Reference no: EM133646706

When Julie Weller joined LLC, the company only produced high-end components in its product line. Much of the assembly was handled by highly-skilled operators, many of whom had been with LLC for 7-10 years. On the contrary, their two recent acquisitions had been in less complex products, with more dependence on automated assembly techniques and equipment, and less dependence on skilled workers. LLC's Manufacturing Department had made good progress over the past 18 months with the integration of the two companies. The move to the larger facility that housed all three assembly lines had gone smoothly. Operational metrics, yields, cycle times, delivery schedules were maintained throughout the transition. Although Julie was proud of her team's progress, she was confident that the operational efficiency could be improved further.

As a result of the acquisitions, LLC's three production lines had different types of workers, with different levels of skills, productivity, and wage scales. Table 1 in the excel workbook summarizes this information. Table 2 shows the hourly allocation of the workers across pay grades and product lines given that each operator works an average of 40 hours per week.

Given the fluctuations in demand, Julie had felt strongly that operators across all three product lines should be cross-trained on each line's processes. That way, if demand for one product line falls, those operators would be able to help out on another line. After several months, all operators had been cross-trained on all of the processes across the three production lines.

Despite the cross-training, Steve Lo (Manufacturing Engineering Manager) noticed that there were still productivity differences between the workers, largely related to the experience and skill of the operators at the different pay grades. After reviewing the production records over the past several months, Steve put together the productivity table shown in Table 3.

Table 4 shows the weekly production plan for next quarter that is used for planning purposes.

While Julie was confident that she could meet this production plan with her current staffing and alloca- tion, she suspected that she could more efficiently utilize the workers. With the current allocation, there was no room for error; the workers were allocated 100% of their time. E.g., with the current allocation of workers to Long-Haul Telecom, the production capacity equals 160 × 2.00 + 360 × 1.80 + 600 × 1.62 = 1940, which exactly matches the planned production of Long-Haul Telecom parts. There was thus neither time for other projects, nor spare capacity (which often came in handy during end-of-quarter production rushes).

Your goal is to build a linear Solver models (and use SimplexLP as the solution method) to help with several variants of the staffing problem that Julie is contemplating.

Identify the most efficient allocation of the existing staff required to meet the production plan. While it is true that the total wages paid is constant, minimize the direct labor cost required to meet this production plan. You can assume that the number of hours allocated can be fractional, and that the hours of one employee can be split across multiple product lines. How much money does this reallocation save relative to the existing staffing allocation?