This chapter discusses on various optimization approaches that can be used strategically in decision making areas. Therefore it underlines the importance of these techniques in the improvement of the supply chain’s performance and sustainability. When it comes to the modern realities of doing business, strategic analytics are one of the key tools for decision-making that increases companies’ adaptation to their environments. The chapter helps to understand the state-of-practice approaches and identifies the potential of using data analytical methods and presents the performances of multi-objective optimisation methods including evolutionary algorithms, swarm intelligence, hybrid metaheuristic and hyper heuristic methods. The chapter begins with definitions of descriptive, predictive, and prescriptive analytics with fundamental concepts. The latter offers an analysis of how the Particle Swarm Optimization (PSO) mathematical algorithm can be applied to improve a specific logistics schedule. It mainly contains problem definition, data collection, determination of the fitness function and PSO algorithm to determine the best routes. Different configuration of PSO is shown in the context of the case study to understand how it influences optimization results such as cost, transit time and computation time. Thus, this paper aims to focus on stressing tangible benefits, which strategic analytics brings, including increased productivity, competitive advantage, optimization of supply networks and chain and enhancements in financial outcomes. It is always wise to learn from experience and from others and this chapter will be of immense value to any business or student and the guidelines will be very helpful in the implementation of optimization techniques in improving business resilience.