Inna N. Pakholkina, Anna Y. Gorlach

Samara State Aerospace University (SSAU), Samara, Russia

STATISTIC IMITATIONAL MODELLING FOR RISK ASSESSMENT IN LEAN MANUFACTURING INVESTMENT PROJECTS

 

Lean manufacturing concept represents a product development, supplier and customer relations management system with a customer-oriented production, which allows developing a product that meets customer needs and has fewer defects compared to mass production products. Lean manufacturing systems make it possible to reduce labor and capital costs and make a product less time-consuming.

Lean manufacturing models benefit companies’ competitiveness through:

·                                                                                                                                                  Minimizing production cycle time;

·                                                                                                                                                  Reducing costs of production program fulfillment;

·                                                                                                                                                  Raising order handling and product quality.

Implementing lean manufacturing adds to company’s ability to grow, reduce production costs and work up a market. Nowadays it is crucial for companies to become more effective, and since lean manufacturing does not require expensive innovations but reasonable usage of organizational potential, it becomes a beneficial and appealing system.

But still there are definite risks associated with lean manufacturing concept implementation. As it requires the products to be more customer-oriented and produced not in bulks but more individually, it makes it impossible to stock raw materials for flawless production and thus makes all the successive production stages dependent on the fulfillment of the preceding ones. All this accounts for the risk of project fulfillment delay. And for the money purchasing power decreases with the lapse of time, long-dated investment projects may become risky in terms of net present value reduction.

In the following paragraphs we’ll show the use of lean manufacturing in short-haul passenger aircraft development, and assess the risk of net present value reduction by more than 10% of projected figure.

The project stages with their planned duration and cost are listed below in Chart 1, and the arrow diagram is shown as Image 1. The purchase price of the finished product is 900 million rubles.

Chart 1 – Project stages

Image 1 – Arrow diagram of the project

Net present value of the project is calculated by the 13% discount rate. Discounting is accurate within 1 day, using the following formula:

                                                     (1)

where S – discounted value,

P – nominal value (costs or revenue),

r – discount rate (r = 0,13),

t – time from the day of project launch in days.

Cost of each stage is paid right before the stage starts, and the purchase price for the finished product is paid upon the last stage fulfillment.

Projected net present value of the project at issue is 275.28 million rubles.

We’ll consider the highest possible deviation of project stages duration and cost to be ±20% of the planned value.

Random external factors influence is described by the following model, which uses the normally distributed random numbers:

,                                                (2)

where z0 – planned value,

γ – element of random numbers with normal distribution law,

kv – variation ratio for the parameter at issue (kv = 0,2),

z*  – value including random factors influence.

Statistic imitational modeling procedure includes, firstly, generating a number of NPV values calculated for random factors influenced data, and secondly, risk estimation by normal distribution law with the generated statistical sample parameters. For data without any variation limitations apart from variation ratio, the risk of the NPV reduction by more than 10% of the projected value is estimated to be 0.29.

Adding some limitations to the model makes it possible to reduce the risk level. For example, if the duration figures shown in Chart 1 are considered to be pessimistic estimates (i.e. duration in the worst possible conditions), the random factors influence is to be calculated using the formula

                                      (3)

This means that the duration can be only shortened by the random factors, but can never be extended. With the absence of any further limitations (e.g. for stages costs), the risk of the NPV reduction by more than 10% of the projected value is estimated to be 0.22.

On the other hand, the costs figures may be considered as pessimistic while the duration estimates stay realistic; in this case, costs variation is calculated with formula (3) and duration variation by formula (2), and the risk of the NPV reduction by more than 10% of the projected value is estimated to be 0.04.

Shall both parameters estimations be pessimistic (and thus their variations calculated using formula (3)), the risk in question will be only 0.014.

Overall, putting restrictions on the parameters of the project can lower the risk level considerably; investments restrictions are more effective than time restrictions in terms of risk cutting.

Altogether, investment projects parameters calculations produced with statistic imitational modeling proved the fact that, when processing long-dated investments projects, it is crucial to make thorough planning and choose parameters estimations carefully in order to minimize risks and raise stability and effectiveness.