Strategic Operations Issues Essay
Strategic Operations Issues
Using your own organisation or a local organisation that you know well, identify a strategic operations issue or problem that needs to be addressed. Describe the organisation briefly and briefly explain the specific operational issue, problem or process that needs to be addressed and provide clear details of its functions within the organisation; analyse the problem using a concept covered in the subject; consider the performance objectives of operations management; changes to performance objectives or outcomes; improvements; theory and recommendations for improving the system or operation.
Manufacturing plant operation issues of XYZ Plastics Pty Ltd This paper studies the operations issues of XYZ Plastics Pty Ltd’s manufacturing plant. In the last three months, the plant experienced a decline in % OEE (% Overall Equipment Effectiveness) from 90 per cent to 81 per cent, the raw materials inventory increased 10 per cent from 900 tons to 990 tons and increased the outsource warehouse cost from $100 000 per month to $150 000 per month. The overtime cost was also increased by 50 per cent from $20 000 per month to $30 000 per month.
The analysis of the manufacturing process and workflow revealed that the root causes were: a) Inadequate communication linkage between the Planner and Maintenance supervisor which resulted in lack of critical spare parts to service major equipment breakdown. b) The batch size of white product was too small which contributed to speed loss and quality loss c) The Planner’s order quantity of raw materials was too big and caused average inventory to increase and inventory overflow to outsource warehouse. d) The double handling of raw materials by the warehouse staff which contributed to the wastage of overtime resources. The study recommended the company:
a) Use a Kanban system to signal “pull” to the Planner to order spare parts for maintenance department. b) To set up an operating policy to limit the minimum production batch size to ˃24 tons for white product. c) To maintain a discipline of using the Economic Order Quality (EOQ) for replenishment order of the raw materials and reorder according to the inventory profile based on safety stock, delivery time and consumption rate. d) To provide training to the operating staff regarding the Wastes of Lean Manufacturing as a way to promote the learning culture.
Table of Contents
Table of Figures
XYZ Plastics Pty Ltd manufactures and sells black and white plastic materials for toy molding application. Its manufacturing process involves grinding the polyethylene plastics into a molten form and mixing with black pigment or white pigment. It produces a black product and a white product. The products are sold in 25 kg bags. The plant operates 24 hours by seven days a week but only four weeks in a month. It produces a product mix of 96 per cent black and four per cent white.
The General Manager reviewed the monthly operations report recently and found that in the last three months, the operations performance was on a downward trend: a) Its Overall Equipment Effectiveness (OEE) dropped down from 90 per cent to 81 per cent. Pophaley (2010) states that there would have been issues associated with the Availability rate, Performance rate or Quality rate.
b) Raw material average inventory was increased by 10 per cent and the outsourced warehouse costs increased by 15 per cent. c) There was 50 per cent more overtime paid to fulltime workers.
The sales volume remained unchanged at around 672 000 kg per month in the last three months. This was alarming because it suggested that the profile and productivity would have dropped. The General Manager called a meeting with its operations team to review the performance data and operations issues. They traced the problem back to the manufacturing processes and workflow to determine the root causes and take corrective actions.
Discussion and analysis
The manufacturing process involves loading the plastic pellets and pigments into the hopper, the grinder and mixer converts the mechanical energy into heat then melts and mixes the raw materials. The mixed molten paste is extruded out through a pressing die with 400 holes to form spaghetti like strips. The strips are cooled by water and cut into pellets by the rotary cutter, the slurry is then spun in a water separator to remove the water and dry the plastic pellets (see Figure 1 below). The products are then packed into 25 kg bags and stacked onto a pallet as a 1000 kg lot.
Figure – Manufacturing process schematic diagram
2.1 OEE performance trend analysis
The operations team reviewed the % OEE trend of the last three months and looked at the date of three components that made up the % OEE, namely Availability rate, Performance rate and Quality rate mentioned by (Pophaley 2010). They found that all three components were on the decreasing trend which contributed to the % OEE decrease from 90 per cent to 81 per cent. The operations team found that the Availability rate dropped from 95 per cent to 93 per cent which indicated that there was increase of equipment breakdowns. They looked at the maintenance log book and found that there was an increase of pressing die cleaning frequency. During the pressing die cleaning, the process is shut down and the maintenance staffs needs to spend 16 hours to drill holes to clean the dirt out. This maintenance job is usually outsourced and they have a spare pressing die but this was not returned to the plant on time from the cleaning services supplier because there was a miscommunication between the Planner and the Maintenance Supervisor. The workflow is described in (Figure 2 below).
Figure – Process mapping of workflow
The maintenance supervisor left an email message to the Planner, to order the die cleaning service which the Planner overlooked. There was a delay in sending the spare pressing die out to the supplier for cleaning. If they had the spare pressing die ready, they would have saved the downtime of 16 hours for cleaning the die. It appears that a Kanban pull system would have provided better systematic demand information as stated by (Claudio & Krishnamurthy 2009) for parts replenishment. The operations team found that the performance rate dropped from 97 per cent to 92 per cent of their maximum capable production rate of 1030 kg per hour, based on the current deteriorated production rate of 947.6 kg per hour (0.92 × 1030) the plant will be behind the monthly production target.
The operations team reviewed the production log book and found that the production speed loss was due to increased production runs of white products. For every white production run, the operating staffs needs to clean the whole production line thoroughly because the black pellets in the previous run will contaminate the white products. The cleaning process required two hours per run. The plant also needs to spend two hours to change the rotary cutter as well. The plant normally runs one white product per month (26 880 kg) but in recent months the sales team requested four smaller white product runs per month (6720 kg × 4). The rationale was there were four smaller customers willing to pay higher price for the white products but they required delivery at short notice without providing forecast to the Planner.
The smaller run size of white product contributes negatively to the per cent performance rate, the cleaning time of two hours per run and cutter changing time of two hours per run contributes a total of four hours of speed loss per run which could have been utilised to make more products.
The Planner and sales team did not communicate with each other on this change and assess the economy of scale for the production run. The operations team re-assessed the change with the capacity size decision process, as (Rabta & Reiner 2012) suggested that optimal values of production batch size will reduce cost and lead time. The operations team took considerations on algorithms of the extra cost required for the cleaning, changing cutter and speed loss for different batch sizes, and generated a graph (see Figure 3 below) to assist the decision on the size of the production run.
Figure – Capacity size decision graph
The Figure 3 graph showed that the smaller run (6720 kg per run) costs more to make per ton and even selling at increased price, it was making a profile margin of only $100 per ton. In comparison, the larger run size (26 880 kg per run) costs less to make but even selling at normal price, it was still making $400 per ton. The graph showed that the most economical run of the white product is ˃24 tons. The operations team also found that the quality rate dropped from 98 per cent to 95 per cent. They checked the quality records and determined that this was due to the rework materials generated from the increased white runs. The manufacturing team used raw materials to purge clean the process system to avoid contamination. The purged materials were used later on the black runs as rework. The time they spent on the rework could have been utilised to make good product.
The above analysis determined the root causes which caused the % OEE drop from 90 per cent to 81 per cent and it is illustrated below:
% OEE = Availability rate % × Performance rate % × Quality rate % Historical % OEE = 95% × 97% × 98% = 90%
Current % OEE = 93% × 92% × 95% = 81%.
2.2 Raw materials inventory analysis
The operations team reviewed the inventory performance on the monthly operations report; it showed that the raw material average inventory volume increased by 10 per cent from 900 tons to 990 tons in the last three months. Due to the increase in average inventory, the plant ran out of storage space and therefore it required the inventory to be stored at an outsourced warehouse location. The plant storage space was enough to store 1000 tons of raw materials but from time to time the space is filled and overflows the inventory to the outsource warehouse especially in the last three months. As a result, it increased the storage costs by 15 per cent from $100 000 to $115 000 per month including logistics and labour costs for the material handling.
The operations team interviewed the Planner regarding the order quality and order frequency. The investigation revealed that the Planner was not ordering based on inventory management principles. The ordering process takes about one week, from placing the order to receiving the goods. The plant normally keeps a safety stock of 672 tons of raw materials (roughly enough for one month consumption). This was based on their experience that the raw materials comes from other state and shipment delays could be up to four weeks; this safety stock is to maintain the service levels of the company. The raw material comes in normal order quantity of 2.5 weeks consumption, about 420 tons (672 × 2.5/4 = 420 tons).
The Planner starts to place order when the inventory gets close to 672 tons (the safety stock mark). When there is a supplier promotion, the Planner would order a bigger quantity than 420 tons, thinking that would save the company money. There were a number of supplier promotions in the last three months and the Planner placed a bigger order quantity of 470 tons for each promotion which resulted in an increase in average inventory level from 900 tons to 990 tons. The operations team constructed a graph to determine if the Planner was making the correct order quantity decision. They used the total inventory cost based on two inventory cost components mentioned by (Schreibfeder 2009), namely holding cost and materials order cost.
Figure – Economic order quality(EOQ)
The EOQ graph in Figure 4 indicated that the order quantity of 470 tons which the Planner placed on the supplier’s promotion was not economical. Rather than saving the company money, the total cost was higher than that of the EOQ of 336 tons. Even the normal order quantity of 420 tons was not as economical as the EOQ of 336 tons. The inventory profile with the new EOQ of 336 tons is shown in (Figure 5 below). It was constructed according to the method shown by (Silver & Zufferey 2011) with the probabilistic replenishment lead time and slope (consumption rate). If The EOQ of 336 tons was adopted and orders were placed when the inventory touches 840 tons, it would have kept an average inventory of 840 tons and maximum of about 1000 tons on site. The inventory profile showed that average inventory (840 tons) would be lower than the current average inventory (990 tons) and most importantly it would eliminate the need for outsource warehouse.
Figure – Raw material inventory profile
2.3 Labour cost analysis
The operations team reviewed the profit and loss statement and found out that there were 50 per cent more overtime paid to fulltime workers. It increased from $20 000 to $30 000 per month in the last three months. The amount of overtime was mainly paid to the warehouse staff. The operations team interviewed the warehouse supervisor and found that when the raw materials warehouse is full, they would move the materials to the finished goods warehouse for temporary storage, and shift it back when there are more spaces. One round trip to transport one ton of materials on a fork lift would require two minutes. These are considered double handling of materials as stated by (Liker & Franz 2011). This is one of the Wastes in Lean manufacturing namely (transportation) that should be avoided (Slack, Brandon-Jones, Johnston, R & Betts, A 2012).
The drop in Availability rate was due to the longer breakdown time required to clean the pressing die as there was no spare pressing die available. It appears that both the Planner and Maintenance supervisor required an improved communication system for the spare parts ordering. The capacity size decision graph in Figure 3 showed that the most economical run of the white products is >24 tons. Any smaller run size would result in speed loss and contribute negatively to the performance rate. The quality records showed that the drop in quality rate was due to the rework materials generated from the increased white runs per month. These root causes need to be addressed in order to improve the % OEE from the current level of 81 per cent back to 90 per cent.
The EOQ graph in Figure 4 indicated that the order quantity of 470 tons which the Planner placed on the supplier’s promotion was not economical; even the normal order quantity of 420 tons was not economical. The associated total costs were both higher than that of the EOQ of 336 tons; it increased the average raw materials inventory level on site and causes inventory overflow to the outsource warehouse. There were double handling of materials by the warehouse staff and this is a “transportation waste” which needs to be avoided according to the lean manufacturing principals described by (Liker & Franz 2011).
The maintenance supervisor should use a Kanban system to signal a “pull” to the Planner to order outsource services to clean the pressing die. Kanban is a visual signal system advocated by the Japanese manufacturer Toyota and mentioned by (Liker & Franz 2011). The Maintenance supervisor can use a red box to signal that the pressing die is ready to be picked up and send outside for cleaning. When the cleaned pressing die returns, it can be placed in a green box indicating it is ready to be used, similar to the dual card system described by (Chen & Subramaniamac 2012).
The plant needs to set up an operating policy which states that the minimum size is to be > 24 tons for white product.
The Planner needs to maintain a discipline of using the EOQ of 336 tons for replenishment order of the raw materials and placing the purchasing order when the raw materials inventory touches 840 tons (a re-order point about one week before it reaches the safety stock of 672 tons). The inventory profile should be reviewed every three months to update the consumption rate to determine the new re-order point.
The plant needs to provide training to operating staff regarding the Wastes of Manufacturing especially the warehouse staff as a way to promote the learning culture as advocated in Toyota and stated by (Liker & Franz 2011). The operations performance such as OEE should be shared with all staff and initiate counter measures to poor performances. This will connect the staff to the continuous improvement cycle to achieve operational excellence.