Technological Integration in Supply Chains

Literature Review

In the Earth, it has been observed that there are more bits of data compared to the grains of sands present. However, the companies present are seen to be using less than 1% of total available data but still, it has led them to become leading data-driven companies. Thus, to focus on the aspect of technology the thesis has been framed in which the importance of technology in the modern era is to be discussed.


To assess the significance of employment for Advanced Technology in the supply chains

To resolve the gap experienced during the application of Big Data Analytics and Business Intelligence Research in Aldi

To implement the framework of Business Intelligence Research and Big Data Analytics in Aldi


The objective of the study is to assess the various processes which are used by Aldi before as well as after the establishment of advanced technologies. The paper is developed to present the different strategies to be required by Aldi for transforming its culture as well as the business environment into an improved data-driven business. In this context, the transition of the company from their traditional practices to modern practices is to be critically analysed to understand the way it can impact them to develop competitive advantage in the retail industry.

To assess the significance of employment for Advanced Technology in the supply chains

In business, the supply chain is regarded as the core part of the business which consists of purchasing, suppliers, production, manufacturing and distribution and lastly target consumers who are present at the end of the supply chain. The key fact about supply chain is that if the link in the chain is weak then it is prone to breakdown which results to provide hindered services to the consumers and this in turn negatively affects business progress as the customer’s satisfaction through a broken supply chain cannot be achieved.

The hiding of a broken supply chain is quite easy before twenty years ago. This is because in such condition the companies would be able to make up for the slower production by increasing the speed of shipping or promoting the acceptance rate of product failure to greater level and compensate it through warranty exchange. In general, the customers would move and do business at a slower pace in this condition. However, current technology has interfered in a dramatic way into the supply chain process and the process through which it interacts with the consumers. The consumers are seen to be connected digitally and they are seen to place queries for breakdown in the supply chain for which they want immediate answers as they do not have patience to wait. The consumers consider the company to have a personal portal where they have an updated tracking number of their product and shipping details along with dates within minimum 10 minutes after their order is placed. In this condition, the companies if mentions the consumers that the product is going to take 14 days to reach them then it is not a considerable time limit accepted by the consumers.

The presence of current technologies has made the supply chain to be more clarified and transparent in nature as well as improved links between the chains. For instance, the retailers are tied together with the technology in the chain. They are seen to have an advertising management system which is going to forecast the products to be placed in the ads on the basis of trends. They are also seen to have inventory software control to automatically monitor the different level of the inventory and make recommendation of purchase on the basis of demands projected. In addition, the presence of advanced technology has dramatically as well as rapidly improved the way supply chain is to be managed and has made information that is virtually demanded by the consumers to be delivered effectively (, 2019).

In current competitive market, there is less space for inefficiency to be considered in supply chain within the business. However, in this case, the current technology for the supply chain is able to simplify as well as optimise the process of business. This is required because it is assertive for the companies to innovate continuously and streamline their supply chain with the software technology to develop a flourishing business. The presence of an effective supply chain technology allows the organisations to develop better visibility in the market as well as gain better control over the inventory and be able to lower cost of products which helps to outpace the competition. The investment by the company in the supply chain is able to help them simplify their supply chain, avoid hindered link formation, improve efficiency and lower expenditure. The optimisation of the links present in the supply chain would also assist the company to lower the risk related with receiving and shipping of products. This, in turn, would help them to avoid consumers to turn away from them as the company would be able to provide goods and products to them within the estimated time with less shipping errors and other issues. The enhancement of the supply chain technology would help the companies to lower risk and improve their business reputation in the market (, 2019).

The decision-makers who have best understanding of data set are able to set proper standards for the business. The Big Data analytics assist the organisation lower cost, make better decision within limited time and develop innovative products for the consumers according to their needs. The data is considered as commodity which may not be considered valuable from the outside but insight into the data would bring better and useful information. The numerous advances which are powered by technologies such as predictive analytics and location intelligence are improving the process in which data within the supply chain is used (, 2019).

The Supply Chain Management is considered as a field in which Big Data and Business Intelligence are used which is driven by quantifiable and statistical performance indicators. However, the kind of analytics that are revolutionised in the current industry such as real-time analytics of great and increasingly growing as well as messy unstructured sets of data are seen to be absent largely in the market. In specific, it is seen that big supply chain analytics uses quantitative ways for improving the way of decision-making for activities executed around the supply chain. In this context, the big supply chain analytics executes two things out of which at first they use it to expand the sets of data for analysis beyond the traditional internal data which is present on the supply chain management (SCM) and Enterprise Resource Planning (ERP) systems. Secondly, they apply powerful statistical methods for both existing and new sources of data to create insight into the process of the supply chain. It is done so that it would assist them to develop proper decision from improvements to be made in front-line operation to strategic choices to be made like the selection of right models of the supply chain.

front-line operation to strategic

A. Inventory, Operation and Sales Planning

The planning in supply chain includes wide range of data inputs from the Enterprise Resource Planning (ERP) and SCM planning tools. At the present, there is significant way to redefine process of planning but the current internal and external data sources are used for shaping real-time increase in demand and supply. The retailers are able to use new sources of data for improving the process of planning and their demand-sensing capabilities. The production volumes, inventory data and visibility point of data are able to be analysed in real-time for identifying mismatches present between supply and demand. The information developed from the process is able to direct changes in product prices, timing and addition of new lines in the advertisement realignment of things and others to be made. After mastering big data forecasting, the next level is required to initiate active shaping of demand. For instance, the leading online marketers use big data analytics, inventory data as well as forecasting data for changing the recommended products for the consumers. This creates change in demand towards the items which are present in stock.

B. Sourcing

In many organizations, the information on procurement volumes as well as suppliers are collected for few activities to be performed in the sourcing process. However, the supply data is seen to go beyond the classic expenditure analysis and annual performance of supplier review. On transactional basis, the supply processes are able to be sensed in real-time for identifying deviations from normal delivery pattern. The mapping of supply chain by using Goggle Trend makes a firm able to monitor the way supply chain disruption in transportation can be faced and is able to take effective steps to improve it. The Clean Sheet Costing is the bottom-up calculations which are applied for purchasing transportation and warehousing. The exploitation of data on the cost breakdown of warehouse operation across the globe is able to help companies create a powerful fact base for challenging its Logistics Solution Providers (LSPs) and carriers by providing real insight into the “Should cost” to be considered during negotiations.

C. Manufacturing

The data present regarding manufacturing parameters like forces implemented to assemble operations or create dimensional different in parts are able to be achieved and analysed for supporting the root-cause examination of defects even if they are going to occur years later. The agricultural seed processors and manufacturers are able to analyse the quality of products with different kind of cameras in real condition to execute quality assessment for individual feed. The Internet of Things along with camera networks and sensors may be able to allow other manufacturers have opportunities in the future. The live information regarding the condition of a machine is able to trigger 3D-printed spare production which is then shipped through a drone to the plant for the engineer to be used who may implement the augmented reality glasses to develop guidance for replacing the part delivered.

D. Warehousing

The logistics are seen to be very cost-focused and it is seen that companies have holistically invested in technologies that could provide them competitive advantage in the market. The warehousing has seen various advances such as ERP data. One of the examples is chaotic product storage approaches which allow effective use of space in the warehouse, in turn, minimising travel distances. Another instance is high-rack bay warehouses which are able to automatically reshuffle pallets in the night for optimising the products as per scheduled for the next day. The companies are able to track performance pickers in different areas to optimise their future allocation of staff accordingly. The new technologies, analytic techniques and data sources are seen to create new opportunity in the warehouse. A leading Forklift provider is seen to be looking regarding the way fork truck is able to act as big hub which collects all nature of data in real-time and blend it with ERP and warehouse Management System adapts for identifying additional waste creation in the warehouse. For instance, the video images which are collected through automated guided vehicles and sensor inputs on analysing can be used for monitoring the accuracy of picking products, inventory accuracy and warehouse productivity in real condition. The forklift driving behaviour and choice of routes can also be assessed and they can be dynamically optimised for driving productivity of picking products. The data is able to be used for executing root-cause analysis of picking errors through weight, colour or shape for helping the process to be made more robust in nature.

E. Transportation

The companies are seen to have already used analytics for improving their operation. For instance, the companies implement fuel consumption analytics for improving efficiency of driving and the GPS technologies are used for reducing wait times by providing information about warehouse bays in real condition. The Big analytics is able to allow logistic providers to deliver parcels within few attempts of delivery by enabling them to find data when a consumer is likely to be at the place of receivable of the product. On a better strategic basis, the companies are able to cut prices and carbon emission by selection of proper transportation modes. A key CPG player is seen to be spending in analytics which would help them to understand when the goods are required to be shipped quickly or when there is time for slow delivery.

F. Point of Sale

The advanced and current analytics are able to help retailers make decision regarding which products are to be pit in value locations such as end of aisle and what duration they are to be kept in the place. It also allows them explore the benefit of sales achieved through clustering related products. The search engine Goggle has been seen to develop Skybox which is a nature of high-resolution satellite imagery to be used for tracking cars in the park to anticipate demand in-store. In other cases, it is seen that companies are using drones that are equipped with cameras for monitoring the on-shelf inventory levels. The topic which is still acting as challenge for retailers is the detection and prevention of out-stock products. In markets which are already developed, it is seen that manual inspection are expensive however RFID tags implement too much cost for it to be applied for individual products in the grocery. Thus, retailers are presently monitoring the sales activity for determining out-stocks indicators. In case an item which is sold each minute is not present in the shelf, alert is triggered to check for the item. Another technology which is being tested is installation of weight sensors on the shelves as well as on in-store cameras for monitoring the levels of on-self stock.

Summary: The bid data is seen to be already assisting leading companies transform their supply chain performance. The term "big data" is seen to have gained great popularity in the current years among IT professionals and others. It describes the massive amount of information to be processed and analysed through the use of technology for allowing the business to gain values which will help the companies achieve competitive edge in the market among the supply chain management. In current condition, these natures of approaches are seen to b exception rather than norms. The lack of capabilities and structured approaches to supply chain big data is seen to be holding many companies. The big data and advanced analytical tools for delivering greater benefits to companies need a more systematic approach to adoption.

To resolve the gap experienced during the application of Big Data Analytics and Business Intelligence Research in Aldi

The increased effect of e-commerce on traditional retailers is one of the notable instances of the revolution created through data-driven technologies which is sweeping many business functions and industry today. However, dew companies are seen to be able to apply proper degree of “big analytics” techniques which are able to transform ways supply management is defined and managed. The bid data is also defined on the basis of variety, volume, veracity and velocity. The volume mentions the amount of data that is able to be effected through the scope and it is the primary attribute. The variety is referred to the data that includes analysis which is retrieved from different sources present in different mediums. The velocity refers to the frequency through which different data is generated and delivered. The veracity is referred to as the uncertainty of data (McAfee, 2012).

There is no presence of globally accepted definition regarding “big data” but Vs concept which is introduced by Gartner analyst Doug Laney in 2001 is seen to develop a common structure to describe it. At first 3 Vs were seen to be used and later other 3Vs were added. The 6Vs is seen to presently characterize big data which are volume that is amount of data, velocity that is generated and processing of data quickly, variety that is processed structured and unstructured data types, value that is aiming generation of significant value for companies, veracity that reliability of processed data and variability that is flexibility of adopting formats of data through gathering, storing as well as processing (McAfee, 2012).

The impact of the big data in the supply chain is mainly restrained by two key challenges out of which the first one of lack of proper capabilities. The supply chain managers even though have increased degree of technical skill are seen to have less or no experience regarding the data analytics techniques used by data scientists. Thus, it often results to create lack of vision to see regarding what is possible with implementation of big data analytics. The second challenge being faced is that most of the organizations do not have structured system for exploring, evaluating and capturing opportunities of big data in the supply chain. The other essential issue is the slow pace with which the developed companies are shifting their business to data-driven culture. Nearly 99% of the virtual responders mentions that the firms are trying to direct their way in another position to be a data-driven company but it is seen that only 1/3rd of the firms have achieved in this objective. This presence of gap is seen in many surveys and the success level is seen not have improved. It is also seen that companies require more concentrated programs for achieving data-related cultural change.

Many recent startups are seen to be data-driven from the beginning of their business and this is the reason behind the large firms fear disruption from the newly established start-up. One of the approaches that are taken by the companies to establish them and deal with data-driven disruption is to make the establishment of new and improved management roles. However, it is seen that there is quite lack of clarity regarding what extent the data-oriented roles are differentially related to one another. This is because big data include increased amount of complex data and it is quite difficult for the companies to sort out the data by maintaining privacy at all levels along with application of all nature of security controls (Ularu et al. 2014). It is mainly experienced when data is gathered from many sources and from various companies which are located in different nations having different sets of laws to be followed.

Risk experienced to implement Big Data Solutions

The implementation of Big Data Solutions creates one of the key risks which is privacy concern (41). This is evident as the collected data can be traced back to the individual from whom it is collected in various form without letting the person to be known through location data, internet hacking video recording and others (42). The further concern which is generated in this aspect is extent to which the data are able to be integrated and analysed for gaining insight into individuals. In this aspect, in 2012 the Google revised their privacy policy that allowed integrate user data to be needed from various services for building complete profile of users. The authorities for data protection are seen to investigate the change and concluded that the policy developed does not show compliance with the EU legal framework and therefore accordingly provided them recommendations for changing (44). However, it is seen that Google has failed to meet mentioned recommendations and faced fine by the French Data Protection Authority (44).

The credibility of data is another key issue to be considered when big data solutions are to be implemented. The poor quality of data and the presence of noise in the data are found to be most common issues for all nature of information collected from different companies. The price for validation of data is huge and studies mention that it may take 50% of the employee’s time for validating and correcting data (46). In the retail industry in the UK, the data regarding product information are inconsistent in more than 80% instances within the supply chain channel which is significantly affecting coordination, operations and profits (47). The insight developed from the data can be as good as the data on which they are based and therefore the use of the statistical method is to be done for measuring, monitoring and controlling data quality as well as credibility (49). The other risk to apply big data analysis is the lack of analytical talent for executing the analysis. The shortage of effective talent for analytics is able to reach 190,000 by 2018 in the US (1). In this aspect, training and education are important for addressing the issue and the analytical culture present in the organisation is found to have a considerable impact on retaining talent for long-term (50).

The most fundamental barrier to be faced while using big analytics in retail operation is the managerial culture (51). The implementation of technologies solely cannot guarantee better productivity but changes are required in decision-making management and culture practices for successfully implementing the big data analytics (52). The academic research required to be carried out on the topic is going to grow and many articles which are providing description of real-life practices are seen to be written from the market perspectives that are focusing on micro-segmentation of consumers for developing better and effective activities of marketing. However, assortment planning, store layout design and pricing are found to be mentioned in the reviewed literature as areas which are able to provide better quantitative analysis. The availability is seen to have been raised as a main issue of retail operations in the reviewed literature which have been affected through online shopping and is able to be improved with the implementation of big data. The other companies which have wide industry presence such as Amazon, Uber and others are seen to have shown reliance on new technologies such as the internet, smartphones and others as the disrupting force. Aldi is found to still have lower technology except for its loyalty program and it is found that they know little about the individual preferences of their customers who are buying online from them (, 2019).

Barriers for implementing Enterprise System in Aldi

Cost and Financial barriers: financial barriers are seen to act as the main barrier. The implementation of enterprise system to millions and tens of millions mainly for the small and medium companies and creating value of their products consider it to be a key information platform. The enterprise system in the short–term apart from assisting to cultivate and help staffs the concept is seen to provide negative returns. In long-term aspect, it is seen that the enterprise system acts for cost reduction and efficiency gaining but it also fundamentally seen to cooperate which would develop fundamental changes in the structure present in the self-optimisation path. Another importance of implementing it is the initial setup and preliminary expenses time and the cost through with adaptation, adjustments and training. In case of business which receives PO per year from the client, the fully integrated MPR, CRM and EDI are unable to act as economies of scale. In this aspect, the Aldi is required to use inexpensive solutions. In case of other business, the use of integrated enterprise system solution may be necessary as rise in volumes of trade that is brought by the EDI force them to again implement their processing of order in the business.

Tools: Many enterprise systems are seen to have failed because tools that are required were not available to the end-users so the enterprise system capabilities were unable to be implemented. The users were seen to regularly use DBMS system for pulling data from different warehouse and import them to the excel through the system’s analytic capabilities which to them seemed to be overly complex and not useful. Despite this fact, we can see the complexity of systems and all about the related to financial matter to buy hardware and developing complex programming (Java, Delphi, Pearl and C ++) and end with the re-engineering of ES if the current system not useful for management and users.

People: The people who have industry skills, information and objective for succeeding are in some instance found to be unenthusiastic for embracing new technology. These people may be internal or external and while applying enterprise system a company requires having value and need to estimate the educational and cultural level with the present location. There are different factors to be considered:

Comfort and security of the employees with the expertise of present factors

Leadership Issue: The senior management is required to have a hard look for finding a business-minded leader who is technology savvy. Management is needed to select the person carefully to accomplish tasks for getting productive and effective leaders who are able to increase use of the system.

Inadequate data is mentioned regarding technology which is being adopted as well as deployed.

Lack of empowerment and training between staffs and management

The lack of proper hiring of human resource is regarded as one of the barriers of implementing information system (IS) because the proper human resources act as the one who provides specification, informs requirements and offer standard assistance for developing improved functionalities in any business enterprise. The communication is seen to act as the importance of implementing technology as every employee and managers within the company require working together as a team for achieving the determined goal.

Unskilled staff: The system is required to be prolonged and supervised regularly and the staffs who would be involved in handling the operation required to be effectively trained and should have the capability of managing the system. The unskilled staffs within the company would act as barrier as they would fail to realise the way to make the system work.

Technical Problems:

Information Technology Infrastructure: The lack of sharing, reliable computing and network infrastructure for addressing the required teamwork for application of IT in the company act as barriers. The organisation when lacks a proper IT infrastructure it is seen that complexity rises in operating a technology-supported program that is consistently managed within the enterprise. This condition is seen to slow down and complicate communication between the organisation and others.

Unworkable Time Frame: most of the information system is seen to take loner time in operating compared to what has been planned. The delay issue regarding timing and attempt for serious technical glitches during planning creates hindrances for regulating operations.

Difficulty in Data management and collection: The Data collection is regarded as the most crucial as well as tricky part for application of information system to establish an entire systematic process. Thus, it informs about the system as well as its capacity and limitations to be experienced by the user.

To implement framework of Business Intelligence Research and Big Data Analytics in Aldi

The observation informs that Big Data when predominantly present in the contemporary environment of business then it informs that wide amount of information may be present from which effective intelligence is able to be gleaned. It is seen that irrespective of any nature of business the petabytes of raw as well as processed data is present in different formats to be consumed and analysed. On the basis of large amount of available data, the study analyses the reason behind the Big Data projected have failed. There are different varieties of answers being provided by the individuals who are in the data science, information technology and business fields because it has been referred above. The presence of appropriate solution of business intelligence for Aldi is effective for them to determine the pitfalls, trends as well as opportunities present early enough. However, the adoption of effective solution is not seen to be easy. The software that is to be chosen requires some initial heavy lifting for increasing its potential. The presence of proper mindset would allow making the staffs be prepared for addressing issues such as resistance to change management, complicated data problems, IT reluctance, warning sponsorship and user adoption challenges. The initial implementation framework is informed that may be implemented in Aldi to adopt successful data-driven culture.

1. Establishing a Business Intelligence Big Data Vision

The Aldi has been seen to have its own vision but there required to be another vision in the company for the Big Data and Business Intelligence System. The company is required to determine their role to be played in business intelligence and need to have technology-based vision to ensure their business strategies are effectively met. In addition, Aldi requires identifying their key drivers of business along with the vision that directs the data subject sections and operates the business unit. It is essential to make sure business initiatives are properly taken as they would guide the knowledge assets and direct the system needed. In addition, the business intelligence strategies required to be embedded in business operations.

2. Developing a long-term roadmap for prioritising initiatives

The Aldi requires prioritising their strategic value as well as simplicity for execution through cost savings from the data by centralisation and mart consolidation. A guideline for the implementation of business intelligence as well as big data required to be developed along with maximum cost required is to be presented which could be funded through centralised benefits and business generations. The mentioned roadmap to be followed by Aldi requires ensuring that the initiatives for business are made priority for helping them to meet the vision of the business. The business of Aldi needs to have the will of deciding the essential resources such as staffs, costs, IT and others. This is because business intelligence does not come out from the IT budget. The business intelligence strategy which is best informs resources to be required at the initial level.

3. Governance and Funding process

The presence of effective governance is needed for easy functionality. The business intelligence technology requires having the presence of established governance set up along with governance executives, team and boards. There is urgency for an established structure of support and communities of business intelligence which would lead to its success. The presence of good governance is mainly based on availability of adequate funds for supporting it. Thus, to launch the project in a proper manner the owners of business require securing finances from the sponsors. After the initiation, Aldi requires to sustain proper finances are made available for Big Data Analytics and Business Intelligence in order to maintain them properly. The launch is to be sponsored with the help of an executive who has responsibility at the bottom-line as they have the ability to present a wider picture of the company’s strategy as well as goals and have knowledge regarding the way to translate mission of the company to focussed KPI mission.

4. Established Business Intelligence Competency Centre

The observation made informs that the company which utilise technologies for business intelligence has huge data warehouse and therefore a skilled data analyst is required to manage it. Therefore, the Business Intelligence Competency Centre is able to be characterised with the presence of skilled and specialists staffs as well as resources which can be shared as well distributed among all business units. Aldi requires managers in the data warehouses who have expertise in modelling of data and information regarding the way to operate specialized analysis technique. The BICC is able to play main role in the creation of awareness initiative. The data sources, data warehouses and the software drawing out insights are not to be neglected as it would lead the company to use them as central tool for navigating the data and bring out insightful analytics.

5. Empowering team to be data-driven

The employees in Aldi required having basic information regarding reporting skills. In addition, the team would require having concern regarding adoption of business intelligence and the employees require understanding regarding what they mean. The managers at Aldi are required to listen to their concerns and then require explaining in details the way the employees would be benefited from their specific roles. The employees require to be on board with the manager and if they are found to be enthusiastic it can be determined that they have understood the benefits. The current development in analytics mentions that users who are implementing business analytics are required to be trained as well as properly skilled. This is going to ensure that the company is properly equipped for supporting the value-driven decision. Aldi requires developing a data-driven approach so that it helps them make intelligent as well as smart, impactful decisions.

6. Clean the Data

Aldi has developed a plan for storing data and therefore it is vital that they clean their data storage areas. The cleaning of data is regarded as equal to cleaning Aldi’s analytics. It is significant that solid data quality management is guaranteed as it is going to help the company maintain cleaned data which is possible for improved operational activities and decision-making is done by relying on the available data. It is mentioned that each year presence of low-quality data cost the American business over $9.7 million and it impacts productivity, bottom-line and overall ROI. However, the company should not become obsessed with 100% pure quality data because the key purpose is not to frame subjective notions of what improved data quality is or not. The main goal is to improve the ROI of the department which is found to be relying on the data. The reduction in the presence of useless reports and identifying new opportunities in the data is going to benefit the entire company. The decision-making process is required to make changes, as well as changes, are required in the structure of the organisation.

7. Identifying Key Performance Indicators

The key performance indicators (KPI) are found to be measurable values which inform to what extent the company is being able to achieve their objectives of business. The KPI is found to be present at the core of a good business intelligence strategy. The KPIs informs the areas in business which is on right track and in which areas further improvement is required. During the implementation of effective business intelligence strategy, the company need to consider their individual strategy as well as align their KPIs related with the objectives of the company. Thus, in case Aldi is indented to initiate with most significant KPI then they have to create standards as well as governance with the presence of KPIs within their minds.

8. Choosing proper BI software

During the process, the company would require to choose as well as perform comparison between cloud and on-premise. This is essential to ensure they choose a solution which can be small as well as easily scaled for allowing Aldi to grow. There is presence of plenty of flexible solutions which addresses needs, as well as free trials, are currently available online for any nature of use.

9. Keeping the process agile

The proper implementation and dissemination of business intelligence software are regarded to be another challenge when new discoveries are intended to be made and hindrances arise after the implementation is on the way. The presence of an agile mindset is regarded as early deliverables within a multi-phased approach of delivery which often acts as ideal for the success of the project. The early feedback of the solution of business intelligence at hand delivers confidence to the business to receive quicker feedback and correction of course of action while the solution is still in the process of being developed. The development of foresight on the issues or having opportunities to use business intelligence data and tools are able to help in preventing the company from being blind-sighted while on the road.

The success of Business Intelligence project and its performance metrics are to be analysed in continuous manner. While a Business Intelligence project is underway it must be ensured that permission is granted for disseminating information to various individuals across the organisation. It is also required that The tailoring data visualisation on the context of roles and experiments that provides new information delivery tools which are suiting the needs of the organisation are to be considered.

10. Monitor as well as Evaluate

The constant monitoring as well as evaluation of the business intelligence software is to be done to ensure the business is using it to their full capacity. The KPIs is required to be identified and the measurable values are to be considered to identify to what extent Aldi is achieving their business objective and is able to make improvements. In addition, it is required to keep reports for showing the positive effect of business intelligence and BDA on Aldi as it is going to help in encouraging their improved adoption. It is been estimated that in Aldi the team leaders are seen to be spending 5 hours in a week on Excel by using a manual system which counts employee working hours. The partnership of Aldi with JDA which is brought by the improved growth of the company in the UK market indicates that the company has developed a shift in thinking. In case JDA marks their first foray in the non-standard Aldi software, then a project is required to b developed for framing a new payroll system with the help of NGA known as Resource Link and it would be indicated as the second mover in the direction.


A platform for Business Intelligence is required to be established in a simple maner to be data-driven. It is required to serve the purpose of improving efficiency, lower cost and improve profitability of the company. The development of a successful business intelligence along with its effective operation in the organization is seen to provide the company great benefits. Since there no presence of “one-size-fits-all” business intelligence strategy, thus the guidelines allow a level of personalization and adaptability is to be built which can be adapted for fitting needs of management and team at Aldi. The goal of the study is to include best practices in the form of a model which would effectively provide guidance regarding the way business intelligence strategy is to be developed.

Research Methodology

The research methodology indicates the different sequential steps required to be adopted in the study to examine the problem along with certain determined objectives. It is regarded as an explanation of systematic method and presence of critical examination regarding an identified topic in the research. The concept of Research Onion is presenting explanation regarding the elements used in the study (Saunders, Lewis, & Thomhil, 2007). The researcher is going to provide description regarding the steps to be adopted in the study. In brief, the research methodology includes explanation, description and justification of different methods used in executing the study (Sharavanavel, 2006).

The concept regarding research onion will be implemented for developing knowledge regarding research process. The key layers of the research onion include research philosophy, research approach, research design and others. The explanation of the elements will provide the individual involved in executing the study have knowledge regarding the way to execute this research (Saunders, Lewis, & Thomhil, 2007). The research onion model which is presented by Saunders et al (2012) is seen to have a key influence on presenting the methodology regarding the work. The research onion model informs the different elements that are included in the study so that a proper design of the research can be framed.

Research Philosophy

The initial condition of any study is to develop a research question according to a particular research philosophy. According to Hudson and Ozanne (1998), ontology is referred as function of reality. In the study of Carson et al. (2001), it is mentioned that epistemology is the interrelationship presence between the researcher and reality. The philosophical perspective of the study is mainly mentioned in the exterior layer of the research onion. As mentioned by Saunders et al (2012), there are four research philosophies which are realism, interpretivism, positivism and post-positivism. In the present study, interpretivism is chosen.

The study by Hudson and Ozanne (1998) mentions that interpretivism in terms of ontology and epistemology as the reality which is numerous as well as proportionate. Moreover, it is mentioned that different realities are depending on a various structure related to connotations. According to Carson et al. (2001) the knowledge obtained through interpretivism is forged socially and not gathered impartially. In condition, when interpretivism is selected as philosophy it helps the study to collect information on recommended as well as existing promotional activities being followed by hypersensitive brands and the way the elements impacts the decision-making by researchers. Thus, the interpretivism philosophy has the key aim to understand responses made by respondents through experience. In addition, the use of the philosophy is able to allow identification regarding the way brand position for the anti-hypertensive is able to facilitate customized strategies of promotion for optimization of the prescribed anti-hypersensitive brands. The researcher when have the focus to gather subjective meanings and develop rich insights instead of developing law-like generalization they are going to use interpretive philosophy. In addition, the philosophy is related with identifying narrative for a social phenomenon present in the natural environment. Therefore, the philosophy instead of focusing on objects is focusing on executing research among the people with a stance to consider and determine the view of the world.

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Research Approach

The study by Babbie (2010) informs that research approach mentions the research foundation strategy and offer direction for research methods. The research approach is considered as examination of different methods that are used in scientific inquiry application and assists to identify interaction between logic and observation. The research approach is able to differentiate between two parts that is inductive and deductive.

In inductive approach, the non-statistical data is used so that the findings of the numeric analysis are able to be sustained. The inductive approach involved building theory wherein collection of data is done initially through different means such as interview and then new theories are developed on the basis of findings (Bryman, 2012). The use of interview facilitates gathering of real information which are necessary for framing new theories. It allows the researcher to identify different things which are happening in the world that are related to the research topic (Bryman and Bell, 2015).

Research Methods

The qualitative research is seen to be confirmatory. As mentioned by Olds, qualitative research are used for gathering and testing textual data like survey, conversational analysis, focus group and others. As mentioned by Crestwell (53), qualitative research is implemented to examine any issue which is related with the work of interviewees by gathering attitudes, views and perceptions. According to DiCicco‐ Bloom and Crabtreem (54), the contribution of qualitative method is that it holds the basis that life experiences are shared through interviewees. Thus, the qualitative research method is to be used in the research for gathering explanation from interviewees that are based on the experiences regarding the way to influence transformational leadership for improving commitment of organization and increase work performance of employees.

Research Strategies

The research strategies mention the way a researcher plans for gathering data. The data collection methods may include survey, case study, experimentation and others. In this study, the case study research strategy is to be used because it provides various examples from real individuals.

Research methods

The primary data to be gathered in this study is to be done through an interview that is to be held with experts present in the retail industry who are operating big data applications. After consideration of the study nature, in which the key aim is to explore possibilities with the focus on previously identified areas for big data in retail operation from reviewed literatures the semi-structured interview is chosen as the method for collecting primary data. This is because it would allow the researcher to provide open-ended questionnaires to participants allowing collecting in-depth responses. The secondary data in the study is going to consist of non-academic literature, academic literature and websites of companies because the field is not been established and is still found to be growing.

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