Space Age Furniture Company: Operations & Scheduling Analysis

Introduction: Operations Challenges at Space Age Furniture

The cost of operations is regarded as one of the major issues facing a business. Management must take necessary corrective action to address challenges such as inventory handling costs and labor costs. In certain conditions, management is required to make decisions that improve the quality of operations. Quality improvement initiatives — including cost reduction and waste elimination — are valued throughout business operations. Production management and scheduling, along with the reduction of capital tied up in large inventories, are approaches that can increase both profitability and long-term business prospects.

The options available to Carol are based on an analysis of the cost of holding large inventories as well as the per-unit cost of products produced during overtime. The reduction of subassemblies is also a considerable option; management must make such decisions in order to effectively manage the labor force and optimize production. A third viable option for Carol is to hire a new, untrained employee to serve as an assistant to Ed. This assistant could be trained on the job while also being stationed near the machine during operations, freeing Ed to focus on higher-level tasks. Effective human resource management will enable Carol to reduce the extra burden on Ed while developing an employee who can provide long-term support. Pull production planning is also a recommended approach to reduce cost and improve product quality, since it lowers the cost of producing a unit while increasing operational control.

Job shop scheduling has a wide variety of applications, ranging from initial product design to queue modeling in manufacturing industries. It is particularly important in small manufacturing companies, enabling producers to make the best use of resources while meeting due dates (Bhal, 2011). Job shop scheduling is critical to the success of small and medium-sized businesses that produce multiple products in small quantities.

Reducing Subassembly Quantities and Inventory Costs

Raw materials, components, and parts are all elements of a system that converts inputs into finished products through operations management. Subassemblies are a significant part of this integrated process. The appropriate quantities of these components are essential to achieving optimal performance. Understanding the multiple ways in which parts and components are combined to produce required results is important for managing production effectively. The effects of multi-channel utilization are also relevant to understanding how different outcomes are produced (Gupta & Starr, 2006).

The reduction of minimum subassembly quantities can be facilitated through use of an MRP (Material Requirements Planning) system, enabling workers to focus on the required output levels and produce only the quantities needed. Management must make decisions that support the integrated functioning of the system. Production quantities can be handled through various combinations, increasing performance while reducing cost. According to Bragg (2007), by reducing finished goods inventory and product variation in storage, businesses can lower inventory costs, save warehouse space, and develop a common set of subassemblies required to assemble multiple products.

The cost of maintaining large quantities is not limited to the direct cost of the components themselves — it also includes the space required to store such inventory. Many organizations reduce inventory sizes by adopting approaches that allow them to operate at optimal levels with smaller on-hand quantities. Ordering costs, inventory carrying costs, and related costs such as accounting and management overhead all affect the financial health of the business. The capital required to maintain large finished goods inventories can be reduced by selecting an efficient inventory model (Farouk, Cherian, & Jacob, 2012).

The cost table below reflects the overall savings achieved through the elimination of excess inventory. The total cost saved over six weeks is $4,000 for Gemini and $3,450 for Saturn. These cost reductions had a profound impact on profitability and also reduced the capital required to maintain large inventories.

Trade-Off Between Overtime Costs and Inventory Costs

There are various simple and complex trade-offs involved in calculating overtime cost versus inventory cost. The units of overtime capacity are defined within a given period, and these production hours serve as the basis for the solution. The unit costs of regular and overtime production are calculated in order to assess the cost difference. The cost of holding units in inventory is also estimated in relation to the relevant periods. Production scheduling and inventory controls provide an in-depth understanding of best practices for a given scenario.

The trade-off between overtime costs and inventory costs requires a management approach that schedules production in accordance with actual demand. The cost of holding large inventories is greater than overtime cost; however, this can be addressed through improvement in the production schedule and elimination of unnecessary subassemblies. The following figures illustrate the trade-off:

The trade-off for this scenario is clear: the business would incur $1,500 to hold the parts in inventory for one week, while the overtime cost to the employee is only $33. It is therefore evident that the business should prefer paying overtime rather than ordering production of required units in advance. These costs are directly relevant to any decision-making process evaluating the trade-off between overtime labor costs and inventory holding costs.

Scheduling is primarily concerned with the distribution of constrained resources across activities with the objective of improving performance measures. Good scheduling algorithms can lower production costs significantly. Problems in scheduling studied since the 1950s were relatively simple compared to modern techniques. Prabaharan (2006) developed two heuristic algorithms — a priority dispatching algorithm and a simulated annealing algorithm — which generate optimal schedules for manufacturing and minimize completion time. An ant colony optimization algorithm has also been proposed to solve the two-machine flow shop scheduling problem with the objective of minimizing total completion time.