Out of Box Maritime Thinker: July 2019

APPLICATION OF A DISCRETE PROBABILITY DISTRIBUTION, POISON DISTRIBUTION,IN CREATING CRITICAL AND OPTIMAL SPARE PARTS LIST ON OCEAN GOING VESSEL.

Note:

This article is made by modifying parts of “INVENTORY MANAGEMENT OF CRITICAL SPARE PARTS AND ITS RELATION TO PMS” - by Aleksandar Pudar The complete paper can be found on https://www.academia.edu/37334387/INVENTORY_MANAGEMENT_OF_CRITICAL_SPARE_PARTS_AND_ITS_RELATION_TO_PMS

The ocean-going vessel provides transportation service, and the spare parts inventory reliability is crucial.

Spare parts inventory is necessary to provide emergency services in getting the vessels back to work in the shortest time possible.

Over the years, on the older vessels, the companies accumulate an extensive inventory and the formal procedure to manage it that is due for improvement. The improvement may result in funds savings that can be used elsewhere in the company.

Existing inventory policy is being refined, and its implementation and use followed up/enforced to assist in this task.

For the calculation that resulted in the Critical or Optimal Spare Parts List for each vessel, first required variable is estimated off-hire cost ( using current rates or past 12-month average ), the failure frequency, and the machinery lifetime where it will be used.

Vessel being off-hire for any reason, whether it is for inspection, maintenance, or malfunction, costs money. The precise cost aids managers in decision making as to when to arrange vessel for dry docking, or in the case of repairs, how much to spend on getting the vessel running again.

For accurate cost projection, it is required to know what is being affected by off-hire. Vessel unemployment and income reduction may look like the most a significant part of off-hire costs, any actual cost of the off-hire, an estimate should include the value of the opportunities that were lost when the spares were not available.

To calculate the total off-hire cost, we need to sum up the following values.

Labour cost, direct and indirect, of the off-hire.

‘To find the direct labour costs, we took the length of the possible off-hire and multiplied it by the hourly costs of the vessel operators. Calculated indirect labour costs by determining how much of a share of the supervisory and support workload the vessel takes, then multiplied that by the costs of the support staff and managers.’ (Source Unknown)

The cost of direct value loss due to off-hire. The loss = to the worth of the service that would have been produced during the off-hire.
The start-up costs are related to restarting the vessel, including any additional workers needed energy surges, and inspection costs.
The costs are related to the actual repair, either temporarily or permanently.

Because of the cost of off-hire will usually exceed the value of the part, all items are valuable on the same level to the operations of the company, to distinguish items of interest from other items, more data needs to be collected rather than just the price of the parts.

We defined a Critical Spare Parts Calculator that took into account:

Off-hire cost per hour if the part is unavailable
Off-hire if the spare part is available in stock
Off-hire if the spare part is not available in stock
An expected remaining lifetime of the machinery
Estimated failure frequency
Inventory interest (including the cost of storeroom, depreciation)
Cost of the spare part

The calculator resulted in financial value:

Extra cost per breakdown without the spare part in stock (C)
‘Probability that the spare part will be used (P)’ (Olofsson, 2018)
Expected downtime cost without the spare part (=C*P)
‘Expected holding cost of the spare part (H)’ (Olofsson, 2018)
Expected obsolescence cost if the spare part never will be used (O)
Total spare part costs (=H+O)
Expected impact cost

The form is used to calculate the costs if a company buys and keeps the critical spares, or not. The calculation is based on statistical methods and is used to optimise maintenance stores. Filling in figures and estimations, and will result in a critical spare part or non-critical spare part (a buy, or not buy,) recommendation.

For the calculation: estimate downtime costs, the failure frequency, and the lifetime of the machinery where it will be used

Example (modified from- J.Fukuda Feb.2008) :

Identify a required spare parts quantity to achieve a confidence level of 90%. Following data were needed:

Units in need of spares per vessel: 4 units A = 4 EA for two vessels N = 2 vsl.

Each unit operates average 225 RH per month, M= 225 RH

The period between overhauls is 7500 RH, MTBR = 7500 RH

The initial period of use is 24 months, T = 24 m. Using the following formula

By using Poison Distribution [2]equation

0 spare, P = exp (-5.76) = 0.003 = 0.3% < 90%

1 spare, P = 0.003 (1+5.76) = 0.0213 = 2.13% < 90%

2 spares, P = 0.003(6.76+16.6) = 0.0736 = 7.36% < 90%

3 spares, P = 0.003 (23.36+31.85) = 0.1739 = 17.39% < 90%

4 spares, P = 0.003(55.21+45.9) = 0.3185 = 31.85% < 90%

5 spares, P = 0.003 (101.1+52.8) = 0.485 = 48.5% < 90%

6 spares, P = 0.003 (153.91+50.7) = 0.6448 = 64.48% < 90%

7 spares, P = 0.003 (204.6+41.7) = 0.7763 = 77.63% < 90%

8 spares, P = 0.003(246.3+30.1) = 0.8710= 87.10% < 90%

9 spares, P = 0.003(276.4+34.2) = 0.9316 = 93.16% > 90%

By following the above, it is determined that the recommended quantity comes to nine spare parts for 24 months lifespan of the unit in question for two ships to achieve a confidence level of 90%. Of course, to expedite the calculation, an excel spreadsheet is used.

Table 2 – “Vessel Spares Quantity (Poison Distribution) Calculator.” – Created by 3rd Officer Stefan Grozev - 2018©Reederei Nord BV - www.reederei-nord.com

The additional data needed is the lead time on the part and the cost of off-hire of the vessel, rather than just the price or the quantity of a new part. Because the new parts price is less than the cost that would incur while not having it, the cost of off-hire is used, instead, along with the lead time associated with that item.

The spares of interest are those who are not accessible to source in a short period and cause an expensive off-hire loss to the company. It takes some items weeks to receive, while others are readily available at any time. Identification of the lead time is crucial.

Snapshot 1 – Inventory - Snapshot by Author from Danaos Enterprise –Supplies Control Module

After the downtime cost, lead time and required quantity were determined next step is to do standard RA using a matrix for each critical and optimal spare part and critical and standard equipment, and the result will be a standardised ship specific Critical and Optimal Spare Parts List. RAs has been done to ensure that there are no omissions in the use of Critical Spare Parts calculator and to identify additional risks. The goal is to minimise the losses by providing a risk assessment.

The collected spare parts data is ranked in an RA matrix allowing the technical team to visualise which items are of interest.

As it is seen, this process does not replace technical expertise and the experience or technical personnel; it aids it.

Table 3 - Extract from “SHIPBOARD MANUAL – DECK / ENGINE PROCEDURES 2.3 – Critical Equipment and System APPENDIX I: RISK ASSESSMENT FOR CRITICAL EQUIPMENT IDENTIFICATION.”

Snapshot 2 - Critical Spare Part List - taken by Author from Danaos Enterprise –Technical module

Effective inventory management is essential and plays a pivotal role in engineering management.

Shipping companies deal with many spares, in inventory and on top we have to cope with unpredictable demand. The standard way of approach cannot be used, but instead, need to be adapted to fit the environment better to assist the company in inventory management.

1. References:

1. Hmida, J.B, Grant, R & Lee, J. 2013. Research Article Inventory Management and Maintenance in Offshore Vessel Industry. HINDAWI - Journal of Industrial Engineering. [Online]. 2013(Article ID 851092), 1-7. [27 February 2018]. Available from: https://www.hindawi.com/journals/jie/2013/851092/

2. Robinson, A. 2014. Transportation Management Company | Cerasis. [Online]. [20 February 2018]. Available from: http://cerasis.com/2014/09/03/inventory-control/

3. Abuhilal, L, Rabadi, G & Sousa-Poza, A. 2006. Supply chain inventory control: a comparison among JIT, MRP, and MRP with information sharing using simulation. Engineering Management Journal. 18(2), pp. 51–57.

4. Koçaǧa, .Y.L & Şen, A (2006). Spare parts inventory management with demand lead times and rationing. [Online]. (Volume 39, 2007 - Issue 9 Ed.). Journal IIE Transactions Volume 39. [20 February 2018]. Available from: http://www.tandfonline.com/doi/abs/10.1080/07408170601013646

5. Vaughan, T.S. 2005. Failure replacement and preventive maintenance spare parts ordering policy. European Journal of Operational Research. [Online]. 161(1), 183-190. [20 February 2018]. Available from:

6. Olofsson, O. 2018. World-class-manufacturing com - Spare Parts. [Online]. [20 February 2018]. Available from: https://world-class-manufacturing.com/spare/spare.php

7. RNBV. 2015. 23 – Critical Equipment and System APPENDIX I: RISK ASSESSMENT FOR CRITICAL EQUIPMENT IDENTIFICATION. In: Reederei Nord BV, R.N.B.V ed. HSEQ - SHIPBOARD MANUAL – DECK / ENGINE PROCEDURES. Amsterdam: Reederei Nord BV, pp. 3-3, Line 29

2. Bibliography:

1. Toomey, G., 2006. Harnessing the power of maintenance. Power Engineering (Barrington). [Online]. 110(3), 42-47. [20 February 2018]. Available from: https://www.osti.gov/biblio/20741054

2. Da Silva, C.M.I, Cabrita, C.M.P & De Oliveira Matias, J.C. 2008. Proactive reliability maintenance: a case study concerning maintenance service costs. Journal of Quality in Maintenance Engineering. 14(2), pp. 346-356.

3. Ballou, R (2003). Business Logistics Management. (5 Ed.). Englewood Cliffs, NJ, USA: Prentice-Hall.

4. Mabert, V.A., 2007. The early road to material requirements planning. Journal of Operations Management. [Online]. 25(2), 346-356. [20 February 2018]. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0272696306000301

5. Chukwuekwe, D.O (2016). Condition Monitoring for Predictive Maintenance: A Tool for Systems Prognosis within the Industrial Internet Applications. [Online]. NTNU-Trondheim: MASTER THESIS (TPK4950) - Norwegian University of Science and Technology Department of Production and Quality Engineering. [27 February 2018].Available from: https://brage.bibsys.no/xmlui/handle/11250/2404760?show=full

6. Mullai, A (2006). RISK MANAGEMENT SYSTEM – RISK ASSESSMENT FRAMEWORKS AND TECHNIQUES. (1 Ed.). DaGoB Project Office - Turku School of Economics: DaGoB publication series.

7. Maria Elena Nenni, Et. al., 2013. Optimizing Spare Parts Inventory in Shipping Industry. International Journal of Engineering and Technology (IJET). ISSN: 0975-4024 / Vol.5. (Jun- Jul 3), pp. 3152-3157.

8. Stump, E., 2018. Using the Poisson Probability Distribution to Estimate Cost of Re-supply Spares - Galorath, Inc. no-date. Galorath, Inc. [Online]. [16 March 2018].Available from: http://galorath.com/library/abstract/using-the-poisson-probability-distribution-to-estimate-cost-of-re-supply-sp

3. Snapshots and Tables:

1. Snapshot 1 - Inventory - taken by Author from Danaos Enterprise –Supplies Control Module

2. Snapshot 2 - Critical Spare Part List - taken by Author from Danaos Enterprise –Technical module

4. Table 2 – “Vessel Spares Quantity (Poison Distribution) Calculator.” – created by 3^rd Officer Stefan Grozev - 2018©Reederei Nord BV - www.reederei-nord.com

5. Table 3 - Extract from RNBV -“SHIPBOARD MANUAL – DECK / ENGINE PROCEDURES 2.3 – Critical Equipment and System APPENDIX I: RISK ASSESSMENT FOR CRITICAL EQUIPMENT IDENTIFICATION.”

[1] The form is used to calculate the costs if you buy and keep the critical spares, or not. The calculation is based on statistical methods and is used to optimize your maintenance stores. Fill in figures and estimations, and will be presented with a critical spare part or non-critical spare part (a buy, or not buy,) recommendation.

For the calculation: estimate downtime costs, the failure frequency, and the lifetime of the machinery where it will be used

[2] In probability theory and statistics, the Poisson distribution (French pronunciation: [pwasɔ̃]; in English often rendered /ˈpwɑːsɒn/), named after French mathematician Siméon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant rate and independently of the time since the last event.[1]The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.

Disclaimer:

“ Out of Box Maritime Thinker” © 2018 and Aleksandar Pudar assumes no responsibility or liability for any errors or omissions in the content of this paper. The information contained in this paper is provided on an “as is” basis with no guarantees of completeness, accuracy, usefulness or timeliness or of the results obtained from the use of this information. The ideas and strategies should never be used without first assessing your own company situation or system, or without consulting a consultancy professional. The content of this paper is intended to be used and must be used for informational purposes only

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