A key factor for motivating intending buyers of raw materials is vendor responsiveness. Therefore, to meet demand, a pre-approved level of stocks is often maintained. In contrast, the decision to keep an uncontrolled amount of stock could be counter-productive with cost components associated with holding often ignored unintentionally. In this study, the objective is to develop a spare parts inventory model that incorporates ignored holding costs with a storage constraint for a motorcycle assembly plant (MAP). The inventory policy, structure of holding costs, and spare parts sales reports were consulted for relevant data. The spare parts were categorized and selected using ABC analysis. A spare parts inventory model, which considers ignored holding cost, was formulated. The model was executed using Lingo optimisation software release 18.0.56 to determine the pair of the order quantity (Ɋ) and reorder point (Ɍ). 177 spare part items were identified using ABC analysis. The parts categorisation revealed that 21, 31, 125 part items belong to categories A, B, and C with 81, 15 and 4% of annual sales value, respectively. From category A, nine items contributed significantly to overall sales. The demand pattern for these items was probabilistic based on their coefficient of variation. The pair (Ɋ, Ɍ) for items N, Z, AY, K, AM, J, P, AL and AZ are (174,688), (71,147), (78,150), (86,163), (18,15), (88,170), (128,118), (33,43) and (87,152), respectively. These pairs yielded a total inventory cost of ₦2,177,363 when compared to the current total inventory investment of ₦6,800,000 resulting in a 67.9% cost reduction. A model to manage spare parts inventory with relevant holding cost components was developed for MAP to ensure the availability of items, maximize usage of storage space, and minimize total inventory cost.
Flood events around the world result in the loss of human lives, disruption, damage to economic, infrastructural and ecological systems. Although, different frameworks to manage flood events exist; however, the complexity (i.e. adjustment and adaptation) associated with some of these approaches is often limited by constraints of time and resources. Therefore, this study attempts to apply a flexible project structure to schedule a post-flood recovery project (PFRP). Twenty-five (25) restorative activities in a PFRP were identified, categorised and scheduled as resource-constrained project scheduling problem with a flexible structure (RCPSP-FS). Monte Carlo simulation was used to reflect the uncertain characteristics of each restorative activity. PFRP completion time was 42 and 86 days under time and resource constraints assumptions, respectively. Thirty- four (34) network paths (sub-projects) were identified and grouped into 4 restorative measures as follows: (i) removal of hazardous materials (ii) evacuation of injured persons (iii) provision of flood technology warning system and technical facilities and (iv) construction of shelter, homes and bridges. Time and cost flexibility values for the network paths range from 6 to 63 days, and 14.79 to 288.77 thousand USD, respectively. Time and schedule sensitivity analysis revealed the impact of each restorative activity on simulated project completion time. Based on these results, it is concluded that a flexible project structure can respond to changing circumstances during post-flood restoration efforts which allow more degree of freedom in activity scheduling, flood events measures and cost alternatives.