to greater employer expectatio ns as to intern performance. Based on this analysis of the external trends in internship program practices, any internship program should be fully committed to complying with all worker protection and labor laws. Companies should clearly communicate their expectations to prospective

Organizational Structure Of The Organization - 3890 Words

Nowadays, with the rapid modernization of daily life and living standards increase quickly, people s needs for quality of products and services therefore also increase. For this reason, enterprises need to set up the structure and policies to suit the changes of the market. One of the essential and most important factors to achieve that is the organizational structure. Organizational structure theory is especially useful for people who manage organizations, or who aspire to do so in the future. It enables the manager to see that his or her organization and its problems are rarely wholly unique. Usually, much of value can be learned from examining the behavior of other organizations in broadly similar circumstances. Organizations,†¦show more content†¦Part 2: The important role of organizational structure in one business. Part 3: The case study â€Å"how to create an effective organizational structure† I. Organizational structure: 1. Definition: An organizational structure is defined as â€Å"the formal system of task and reporting relationships that controls, coordinates and motivates employees so that they can achieve an organization s goals†. It consists of activities such as task allocation, coordination and supervision, which are directed towards the achievement of organizational aims. 2. The classification of organizational structure a) Pyramidal structure: Pyramidal structure (also called hierarchical or line structure) is one of the simplest structures with one person or a group of people at the top and number of people below them. All the people in the organization know who their superior and immediate subordinates are. This kind of structure is suitable for small businesses where there are few subordinates or organizations where there is largely of routine nature and methods of operations are simple. Advantages Disadvantages Simple to establish and operate Promotes prompt decision making Easy to control Communication is fast and easy as there is only vertical flow of communication Lack of specialization Managers might get overloaded with too many things to do. Failure of one manager to take proper decisions might affect the whole

Rhetorical Analysis, Global Warming - the Great Delusion

Kevin Breuninger Prof. Jerry Phillips Prof. Harris Fairbanks English 3633W 23 February 2012 Rhetorical Analysis, â€Å" Global Warming – The Great Delusion† Matt Patterson argues in â€Å"Global Warming – The Great Delusion† that the alleged scientific consensus surrounding the theory of global warming is based not on fact, but rather on a web of mass hysteria and deceit. Patterson contends that â€Å"In fact, global warming is the most widespread mass hysteria in our species’ history†, and that the beliefs of global warming proponents are the result of their own delusional imaginations and a subconscious apocalyptic yearning toward which masses of people tend to subject themselves. While Patterson worries that what he perceives to be the†¦show more content†¦Patterson expresses a fear that â€Å"Man will be convinced by these climate cultists to turn his back on the very political, economic, and scientific institutions that made him so powerful, so wealthy, so healthy†. By framing his argument in a way that transiti ons from highlighting the scientific ignorance of global warming to the policies that such a worldview could impact, Patterson attempts to establish a chain of logic that justifies his concern for global warming as an influence on government. The language used in the sentence (â€Å"climate cultists† trying to convince â€Å"Man†, turning their back on beneficial institutions) also implies to the reader that the proponents of global warming are actively attempting to undermine the institutions that have allowed humankind to thrive in the modern world. This opinion is underlined later in the article, when Patterson contemplates why many â€Å"hope† for climate change catastrophe. At this point, Patterson approaches the core of his argument, wherein he provides what he believes to be sufficient evidence that the idea global warming will soon cease to be a threat to the progress. He argues that the â€Å"fever is breaking, as more and more scientists come forward to admit their doubts about the global warming paradigm†. The use of a fever as aShow MoreRelatedOrganisational Theory230255 Words   |  922 Pagescontribute to our understanding of organizations. Professor Tomas Mà ¼llern, Jà ¶nkà ¶ping International Business School, Sweden . McAuley, Duberley and Johnson’s Organizational Theory takes you on a joyful ride through the developments of one of the great enigmas of our time – How should we understand the organization? Jan Ole Similà ¤, Assistant Professor, Nord-Trà ¸ndelag University College, Norway I really enjoyed this new text and I am sure my students will enjoy it, too. It combines rigorous theoretical