Maynards operation sequence technique

In today’s competitive world market, manufacturers face more tough challenges and are pressured to find ways for
productivity improvement wherever possible in the entire supply chain. To give competition to other business under the current
global situation, a company needs to reduce or eliminate the idle and downtime of operations and improve the current working
methods. Improve profit is the primary goal of any manufacturing industry. The successes of the sector depend on its
productivity. This productivity decreases because of deficiency in previous standard time for activities carried out by
operators/labours, non-value-added activities involved and the ineffective methods, and imbalance in the material flow. This
paper highlights a methodology developed for standardization in the process activities in different production industries and
research and development departments using Maynard’s Operation Sequence Technique. (MOST) Due to high uncertainty in
demand, it is challenging to meet the demand with existing supply. In this situation, industrial engineering (IE) techniques are
used to resolve the existing manufacturing situation and identify the potential for increased productivity. MOST (Maynard
Operation Sequence Technique) is a good work measurement technique that allows better productivity and Resource
Optimization. The main objective of the MOST method is to reduce the work content and improve the productivity of the process.

1. Productivity Improvement by Maynards Operation Sequence Technique & Time Series Forecasting By Mr. Gajanan Pathak M. Tech II Year 119M0078 Prof. Prakash Vaidya Prof. Suvarna Mane Guides Dr. G. N. Kotwal H.O.D

2. What is time Study?

3. M aynard O peration S equence T echnique (MOST ® ) Considered a revolutionary Predetermined Motion Time System(PMTS) Developed by Kjell Zandin and H. B. Maynard and Company, Inc. in 1974 MOST ® times represent ranges of motions. Very accurate results are produced because ranges are statistically derived. Based on Methods Time Measurement(MTM).

4. Sequence Model and Terminology Activity Sequence Model Parameter General Move A B G A B P A A= Action Distance B= Body Motion G= Gain control P= Placement Controlled Move A B G M X I A M= Move Controlled X= Process Time I= Alignment Tool Use A B G A B P A B P A F= Fasten L= Loosen C= Cut S= Surface Treat M= Measure R= Record T= Think

5. General Move The general move sequence model is: A B G A B P A Action Distance(A) Body Motion(B) Gain Control(G) Placement(P) Index values for the General Move in Basic MOST

6. Controlled Move The controlled move sequence model is: A B G M X I A Move Controlled(M) Process time(X) Alignment(I) Index values for the Controlled Move in Basic MOST

7. Tool Use The controlled move sequence model is: A B G A B P A B P A The other parameters in tool use are: Fasten(F), Loosen(L), Cut(C), Surface Treat(S), Measure(M), Record(R), Think(T) Index values for the Tool Use Action Move in Basic MOST

8. Methodology Shadow an lobour throughout the shift to understand the flow and the work. Map all micro activities carried out by labour in detail Assign the standard timings to each activity as per PMTS Measure the work and Derive standard work content for the entire process Understand the different losses & derive the utilization Identify opportunity for improvements.

9. Time Unit Used in Most A typical MOST work sequence code would look like this: A 10 B 6 G 3 A 6 P 3 A Step – 1 Adds up all the subscript numbers 10+6+3+6+3+0= 28 (the subscript is the MOST index value) Step – 2 multiple the sum of the index by 10. This answer gives the TMU equivalent 28 x 10 = 280 TMU Step – 3 Convert to time in seconds 280U *0.036 seconds = 10.08 seconds. 1 TMU= 0.00001 Hour 1 Hour= 100,000 TMU 1 TMU= 0.0006 Minute 1 Minute= 1667 TMU 1 TMU= 0.036 Second 1 Second= 27.8 TMU

10. Example Labour- Bhau Kamble.xlsx

11. Summary Analysis of Labour’s Perticulars Labour Bhau Kamble Pratik Jadhav Ashok Lohar Tukaram Ahire Genral Information Department Production Production Production Production Designation Labour 1 Labour 2 Labour 3 Labour 4 Shift Genral Genral Genral Genral Shift Information Estimated Time (Min) 540.00 540.00 540.00 540.00 Breaks (Min) 90.00 90.00 90.00 90.00 Effective Working Time (Min) 450.00 450.00 450.00 450.00 MOST Analysis Work content (Min) 334.54 276.46 377.62 334.45 Movement (A) 9.51 13.73 28.53 20.82 Body Motion (B) 0.14 0.14 2.22 2.10 Grasping (G) 0.85 0.89 2.7 2.34 Placement (P) 5.91 1.23 4.4 3.99 Move controlled (M) 0.64 0.64 2.11 1.93 Process Time (X) 0.00 0.00 0.00 Alignment (I) 3.08 3.08 9.30 8.63 Tool Use (T) 53.09 46.93 43.01 34.26 Clocked Activity © 33.01 49.68 41 53.57 Time Wasted 115.46 173.54 72.00 115.59

12. Summary Analysis

13. Average of MOST Summary Bhau Kamble Pratik Jadhav Ashok Lohar Tukaram Ahire Breaks (Min) 90.00 90.00 90.00 90.00 Work content (Min) 334.54 276.46 377.62 334.45 Time Wasted 115.46 173.54 72.00 115.59 Estimated Time (Min) 540.00 540.00 540.00 540.00 Breaks (Min) 90.00 Work content (Min) 330.77 Time Wasted 119.15 Estimated Time (Min) 540.00 Average of MOST Summary MOST Summary

14. Summary of All Micro Activities of Each Labour’s

15. Observation’s Time is wasted for collected material for stitching belt Time is wasted due to long distance between machine and storage Time wasted for adjusting material More time taken for starting their work Time is wasted for adjusting machines more time taken for cutting of material Time is wasted due to collection of material more time taken for handling machines More Time is wasted in clocked activities

16. Result and Conclusion   Time in Min Percentage Shift 540.00 100.00 Breaks 90.00 16.67 Effective working time 450.00 83.33 Work content by Most 377.62 69.93 Time wasted 72.38 13.40 Akash Lohar   Time in Min Percentage Shift 540.00 100.00 Breaks 90.00 16.67 Effective working time 450.00 83.33 Work content by Most 334.54 61.95 Time wasted 115.46 21.38 Bhau Kamble   Time in Min Percentage Shift 540.00 100.00 Breaks 90.00 16.67 Effective working time 450.00 83.33 Work content by Most 276.46 51.20 Time wasted 173.54 32.14 Pratik Jadhav   Time in Min Percentage Shift 540.00 100.00 Breaks 90.00 16.67 Effective working time 450.00 83.33 Work content by Most 334.45 61.94 Time wasted 115.55 21.40 Tukaram Ahire

17. Improvement They can start their work earlier without wasting time They should sort their material in store room for ease of finding them The distance should be less between machine and storage if possible Less time should take for discussion and phone calls i.e. clocked activities

18. Time Series Forecasting

19. Different Time Series Component Trend Component Seasonal Component Residual Component

20. Methodology The quarterly sale of number of orders is given below: Create line chart with markers for visualization of data and the chart is given below: Year Quarter Number of orders per 1000 2017 1 4.8   2 4.1   3 6   4 6.5 2018 1 5.8   2 5.2   3 6.8   4 7.4 2019 1 6   2 5.6   3 7.5   4 7.8 2020 1 6.3   2 5.9   3 8   4 8.4 Step 1:

21. Step 2: Insert Time Column Step 3: Take Moving Average of 4 Period T year Quarter Number of orders per 1000 1 2017 1 4.8 2   2 4.1 3   3 6 4   4 6.5 5 2018 1 5.8 6   2 5.2 7   3 6.8 8   4 7.4 9 2019 1 6 10   2 5.6 11   3 7.5 12   4 7.8 13 2020 1 6.3 14   2 5.9 15   3 8 16   4 8.4 T year Quarter Number of orders per 1000 MA(4) 1 2017 1 4.8   2   2 4.1   3   3 6 5.4 4   4 6.5 5.6 5 2018 1 5.8 5.9 6   2 5.2 6.1 7   3 6.8 6.3 8   4 7.4 6.4 9 2019 1 6 6.5 10   2 5.6 6.6 11   3 7.5 6.7 12   4 7.8 6.8 13 2020 1 6.3 6.9 14   2 5.9 7.0 15   3 8 7.2 16   4 8.4  

22. Step 4: Calculate Center Moving Average t year Quarter Number of orders per 1000 MA(4) CMA(4) 1 2017 1 4.8     2   2 4.1     3   3 6 5.4 5.5 4   4 6.5 5.6 5.7 5 2018 1 5.8 5.9 6.0 6   2 5.2 6.1 6.2 7   3 6.8 6.3 6.3 8   4 7.4 6.4 6.4 9 2019 1 6 6.5 6.5 10   2 5.6 6.6 6.7 11   3 7.5 6.7 6.8 12   4 7.8 6.8 6.8 13 2020 1 6.3 6.9 6.9 14   2 5.9 7.0 7.1 15   3 8 7.2   16   4 8.4    

23. Step 5: Calculate Seasonality and Irregularity T year Quarter Number of orders per 1000 MA(4) CMA(4) S t , I t 1 2017 1 4.8       2   2 4.1       3   3 6 5.4 5.5 1.10 4   4 6.5 5.6 5.7 1.13 5 2018 1 5.8 5.9 6.0 0.97 6   2 5.2 6.1 6.2 0.84 7   3 6.8 6.3 6.3 1.08 8   4 7.4 6.4 6.4 1.16 9 2019 1 6 6.5 6.5 0.92 10   2 5.6 6.6 6.7 0.84 11   3 7.5 6.7 6.8 1.11 12   4 7.8 6.8 6.8 1.14 13 2020 1 6.3 6.9 6.9 0.91 14   2 5.9 7.0 7.1 0.83 15   3 8 7.2     16   4 8.4       Yt = S t x I t x T t Y t is orders per quarter So in order to calculate S t , I t is a Y t /CMA i.e S t , I t = Y t /CMA

24. Step 6: Let’s Get Rid of Irregularity and Deseasonalize the Data T year Quarter Number of orders per 1000 MA(4) CMA(4) S t , I t S t 1 2017 1 4.8       0.93 2   2 4.1       0.84 3   3 6 5.4 5.5 1.10 1.09 4   4 6.5 5.6 5.7 1.13 1.14 5 2018 1 5.8 5.9 6.0 0.97 0.93 6   2 5.2 6.1 6.2 0.84 0.84 7   3 6.8 6.3 6.3 1.08 1.09 8   4 7.4 6.4 6.4 1.16 1.14 9 2019 1 6 6.5 6.5 0.92 0.93 10   2 5.6 6.6 6.7 0.84 0.84 11   3 7.5 6.7 6.8 1.11 1.09 12   4 7.8 6.8 6.8 1.14 1.14 13 2020 1 6.3 6.9 6.9 0.91 0.93 14   2 5.9 7.0 7.1 0.83 0.84 15   3 8 7.2     1.09 16   4 8.4       1.14 T year Quarter Number of orders per 1000 MA(4) CMA(4) S t , I t S t Deseasonalise 1 2017 1 4.8       0.93 5.1 2   2 4.1       0.84 4.9 3   3 6 5.4 5.5 1.10 1.09 5.5 4   4 6.5 5.6 5.7 1.13 1.14 5.7 5 2018 1 5.8 5.9 6.0 0.97 0.93 6.2 6   2 5.2 6.1 6.2 0.84 0.84 6.2 7   3 6.8 6.3 6.3 1.08 1.09 6.2 8   4 7.4 6.4 6.4 1.16 1.14 6.5 9 2019 1 6 6.5 6.5 0.92 0.93 6.4 10   2 5.6 6.6 6.7 0.84 0.84 6.7 11   3 7.5 6.7 6.8 1.11 1.09 6.9 12   4 7.8 6.8 6.8 1.14 1.14 6.8 13 2020 1 6.3 6.9 6.9 0.91 0.93 6.8 14   2 5.9 7.0 7.1 0.83 0.84 7.0 15   3 8 7.2     1.09 7.3 16   4 8.4       1.14 7.3

25. Step 7: Find Out Trend Component   Coefficients Intercept 5.09961 t 0.147139 Short Summary of Simple linear regression T year Quarter Number of orders per 1000 MA(4) CMA(4) S t , I t S t Deseasonalise T t 1 2017 1 4.8       0.93 5.1 5.25 2   2 4.1       0.84 4.9 5.39 3   3 6 5.4 5.5 1.10 1.09 5.5 5.54 4   4 6.5 5.6 5.7 1.13 1.14 5.7 5.69 5 2018 1 5.8 5.9 6.0 0.97 0.93 6.2 5.84 6   2 5.2 6.1 6.2 0.84 0.84 6.2 5.98 7   3 6.8 6.3 6.3 1.08 1.09 6.2 6.13 8   4 7.4 6.4 6.4 1.16 1.14 6.5 6.28 9 2019 1 6 6.5 6.5 0.92 0.93 6.4 6.42 10   2 5.6 6.6 6.7 0.84 0.84 6.7 6.57 11   3 7.5 6.7 6.8 1.11 1.09 6.9 6.72 12   4 7.8 6.8 6.8 1.14 1.14 6.8 6.87 13 2020 1 6.3 6.9 6.9 0.91 0.93 6.8 7.01 14   2 5.9 7.0 7.1 0.83 0.84 7.0 7.16 15   3 8 7.2     1.09 7.3 7.31 16   4 8.4       1.14 7.3 7.45 Do the Simple Linear Regression of Deseanonalize Data for Finding the Trend Component

26. Step 8: Forecasting the Time Series Data T year Quarter Number of orders per 1000 MA(4) CMA(4) S t , I t S t Deseasonalise T t Forecast 1 2017 1 4.8       0.93 5.1 5.25 4.89 2   2 4.1       0.84 4.9 5.39 4.52 3   3 6 5.4 5.5 1.10 1.09 5.5 5.54 6.06 4   4 6.5 5.6 5.7 1.13 1.14 5.7 5.69 6.50 5 2018 1 5.8 5.9 6.0 0.97 0.93 6.2 5.84 5.44 6   2 5.2 6.1 6.2 0.84 0.84 6.2 5.98 5.01 7   3 6.8 6.3 6.3 1.08 1.09 6.2 6.13 6.70 8   4 7.4 6.4 6.4 1.16 1.14 6.5 6.28 7.18 9 2019 1 6 6.5 6.5 0.92 0.93 6.4 6.42 5.99 10   2 5.6 6.6 6.7 0.84 0.84 6.7 6.57 5.50 11   3 7.5 6.7 6.8 1.11 1.09 6.9 6.72 7.35 12   4 7.8 6.8 6.8 1.14 1.14 6.8 6.87 7.85 13 2020 1 6.3 6.9 6.9 0.91 0.93 6.8 7.01 6.54 14   2 5.9 7.0 7.1 0.83 0.84 7.0 7.16 6.00 15   3 8 7.2     1.09 7.3 7.31 7.99 16   4 8.4       1.14 7.3 7.45 8.52 17 2021 1       0.93     7.60 7.09 18   2       0.84     7.75 6.49 19   3       1.09     7.90 8.63 20   4       1.14     8.04 9.19

27. Result So the Final Forecasted Data is given below: Year Quarter Forecast(Per 1000 Orders) 2021 1 7.09   2 6.49   3 8.63   4 9.19

28. Thank You