Enterprise Management in the Coca-Cola - Case Study Example

Enterprise Management: The Case of Coca-Cola Student’s Name Institutional Affiliation Date Introduction Intelligent automation also known as robotic process automation entails the combination of automation and artificial intelligence. The automation has changed the way that modern businesses conduct their operations. The effective functioning of intelligent automation systems depend on their ability to collect and process large amounts of information thereby enabling the automation of the workflows or processes within the organisation. With the help of artificial intelligence (AI), such systems are capable of adapting to the changing workplace environment. This adaptation enables the firm to offer goods or services that meet the changing needs of the customer. AI applications vary from revolutionary to routine. AI has enabled the automation of processes that include data collection, analysis, decision-making, and guiding advanced robots and autonomous vehicles. Just like other projects, there are several opportunities and challenges associated with the use of AI. Enhanced operational efficiency suffices to be the major opportunity presented by AI. However, the fear that using AI applications such as advanced robots may have an adverse impact on the society and the economy is the main challenge that firms have to address prior to implementing AI applications into their operations (Bunker, 2016; Masayuki, 2016). Coca cola is one of the companies that have implemented AI into its operations. The first section of the paper covers the potential challenges and opportunities presented by AI to the Coca-cola Company. The second section addresses potential issues that the firm will encounter in the implementation of the AI project. The Coca cola Company Opportunities Presented by Artificial Intelligence Currently, the Coca- Cola Company has endeavoured to help its retailers manage the stock of their products. The company has created a program that enables retailers to sell more of its products as well as encourage customers to purchase it products (Coca- cola, 2016). Currently, all retailers have scanners that collect substantial amount of information on customer purchases such as purchase made by frequent shoppers, time, price, number of times, and universal product codes. From the situation, it is evident that retailers already analyse customer data. However, it is apparent that the capabilities of the scanner in analysing customer data differ. The company has decided to offer free services to its retailers that are already using scanners to collect customer data. The programme enables retailers to transfer significant amounts of information to the database of the company’s headquarters in Atlanta. However, the transferred data does not include customer identifiers. Upon the receipt of the information, the software categorises the collected information on the basis of the average basket size, generated profit, day, shopping frequency, time, and total expenditure, and brand and category choices. The SAP BI Implementation in Coca-Cola Numerous issues arising from the functionality of the legacy systems necessitated the implementation of SAP BI in Coca-Cola. The company had to use AI to realise a smooth expansion into other markets, implement a multilevel compensation structure, and communicate with its bottlers and distributors. The objectives of the SAP BI included monitoring the sales compensation structure and service levels, revise the compensation and ordering process, implement business suite and attain a proper integration with the already existing structures. The chart below shows the components of SAP BI. Figure 1: Components of SAP BI During its implementation, the SAP SEM Business Consolidated and Business Planning was responsible for solving problems associated with financial information. The SEM Business Planning Simulation was responsible for the collection and analysis of business information. The SAP BI system also entails the SAP BI Data Warehousing that analyzes financial records to aid in the decision-making process. Finally, the SAP Co extracts financial records on a daily basis to enable the generation of financial reports and the forecasting of future demand (Slideshare, 2016). The figure below shows the system architecture for the SAP BI. Figure 2: SAP BI System Architecture Gerland’s Food Fair is one of the company’s retailers that have benefitted from the SAP BI system. Upon submitting its customer sales data to the company’s database, the retailer had decided to stock its shelves with convenience items and snack. The decision of the company to stock convenience item and snacks emanated from the need to satisfy the rising demand for the products from occasional customers. However, Coca- cola advised the firm on the need to serve the needs of its core customer. As a result, it recommended that the retailer should stock soup, pasta, and tissues as demanded by its core customers. Upon heeding to Coca-Cola’s advice, Gerland’s recorded an increase in it sales revenue over the period due to the consequent increase in core shoppers. As a result, Gerland’s recorded an increase in the revenue generated from sales thus indicating that firm growth is one of the opportunities presented by the intelligent system. The Business Model The implementation of the SAP BI system emanated from the need of having a single environment for business planning, consolidation, and data warehousing for the global executives of the company. Integration of financial planning and consolidation is the first element of the SAP business model implemented by Coca-Cola. The enabling capabilities for the artificial intelligence system include Business Planning and Simulation (SEM-BPS) and SAP SEM Business Consolidation (SEM-BPS). The SEM-BCS functionality accelerates corporate accounting procedures by automating the management and legal consolidation processes. The results are consolidated financial statements that include balance sheets, cash flow statements, appendices, and income statements. Having obtained information from mySAP Financials, SEM-BPS automates planning functions used in modelling, target setting, scenario planning, risk evaluation, resource allocation, forecasting, integrated budgeting, and simulation. Finally, the Business Intelligence provides the data warehousing solution that reports the entire process of SAP implementation (SAP AG, 2001). The implementation of AI applications will also increase the productivity of the firm by increasing it output. Factories that are using advanced robots to perform manufacturing activities have noted an increase in their productivity rates (Ayres & Miller, 1981). It is alleged that the use of robots in the manufacturing sector will reduce labour costs by approximately 16% (Associated Press, 2015). This implies a reduction in the total expenses incurred by firms. As a result, firms will realise increased profits. In essence, the main opportunities presented by the use of robots in factories and other firms are increased efficiency and reduced costs of production. Increased output from associated with the use of robots emanates from the fact that programming robots to perform a particular task requires little time as compared to retraining humans to carry out the same role. Moreover, robot pricing has exhibited a downward trend over the years implying that firms will incur less acquisition expenses to acquire the machines. Challenges Presented by the use of AI Coca cola, just like any other firm that intends to automate its operations by using robots and other AI applications encounters several challenges. One of the major concerns is the potential adverse impact that robots have on the social and economic sectors. In essence, Coca-cola has to deal with the distributional concerns that accompany the use of robots. Even though the use of AI enhances economic output by improving productivity, their possible effect of replacing human workers poses a challenge to Coca-cola among other firms that intend to implement AI into their operations. Firms that intend to integrate AI into their operations purport that robots play a transformative rather than a replacement role with regards to workplace operations. However, it is evident that the continued introduction of robot at the workplace may replace human workers permanently thereby rendering them jobless. This leads to the threat of change resistance since most of the employees working in areas that can be automated face the risk of losing their places at the organisation (West, 2015). The decision of Coca cola to use robots in some of the sections of the manufacturing sector may result in additional capital investments in the future. Such investments present the risk of supplanting the existing human labour instead of supplementing (Burkeman, 2015). The implication is that Coca-cola will eventually replace most of its workers with robots. However, Coca cola argues that the increased use of robots indicates an increase in capital investment. Apparently, capital complements labour (Giroud & Mueller, 2015). As a result, an increase in capital investment raises the revenues accruing to labour. In essence, developing a harmonious relationship between AI application and human workers will present the greatest challenge to the company in its endeavour to automate its operations. For instance, the decision of Coca-cola to use algorithms in analysing historical quality-control or real-time data, identifying issues of quality and their possible causes and determining effective ways of minimising wastes at the company may pose a threat to employees that work under the quality control department of the firm. The implementation of such a change may encounter significant resistance from workers that consider their job positions to be under threat. It is apparent that Coca-cola’s decision to implement such an algorithm will reduce the number of workers that have specialised in quality control. On the other hand, the firm will require industrial data analysts to use the information provided by the quality control algorithms. Therefore, a probable solution would entail transforming the roles of employees in the quality control sector and training them on new skills concerning the analysis of industrial data (Struijk, 2011). By so doing, the threat of losing jobs to AI applications becomes a challenge for individuals that do not intend to undergo role transformation. Robot-assisted production is the other area that Coca-cola can invest to enhance production levels. Being a firm that makes use of bottles and other items that require manufacturing, AI provides robots that are capable of handling the production of these items at a faster rate. Moreover, such robots require less time to programme them to perform other tasks. Such robots also have cameras and safety sensors that enable them to interact effectively with the environment and respond to its changing needs (Lorenz et al., 2015). The decision of Coca -cola to use such robots in the production sector will reduce the amount of manual labour required in the sector thereby eliminating majority of manual labourers. Even though monitoring the performance of robots is a responsibility of a robot coordinator, it is evident that the number of coordinators required by the company to supervise the robots will not be equivalent to the number of manual employees that the firm had employed in the production sector. However, increased capital investment in AI implies increased productivity and increased revenues generated by the firm. As a result, Coca cola can transform the roles of manual labourers to other roles that have not been automated by the company. AI has also seen the introduction of self-driving logistics vehicles within the logistics section of the company (Lorenz et al., 2015). The vehicles are automated transportation systems that navigate independently and intelligently within the factory. The massive deployment of such vehicles implies that the firm will have to reduce the number of its logistics personnel. On the other hand, the firm will need the services of a logistics vehicle coordinator that would manage the operations of the vehicles. Moreover, Coca-cola will create additional room for vehicle repair and maintenance services. Dealing with the loss of jobs in the sector would require transforming the roles of logistics personnel from transporting beverages on the factory floor to servicing the vehicles and coordinating the operations of the vehicles. Rather than threatening existing job opportunities in the firm, AI can also create production line simulations through the use of innovative software to simulate lines of production before installation and apply the resultant insights in the optimisation of operations within the production line. Apparently, the AI application will increase the need for simulation experts and industrial engineers. The firm can train some employees to become simulation experts thereby retaining them rather than being compelled to retrench them. In essence, the main challenge posed by the implementation of AI in the firm is the probable replacement of human employees with AI machines. Change Leadership As opposed to complexities that necessitate management, change requires leadership (Blanchard, 2010). The implementation of automated systems is a change process that requires proper leadership skills on the part of the management of the Coca- cola Company. Therefore, the implementation of AI in the operations of the company requires the management authorities to exhibit effective leadership skills. The use of a project leader is crucial to the successful implementation of the AI project. The leader should set the vision for the company towards the use of AI (Brecken, 2004). For instance, the leader should heighten the significance of AI to the future success of the company. There exists a clear distinction between setting the vision and long-term planning since the former is a leadership role whereas the latter is a management role. Therefore, implementing the AI change at the company is an inductive process that requires gathering substantial amounts of data and identifies relationships, linkages, and patterns that explain the various aspects of artificial intelligence applications (Blanchard, 2010). In the implementation of quality control AI applications, the leader should sensitise all stakeholders about the functioning of the algorithm. The algorithm is capable of handling historical or real-time quality-control data. The application is also capable of identifying issues associated with quality including their causes. Moreover, the leader should explain the ability of the system to identify ways of reducing wastes. Focusing on the merits of the project to the overall success of the company in the competitive beverage industry is imperative for the leader. This also helps to convince other employees about the essence of the project towards enhancing productivity. As mentioned before, the challenge arises from the potential of the AI system to declare some roles redundant thereby eliminating the need for some workers in the department of quality control. At this juncture, the leader should reaffirm workers under the department that the system has only transformed their roles instead of replacing their roles as initially feared. In essence, aligning people should be the objective of the executives of the firm in the endeavour to implement AI applications into its operations (Tosti & Jackson, 2003). It is evident that change management does not require executes to organise workers. Instead, they should align workers towards accepting the change. Coca-cola executives should understand that interdependence is a central feature exhibited in most organisations (Tjosvold, 1989). Therefore, employees at Coca cola also portray interdependence. In modern organisations, employees do not have complete autonomy. Instead, the hierarchy, management systems, technology, and work tie these employees together in the workplace environment. The connections make it difficult for organisations to implement changes successfully (Townsend, 2007). This provides the need for organisational realignment that target to convince employees about the need for the strategic change (Andolsen, 2007). Aligning employees presents a communications challenge o executives. Since aligning requires communicating to as many workers as possible, Coca-cola executives should communicate to workers of the affected departments with regard to the implementation of the AI systems. Communicating the change extends beyond subordinates to include other stakeholders of the change process (Wanyama, 2013). On the part of Coca-cola, change communication should target subordinates, bosses, staffs, peers, customers, government officials and suppliers. The rationale behind communication is the need to include stakeholders whose contribution may result in the success or failure of the project (Husain, 2013). Apparently, AI systems represent an alternative future. As a result, executives tend to find it difficult to convince staffs about an alternative future characterised by the transformation of human roles and the automation of traditional roles by AI systems. Besides aligning individuals in readiness for the change, leaders should also have motivational and inspirational attributes. In the case of Coca-cola, it is apparent that motivating workers will energise them through the satisfaction of basic human needs as opposed to pushing them to the desired direction. The major concern for Coca employees is the threat of losing jobs to AI systems. The threat in itself is enough to de-energise thee workers and invoke change resistance (Kilpimaa, 2006). However, providing assurance that their positions will be in place including a further salary increase energises workers and motivates them to accept the change. The ability of Coca-cola’s executives to motivate workers yields a sense of self-esteem, recognition, belonging, and life control. Motivating employees at the firm will also enable them to live up to the ideals of other individuals (Wright et al., 2001). There are several ways through which executives at the firm can motivate their employees. One of the techniques is the articulation of the vision in a way that prioritises the needs of workers over the organisational needs while understanding that the change will also impact positively on organisational growth (Kotter, 2001). By so doing, employees at Coca would develop the sense of feeling that their work is important to the organisation. As a result, they will feel confident and secure about their job positions within the organisation (Ganta, 2014; Omar et al., 2010). Executives can also motivate workers by involving them in prior decision-making processes associated with the change. This invokes the sense of belonging among these individuals thereby encouraging them to support the change. The reality is that AI transforms human roles rather than replacing them (Uzialko, 2016). Therefore, leaders should motivate workers by training and coaching them on their new roles at the workplace (Jehanzeb & Bashir, 2013). For instance, it is certain that Coca-cola has employed a lot of workers in firms that produce plastics as well as in the logistics section. Therefore, executives of the firm should communicate to the employees that carry out these roles that the firm intends to use self-driving logistics vehicles and robot-assisted production to carry out their roles. However, instead of handling the heavy work witnessed in the sections (Nilsson, 1984), workers will perform lighter roles with better compensation plans. Such roles include robot coordinators, the maintenance and servicing of self-driving logistics vehicles, simulation experts and industrial engineers. In essence, the executives of the company should focus on positive leadership in the entire AI implementation process. They should also realise that the change process follows a sequence that involves dealing with the current issues at hand, then addressing future issues and finally determining the interconnections (Brown & Eisenhardt, 1997). The executives should emphasise the fact that adopting AI technologies would enhance production and company growth as compared to the use of conventional human labour (Florin & Atanasiu, 2008). The executives should also emphasise affirmative bias that includes the capabilities and strengths of the AI systems as well as well as the significance of the already existing human labour in the growth of the company (March, 1981). Finally, the executives should also emphasise on virtuousness in the organisation and individuals. This entails communicating to employees that AI systems are good for the modern business. As a result, the success of any organisation depends on systems for enhanced efficiency and productivity. Conclusion The use of artificial intelligence (AI) in modern firms has turned out to be a necessity rather than an alternative to the rapid growth of the business. Ai systems enhance production by collecting substantial amounts of information to automate processes and workflows within the organisation. Examples of AI systems include quality-control systems, robot-assisted production, self-driving logistics vehicles and the simulation of product lines. The Coca-Cola Company is among the global corporations that have decided to automate its operations, processes and workflows with the objective of enhancing efficiency and production thereby boosting its growth. However, the company faces a major concern associated with implementing AI systems. Workers exhibit fear that increasing capital investment in AI systems may replace their roles at the workplace thus rendering them jobless. Implementing the change requires proper leadership skills on the part of the executives of the organisation. 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Human resources and the resource based view of the firm. Journal of management, 27(6), 701-721. Read More