The French firm Michelin has been operating in Dundee for over 46 years and at the end of 2018 announced its closure plans for mid-2020 after having their ‘volume cut three times this year’ (BBC News, 2018), this will result in a loss of 845 jobs which is a major blow to the Scottish economy. One of the reasons for the fall in demand for Michelin tyres according to BBC News is that there has been a structural change to the general European tyre market as the UK is ‘being flooded with cheap Asian imports… selling smaller tyres for as little as £14.60 each’ (Russell, 2018). Production has slowed down significantly as Michelin can’t compete with the prices that Asian companies are setting in the UK. They produced their lowest volume ever in 2018, signalling a movement towards shutting down. Another key factor that might influence demand is that compared to the past there is now a much larger range of tyres available. According to BBC News, in 1997 there were only 19 different sizes, whereas in 2018 there are 120 different tyre sizes. Michelin in Dundee only produces one size that is very small, therefore they cannot easily compete in the global market as demand falls when there is more consumer choice.
Less significantly, there is also natural seasonal fluctuations in demand, meaning when car sales are low, tyre demand will also fall, but then this can rise again very quickly as cars and tyres are perfect complements.
Michelins plans to close in Dundee is following a pattern of other tyre factories shutting down, ‘Goodyear-Dunlop in Birmingham shut shop about four years ago’ (Russell, 2018). This is showing a general decrease in demand for UK produced tyres as they cannot compete anymore in a growing global market.
For Dundee specifically, they produce relatively small tyres that are 16 inches and below, supplying for smaller cars. Recently however, there has been a significant rise in demand for bigger cars and a fall in demand for smaller cars which Michelin in Dundee is currently catering for. The chain of demand and supply is also a very important aspect of demand as Michelin will be supplying to garages who stock tyres. In the increasing fast paced economic environment and with increasing competition, garages are less likely to stock a range of tyres as they want to reduce costs. This puts Michelin under pressure as the tyres will be delivered to order so they will not be able to do big shipments of stock, instead waiting for garages to order what they need.
Within Europe, emissions testing has become much stricter which has meant a dent in car sales, showing again how the chain of production effects demand as they are interrelated. The ‘declining demand for tyres in western Europe and China in part is because of new EU emission standards’ (Keohane, 2018). In order to meet these standards, car production has fallen which has led to tyre demand also falling. In general, Michelin tyres in Dundee are in increasing global competition and with emissions rules and other restrictions, this creates a secondary demand effect on tyre sales.
b) Explain how you would forecast the demand for Michelin tyres
The purpose of forecasting is to predict the future demand for products or services so as to reduce uncertainty. When forecasting demand there are many methods that can be used, these can be categorised into qualitative or quantitative. Michelin tyres has been operating in Dundee for over 46 years which means they will have historical data available to use for forecasting, using a combination of quantitative and qualitative methods will give the most accurate and unbiased outcome. An important quantitative method is using a time series model, it focuses on ‘patterns and pattern changes, and thus relies entirely on historical data’ (Chambers, Mullick, & Smith, 1971), this utilises past data and will be appropriate for Michelin. According to Shahabuddin (2018), this is a very common forecasting approach as it is mathematical, giving numbers that are comparable.
To go about this method of time series modelling, you will need a minimum of two years of sales history so that you can see previous trends of costs, sales and profits. This data is published annually by the company as they are publicly limited, meaning the company accounts are available to the public. Time series models can highlight random and seasonal fluctuations in the long run, as well as showing a reflection of the business cycle, highlighting any ‘regularity or systematic variations in data’ (Chambers et al., 1971). The forecasting equation shows that the actual data is equal to the trend plus the seasonal fluctuation. To get the prediction for the next period, you will need the previous period sales for Michelin, and adjust this with the difference between the previous periods to find a figure for the next periods demand using sales figures. This method is relatively low cost as it uses data that has already been collected, giving unbiased figures.
This is however only using historical data, sometimes ‘without understanding it’s underlying basis’ (Shahabuddin, 2009, pp 671). In reality the economy is constantly changing, especially when cheaper Asian imports are increasingly flooding the tyre market. This model therefore needs to be combined with a qualitative method that can give more direction such as getting an expert opinion. This can be a costly process for Michelin due to the nature of the job, however they will not need to collect elaborate data which reduces certain costs. They will be looking at past data and at Michelins relationships with its customers to establish future demand levels, additionally also discovering the elasticity of demand. One good example of this method is the Delphi Method where a panel of experts individually look at the problem and then come together to come up with a consensus opinion. This is repeated until they come to an expert conclusion and this is fed back to Michelin to show them demand forecasting. The experts will be considering the constantly changing environment of the tyre market and therefore come up with more accurate results. Combining these two methods will result in a higher degree of accuracy than just using one of them alone as it utilises multiple viewpoints.
c) What insights could be gained by using Input-Output analysis to understand the economic impacts of the Michelin closure on the Scottish economy?
Using Input Output analysis to understand economic impacts in Scotland is insightful as it allows you to see the multiplier effects. The Scottish government has published input-output data on the Gross Value Added of industries in Scotland which aggregates wages and profits, tyres come under the category of manufacturing. The tables show that the manufacturing industry in Scotland has only had two significant changes in GVA to the economy; in 2003 adding £49 million and in 2009 adding £181 million which doesn’t show very strong growth over the past 20 years. This could be a reason for Michelin deciding to close.
There are different kinds of multipliers that input output analysis can show; output multipliers which encompass direct and indirect effects, employment multipliers and household income multipliers. Input-Output analysis shows that there is ‘interdependencies among components of input-output networks’ (Tan et al., 2018, pp141), showing how multiplier effects are caused.
Output multipliers show the change in demand directly for tyres specifically and indirectly for the economy as a whole. The tables produced by the Scottish government show that in 2015, the output multiplier for manufacturing was 1.5 which although is positive, it’s fairly average when compared with other industries in Scotland. When Michelin in Dundee will close in 2020, the output for manufacturing will fall, making it a decreasing secondary industry. Sales will fall for tyres in Scotland and the economy will suffer from 845 job losses which is an employment multiplier.
According to Optimal Economics (2016), the employment multipliers show that there will be direct job losses in the tyre industry specifically for Scotland, and also indirectly there will be a loss of jobs in industries that are interrelated. The input-output tables show the employment multiplier for manufacturing is only 1.5 which ranks very low down when compared to other industries such as the petroleum industry. Additionally, when these employees lose their jobs, they will no longer be able to consume as much in the economy which is a spill over effect for Scotland as it will affect the ‘re-spending of wages by employees’ (Pinto and Jones, 2012). Indirectly the closure of Michelin will also lead to a loss of jobs in industries such as the transport system as the tyres will no longer need to be transported from the Dundee plant to customers.
Finally, the input-output analysis can show the income multiplier effect, emphasising the change in household income after the loss of jobs, when combined with the change in output ca be used to calculate the change in income for the economy as a whole. The Scottish government analysis shows that the income multiplier for manufacturing is 1.4 which is ranked 72nd in Scotland. This is a very low multiplier showing that a loss of jobs from Michelin will not affect household income massively.
d) Evaluate the limitations of using Input-Output analysis in this case
The limitations of Input-Output analysis are that they have quite a few key assumptions when producing the data. The first key assumption is that there is a constant return to scale where an increase in inputs of labour or capital leads to a proportional increase in output. This is not always the case for Michelin as if they were to increase labour, output increase is not necessarily proportionate. This can be considered an important limitation as Michelin’s output was not increasing enough to survive, by adding more input it did not result in an equal change in output, making the assumption redundant.
Another key assumption made during input output analysis is that there are no supply constraints, implying ‘no restrictions to raw materials and employment… enough to produce an unlimited amount of product’ (Cheney, 2018). Michelin will however have constraints on employment as not everyone has the right skills for the job, importantly labour is not a non-scarce resource for Michelin or for the economy as a whole.
Less importantly, the table is only a snapshot for a year as the tables are produced every five years as it is costly, the data can also be ‘limited by third-party privacy’ (Pinto and Jones, 2012, pp30) which simplifies the results. The model is therefore static unless compared with the next or previous year of data which will be very costly for Michelin to produce.
Within the analysis, it is assumed that there is no technology improvement over the year. Cheney (2018) explains that input output assumes that an industry uses the same technology to produce everything. This is not the case for Michelin as they will constantly be improving their technology so that they can be as efficient as possible. This is not a very serious limitation, it will just be a consideration as it will not seriously affect output and employment which are the important results in the analysis.
A final limitation of Input-Output analysis that can be applied to this case is that prices are constant and fixed. For Michelin, the costs of production will constantly be changing as inputs become more scarce. This is however only a consideration and not serious as Michelin can make conversions in order to account for this.
In conclusion, input output analysis makes some key assumptions and it can’t tell you everything, in order to be more accurate it needs to be combined with other methods to get the best results.