Teys Australia's Supply Chain Strategies

Introduction

Teys Australia is regarded as a meat processing company with the headquarters anchored at the Freeway Office Park, Logan Road, Eight Mile Plains, Australia. The company prides as being the second largest meat processor as well as exporter in Australia. Teys is known for working in partnerships including the Consolidated Press Holdings and the Canada Packers Inc. Teys Australia is essentially committed to production as well as maintenance of high standards of quality meat. This is supported by the use of technology denoted as foreign contaminant detection, which ensures that the product integrity is never jeopardised. The same effort is showcased in customer service as well as satisfaction, which are equally given the highest priority. Subsequent implementation of the Electronic Document Management System ensures availability of the real time information for the Teys companies as far as remote access as well as reporting is put into consideration. Over the years, Teys Company sought value from the relationship which is based on trust. This has generated a strong foundation for the supply chain. At one point, the company CEO insisted on the direct value indicators, instead of the complicated trading descriptions, while trying to improve the product. However, challenges have been on the rise with regards to the current grading, as well as pricing system which seem to distort the right supply chain signals. Tom Maguire further insisted on the inventory measures, which may help the company to find the significant value.

Identify the Problem/Opportunity for Improvement

Teys Australia prides itself on provision of good quality with prime cut offs being on high demand while attracting high prices. However, generation of value from the trim is part of the notable challengers for such processors like Teys Australia. In the traditional sense, trim is largely used for hamburgers and sausages among other processed meat products. Notably, obtaining market premiums from no carcass parts and trim is another challenge for the premium products where the company is forced to pay for the whole animal and only attain premium for prime cuts alone (Cox and Cunial 2006). For Teys to get the value of their services, inventory management is thought to be better placed while trying to strike a balance between carrying costs, inventory visibility, replenishment lead time, physical inventory, returns, demand forecasting and price forecasting. As much as Teys Australia stands out as a strong brand, the company still struggles in monitoring the stock while trying to meet the financial needs and consumer demand (Swigert 2014). This equally implies the sense of poor planning and poor decision making which puts the supply chain of the company in limbo. With signs of an imperfect supply chain, Teys Australia apparently shows signs of a troubled inventory which can substantially support the balance of the company costs, the consumer demand and the significant revenue which ensures that the firm remains competitive (Paterson 2006). This also points at all indicators of an improperly analysed supply chain, which also has an impact on the operations and processes. Will little efforts left for the optimization process in the meat industry, the unstoppable costs have repercussions on both planning and decision making in the face of inventory management. The company continues to face challenges while trying to tilt the supply chain in favour of balancing the forces of demand and supply. With chances of disposing off the carcass after extracting the wanted elements, Teys Australia suffers from huge amounts of waste, an overstock or sometimes an undersupply of the inventory itself (Goodwin 2018). This undermines the company’s pursuit of good quality and good performance. Perhaps, the significant part of the challenge resides behind maintenance of effective inventory levels.

It is worth noting that when Teys Australia has an overstocking, there are high chances of wastage which attracts a negative impact on the company’s revenue. This also opens more platforms that would lead to loss of money in cases where excessive inventory is realized. When a company spends more than what it can gain, as it is in the case of Teys Australia, chances are quite high that services and products served to customers may carry a depreciating quality, which may be noticed with time. As noticed by Tom Maguire, the company’s boss, the genetics of Teys Australia are not sufficiently attached to the consumer (Norton and Rafferty 2010). This could be realized in the target carcase weights, which would sometimes be heavier than the customer requirements. This would sometimes imply extra product that is never needed, or poor quality with costs of preserving the meat products passed on to the customers. In the opinion of Tom Maguire, the company faces the risk of stock-outs, which has a severe impact on the business in terms of the revenue received (Condon 2019). This has the implication of more investments that has less returns. The baseline is that the company needs to supply food products at the right time, in the right quality and should therefore attract the right price. Lack of this means the inventory has issues that need more attention.

Choosing the Quantitative Method

In handling the supply chain issues raised from the case of Teys Australia, it is significant to deploy a quantitative method that would look into the insights before working on the solutions. A number of methods lined up include waiting line models, integer programming, decision theory, risk analysis and simulation. In this context, the study picks on risk analysis as the first method which is better placed while working on the optimal strategy (Ilgin and Gupta 2016). This can be associated to decision discretions as well as uncertain patterns linked to occurrences. Risk analysis is regarded as a decision maker amid the probability information concerning an occurrence that can either be unfavourable or favourable. In this case, risk analysis is best suited as a method that would define significant preventive measures said to reduce probability of certain factors from happening while pointing out the countermeasures which would deal with the constrains (Ilgin and Gupta 2016). In the computer field, the facilitated risk analysis process is better placed in identifying as well as assessing factors which would jeopardize the possible success of the given project. In the case of Teys Australia, risk analysis shall draw the relevance of controlling as well as attaining the objectives of supply chain management which constitutes reduction of production costs, enhancing service levels and containing risks linked to the company’s supply chain. Secondly, the study picks on the simulation quantitative method, which provides means of imitating the significant operation of the real world system. Models would be used in representing the functions, characteristics as well as behaviours of the selected system. Simulation models essentially entail equations known for duplicating functional relationship linked to the real system. Upon running the computer program, the mathematic dynamics would form an analogue of the anticipated behaviour of the system thereby leading to formation of the data lists as well as images (Lu and Di Renzo 2015). Simulation is regarded as one of the quantitative approaches applied in decision making while considering the organization’s objective of minimizing the costs. In this context, the simulation model would gain the sense of establishing investigations that would prompt change, compress timeframes and study complex systems which could not be studied in real life situations. For Teys Australia, the simulation model would be progressive in running complex simulations while trying to establish solutions towards the supply chain risks facing the company.

Data Collection/Selection

Before constructing the model for the problem at hand, it was difficult to reach out to Tom Maguire who is the head of the company. However, it was possible to access some of the online interviews he had with different media company before settling on the Summary Order Cost, which is a key input in the model. Through online details, it was possible to also have a hint of the ordering records, which further helped in learning more about a decline in production at Teys Australia (Teys Australia 2018). The required inputs for the model needed the cost of meat, cost of technology, the average quantity for every order and the involved cost while delivering an order. Further details required included the sale price as well as the profit which could be made from the sale on average terms (Beef Central 2018). The probability frequency of 10 weeks could be extracted for the purposes of evaluating the demand. The inputs were acquired from the online interviews and records shared by the company in different online forums. On the side of the investment interest rates, the model would determine the holding cost as well as the highest rate which could reflect on the earning potential while focusing on the Australian market.

Problem Formulation

In the course of formulating the problem, the model worked on the Average Order Cost (AOC) over a predetermined number of attempts. The simulation model further gave room for determination of the Stock-Out Risk percentage denoted as SORP. Upon collecting data, it could be possible to work on the model with first, creating a description of the distribution of probability of the demand. With only a sample of 40 days, it was easier to determine the frequency demand levels. Based on the frequencies, probabilities for every level of demand could be computed which also paved way for determination of the cumulative frequency. Perhaps, the upper and lower limits could be determined based on the desirable width. The rest of the inputs were organized as well as grouped in an orderly fashion. The next table is the order information table which was created for Order Quantity as well as Frequency of the Order. At this point, one could now work on the simulation which would reflect on the ordering costs, demand on daily basis and the stock-out risks. The number of ATTEMPTS was 300 which provide the probable number of the working days. Each attempt would also be randomized with the help of RAND function, which creates a RANDOM number for each attempt. Every random number extracted from the function could then be put through the VLOOKUP function with the help of the demand in terms of kilograms, the upper limit and the lower limit. This could now give a reflection of the demand levels of meat needed in really life from Teys Australia. Apparently, the demand level would eventually be deducted from the quantity of the order in a day before determining the INVENTORY. This would represent the inventory that remains in storage in daily basis. Another variable is the HOLDING COST which is a product of the Market Investment Interest Rate with Inventory which is also the price for every kilo. The model would eventually determine the possibility of having a DEFICIT COST, which would reflect on a higher demand which could not be addressed by the quantity of the order. In summary, the stats should include the STOCK OUT RISK PERCENTAGE and the AVERAGE ORDE COST. The formulas below could be used in this model.

Holding Cost = Market Investment Interest Rate x (Cost of Technology) + (Price per Kilo of Meat (or meat product) x Remaining Inventory in a day)

AOC = SUMPRODUCT (Deficit Cost + Holding Cost)/Number of attempts + Delivery Costs

Deficit Cost = No. of Orders per Kilo x Unaddressed Daily Demand x Profits accrued per sale

Delivery Cost = (Delivery Cost per Order x Frequency of Orders in a week)/Business Days

For Stock-Out Risk Percentage,

SORP Number of times shortage costs were realized/Number of attempts x 100%

Analysis

Based on the model above, a number of outcomes could be determined while adjusting the inputs of the quantity of order and the frequency of the order. The key outputs under observation include the Stock-Out Risk Rate and the Average Order Cost (AOC).

Outputs

The actual outputs under the analysis could only be realized while applying the actual inputs based on the prevailing ordering system. According to the records, quantity increase could only be made at intervals of 1 kilo, which prompted for REAL configuration that could be set in the system.

Real Inputs

Frequency of Order in a week: 1

Quantity of the order in kg: 20

Results shown

The outputs signal the fact that Teys Australia would spend an average of $40.45 on orders and still had a chance of realizing 31.41% of encountering a shortage. A number of configurations could be tried for the purposes of hitting the optimal output a system would realize.

Recommendable Inputs

Frequency of Orders: 2

Quantity of order: 10

From the inputs, the following outputs could be realized

In the second case, the outcomes are more appealing compared to those from the REAL configuration of the inputs. From the recommended inputs, the average that could be spent on orders stood at $10.11, which would attract a deficit of 2.21%. This means that if Teys Australia would run quarter of the REAL AOC, then it stands to reduce the SORP by more than 15 times.

Results and Discussion

The study incorporated simulation and risk analysis of the meat ordering system used by Teys Australia. The simulation model is exposed to variable conditions which are said to influence the demand of meat and meat products on daily basis. The model attracted two sides with one dubbed as REAL, which is also the company’s process, while the seconded is dubbed as OPTIMAL which points at the recommended inputs, which would enhance the frequency while trying to reduce the quantity of meat and meat product orders. On either side, the simulation involved the summed-up distribution and the probability distribution. The peak would hit 6kg, which attracts approximately 13 pounds of meat with the graph falling when the demand goes beyond a certain point. Additional information said to have supported the simulation process include the profit per order, market investment interests, delivery cost of meat and the day of the weekly operation. Recommended order inputs would enhance the frequency of orders by almost twice while reducing the quantity of orders. Such changes would attract an increase in the quantity of order in a day while the price of a kilo maintains the same rate. The attempts done for the first 80 days shows that Teys Australia seem to incur extreme shortage costs, which would imply that the company is not addressing the deficit as a result of low levels of the inventory. While putting the average order cost at $40.45, the company would realize a stock out risk percentage which stands at 31.41%. This means that almost a third of the customers would not be served due to low inventory levels. This attracts a number of factors in play, which would lead to the stock out. First, this can be blamed on poor forecasts in terms of production and demand. While a cow would weigh 1000 kilograms, only 247 kilograms of meat would be extracted from such a cow. However, this is not the standard approximations with some cows having more weight but produce less meat in the end. Secondly, planning is an issue especially where the market could no longer be controlled by the forces of demand and supply. This is prompted by prompted-peaks, which could not be addressed instantly due to lack of the required plans. However, a more recommended simulation shows the probability of enhancing the inventory levels which would significantly address the deficit noted from time to time.

With only a slight increase in terms of the holding cost, then Teys Australia would enjoy a reduction in terms of deficit costs as a result of the product achievement of the required satisfactory level. On the other side, as a result of increased frequency of orders, then the delivery cost is expected to increase. Eventually, the model recommends that the quantity of an order should be reduced to 10 kg with frequency going at two times the REAL configuration input. Such changes would attract a reduction in terms of deficits. The recommended configuration is however accompanied by a decline in the stock out risk percentage (SORP) and the average order cost (AOC). Therefore, the combined use of risk analysis as well as the simulation model attracts a positive impact on the company while trying to avoid stock deficits and trying possible means of addressing the demand in the market. As much as the model would look more appealing, variability of certain factors reduces its ability to address demand in some situations.

Recommendations

As noted from the results and analysis, the management has apparently shown weaknesses of coming up with substantial and more effective decisions that would contain the deficit. This attracts the first recommendation, which attracts changes in terms of models of decision making at Teys Australia. This would ultimately save the company’s reputation and earn it more revenue when compared to the situation before. The deficits seen before must have been as a result of poor management. However, when Teys Australia introduces enabling technologies, then the management stands a chance of limiting the waste and costs while attracting avenues said to enhance the profits accrued by the company. Secondly, Teys Australia needs to increase deliveries in any given week to almost twice the number while reducing the quantity of every order to be delivered. Such adjustments would make the company to realize a reduced stock out risk while saving costs linked to deficits. It should be noted that such adjustments would also increase the quantity of an order to be delivered in any given day. This targets higher customer satisfaction while trying to cut down on the persistent demands or product deficits in the market. Finally, Teys Australia should work on the managerial capacity which should resonate with market dynamics as argued by Tom Maguire. This would enhance the capacity of decision making and therefore appeal to customer taste where necessary.

References

Cox, R.J. and Cunial, C.M., 2006. Best practice within Australian food service, a case study: customer satisfaction with red meat products. Australasian Agribusiness Review, 14(1673-2016-136781).

Paterson, I., 2006. Trust and technology adoption in Australian agribusiness supply chains: a gap analysis approach (Doctoral dissertation, University of Southern Queensland).

Norton, K. and Rafferty, M., 2010. Work, Skills and Training in the Australian Red Meat Processing Sector. A National Vocational Education and Training Research and Evaluation Program Report. National Centre for Vocational Education Research Ltd. PO Box 8288, Stational Arcade, Adelaide, SA 5000, Australia.

Lu, W. and Di Renzo, M., 2015, November. Stochastic geometry modeling of cellular networks: Analysis, simulation and experimental validation. In Proceedings of the 18th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (pp. 179-188). ACM.

Ilgin, M.A. and Gupta, S.M., 2016. Remanufacturing modeling and analysis. CRC Press.

Modarres, M., 2018. What every engineer should know about reliability and risk analysis. CRC Press.