Tea and More business are renowned as a wholesale operator of assorted tea. The company was founded as Global Tea by three sisters in 1985 as a supplier of unusual and specialty blends (Doyle & Bell, 2014). Jack Reynolds and his two friends bought the company in 1992 and changed its name to Tea and More (Doyle & Bell, 2014). They also overhauled the operations by introducing new suppliers, designs, production lines, and a product catalog. These business modifications brought much success, which allowed Jack to buy out his friends’ shares and maintain sole control of all decisions in the organization.
Tea and More’s major problems include the decision-making process and supply chain management. It takes a long time for the decisions to be approved since all employees need to confirm their ideas with Jack. Additionally, the business struggles with supply chains management issues such as sourcing, packaging, marketing, and tea distribution to their customers. These problems affect the productivity of the company, which is beginning to lose customers to the competitors.
Optimal decision-making is critical for the profitability and improved performance of a business. Organizational leaders use a combination of intuition and logic to make the right choice. Rational decision-making involves a critical evaluation of the available evidence to establish relevant decisions and alternatives objectively. Intuitive decisions come as a result of personal insights and experiences (Abubakar et al., 2019). Adoption of the suggested solutions to the company’s challenges comes after deliberation with other members. However, Tea and More had a traditional decision-making approach where the top management, run by a sole individual, made all the decisions without consultation with lower levels. As a result, the business always had sub-optimal solutions to the existing challenges that led to poor performance. One mechanism of solving decision-making challenges at the Tea and More should incorporate decision support systems (DSS). The preferred DSS emphasizes communication, data records analysis, documenting changes, and using knowledge from consultants and experts (Yazdani et al., 2017). Using these approaches will help understand all facts and explore different solutions to the company’s problems instead of relying on one person to make all the decisions. However, one challenge with this suggestion is it might lead to a prolonged decision-making process.
Another solution is changing the overall company’s culture and organizational behavior. First, Jack himself does not allow other people to make any decisions concerning the company, and he blames all the employees for the business’s challenges. His approach has created an unmotivated workforce and increased turnover. Additionally, the contract sales staff, responsible for over 15% of Tea and More business, are not compensated for their efforts (Doyle & Bell, 2014). Instead of finding a lasting solution, the management uses threats and a carrot approach to force them into work. These techniques have created an unfavorable working environment, resulting in low performance, and the company is losing its market. The change in culture and organizational behavior involves incorporating new values and beliefs. Additionally, the personnel in Tea and More should start socially interacting and being open to new ideas that would bring cultural as well as behavioral changes. Such activities foster effective relationships and a better understanding of the employees, suppliers, marketers, and consumers. This would prevent loyal and long-term clients from turning to competitors, as happened with the company. This approach’s disadvantage is that it might be met with resistance from employees, and the company might focus more on achieving organizational culture than performance.
The other solution to this problem should be investing in supply chain management systems and big data analytics. The company currently faces logistic issues, particularly with the EML’s three-month waiting period for their California production plant. This waiting period means that if the business unexpectedly runs out of stock, they usually wait for a long period, which forces some clients to buy from the competitors. Data analytics will enable Tea and More to collect, store, and analyze large volumes of data from its network of suppliers, distributors, sales representatives, and customers to generate market trends, patterns, and insights (Tiwari et al., 2018). This information will help the company eliminate the supply chain management issues such as operational costs, as well as production and distribution delays. The drawback of this solution is the investment costs involved in purchasing and implementing the technologies.
Choice and Rationale
Tea and More is also losing its market share to its competitors for lack of unfulfilled orders. One approach that the company has not thought of is engaging in customer relationship management (CRM) and corporate social responsibility (CSR). The CRM strategy aims to support the personalization and customization of customers’ needs, services, and needs (Anshari et al., 2019). Big data availability can help Tea and More manage clients’ expectations and introduce products that appeal to them and at the right price. CSR is also concerned with customer satisfaction by engaging in socially responsible behaviors such as charities, volunteering, and other societal goals (Mohammed & Rashid, 2018). These choices will improve brand image, boost satisfaction, and promote sales.
The implementation plan’s first process comprises all the stakeholders, including directors, suppliers, employees, consultants, customers, and sales representatives. The individuals will help create data and the strategy needed that will guide the implementation team. The experts will propose a system that will be more efficient for the Tea and More business strategy. Issues to consider include inventory management, customer requirement processes, warehouse management, logistics, supplier management, and analytics. The final process will involve testing and evaluation of the system.
Abubakar, A. M., Elrehail, H., Alatailat, M. A., & Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, 4(2), 104-114.
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), 94-101.
Doyle, B., & Bell, A. (2014). Reading the tea leaves at tea and more: Resolving complex supply chain issues. Operations and Supply Chain Management: An International Journal, 2(3), 172-177.
Mohammed, A., & Rashid, B. (2018). A conceptual model of corporate social responsibility dimensions, brand image, and customer satisfaction in Malaysian hotel industry. Kasetsart Journal of social sciences, 39(2), 358-364.
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330.
Yazdani, M., Zarate, P., Coulibaly, A., & Zavadskas, E. K. (2017). A group decision making support system in logistics and supply chain management. Expert Systems with Applications, 88, 376-392.