302 Process problem solving and improvement ᭹ Steps in constructing a cause and effect diagram include identifying the effect, establishing the goals, constructing a framework, recording all suggested causes, incubating the ideas prior to a more structured analysis leading to plans for action. ᭹ A variation on the technique is the cause and effect diagram with addition of cards (CEDAC). Here the effect side of the diagram is quantified, with an improvement target, and the causes show facts and improvement ideas. ᭹ Scatter diagrams are simple tools used to show the relationship between two factors – the independent (controlling) and the dependent (con- trolled). Choice of the factors and appropriate data recording are vital steps in their use. ᭹ Stratification is a sample selection method used when populations are comprised of different characteristics. It involves collecting or dividing data into meaningful groups. It may be used in conjunction with other techniques to present differences between such groups. ᭹ The principles and techniques of problem solving and improvement may be applied to any human activity regarded as a process. Where barriers to the use of these, perhaps in non-manufacturing areas, are found, training in the basic approach of process data collection, analysis and improve- ment action may be required. References Ishikawa, K. (1986) Guide to Quality Control, Asian Productivity Association, Tokyo, Japan. Lockyer, K.G., Muhlemann, A.P. and Oakland, J.S. (1992) Production and Operations Management, 6th Edn, Pitman, London, UK. Oakland, J.S. (2000) Total Quality Management – text and cases, 2nd Edn, Butterworth- Heinemann, Oxford, UK. Pyzdek, T. (1990) Pyzdek’s Guide to SPC – Vol. One – Fundamentals, ASQC Quality Press, Milwaukee WI, USA. Sygiyama, T. (1989) The Improvement Book – creating the problem-free workplace, Productivity Press, Cambridge MA, USA. Process problem solving and improvement 303 Discussion questions 1 You are the Production Manager of a small engineering company and have just received the following memo: MEMORANDUM To: Production Manager From: Sales Manager Subject: Order Number 2937/AZ Joe Brown worked hard to get this order for us to manufacture 10 000 widgets for PQR Ltd. He now tells me that they are about to return the first batch of 1000 because many will not fit into the valve assembly that they tell us they are intended for. I must insist that you give rectification of this faulty batch number one priority, and that you make sure that this does not recur. As you know PQR Ltd are a new customer, and they could put a lot of work our way. Incidentally I have heard that you have been sending a number of your operators on a training course in the use of the microbang widget gauge for use with that new machine of yours. I cannot help thinking that you should have spent the money on employing more finished product inspectors, rather than on training courses and high technology testing equipment. (a) Outline how you intend to investigate the causes of the ‘faulty’ widgets. (b) Discuss the final paragraph in the memo. 2 You have inherited, unexpectedly, a small engineering business which is both profitable and enjoys a full order book. You wish to be personally involved in this activity where the only area of immediate concern is the high levels of scrap and rework – costing together a sum equivalent to about 15 per cent of the company’s total sales. Discuss your method of progressively picking up, analysing and solving this problem over a target period of 12 months. Illustrate any of the techniques you discuss. 3 Discuss in detail the applications of Pareto analysis and cause and effect analysis as aids in solving operations management problems. Give at least two illustrations. You are responsible for a biscuit production plant, and are concerned about the output from the lines which make chocolate wholemeal biscuits. Output is consistently significantly below target. You suspect that this is because the lines are frequently stopped, so you initiate an in-depth investigation over a typical two-week period. The table below shows the causes of the stoppages, number of occasions on which each occurred, and the average amount of output lost on each occasion. 304 Process problem solving and improvement Cause No. of occurrences Lost production (00s biscuits) WRAPPING: cellophane wrap breakage 1031 3 cartonner failure 85 100 ENROBER: chocolate too thin 102 1 chocolate too thick 92 3 PREPARATION: underweight biscuits 70 25 overweight biscuits 21 25 biscuits mis-shapen 58 1 OVENS: biscuits overcooked 87 2 biscuits undercooked 513 1 Use this data and the appropriate techniques to indicate where to concentrate remedial action. How could stratification aid the analysis in this particular case? 4 A company manufactures a range of domestic electrical appliances. Particular concern is being expressed about the warranty claims on one particular product. The customer service department provides the following data relating the claims to the unit/component part of the product which caused the claim: Unit/component part Number of claims Average cost of warranty work (per claim) Drum 110 48.1 Casing 12 842 1.2 Work-top 142 2.7 Pump 246 8.9 Electric motor 798 48.9 Heater unit 621 15.6 Door lock mechanism 18 442 0.8 Stabilizer 692 2.9 Powder additive unit 7 562 1.2 Electric control unit 652 51.9 Switching mechanism 4 120 10.2 Process problem solving and improvement 305 Discuss what criteria are of importance in identifying those unit/component parts to examine initially. Carry out a full analysis of the data to identify such unit/component parts. 5 The principal causes of accidents, their percentage of occurrence, and the estimated resulting loss of production per annum in the UK is given in the table below: Accident cause Percentage of all accidents Estimated loss of production (£million/annum) Machinery 16 190 Transport 8 30 Falls from heights >6′ 16 100 Tripping 3 10 Striking against objects 9 7 Falling objects 7 20 Handling goods 27 310 Hand tools 7 65 Burns (including chemical) 5 15 Unspecified 2 3 (a) Using the appropriate data draw a Pareto curve and suggest how this may be used most effectively to tackle the problems of accident prevention. How could stratification help in the analysis? (b) Give three other uses of this type of analysis in non-manufacturing and explain briefly, in each case, how use of the technique aids improvement. 6 The manufacturer of domestic electrical appliances has been examining causes of warranty claims. Ten have been identified and the annual cost of warranty work resulting from these is as follows: Cause Annual cost of warranty work (£) A B C D E F G H I J 1 090 2 130 30 690 620 5 930 970 49 980 1 060 4 980 3 020 306 Process problem solving and improvement Carry out a Pareto analysis on the above data, and describe how the main causes could be investigated. 7 A mortgage company finds that some 18 per cent of application forms received from customers cannot be processed immediately, owing to the absence of some of the information. A sample of 500 incomplete application forms reveals the following data: Information missing Frequency Applicant’s Age 92 Daytime telephone number 22 Forenames 39 House owner/occupier 6 Home telephone number 1 Income 50 Signature 6 Occupation 15 Bank Account no. 1 Nature of account 10 Postal code 6 Sorting code 85 Credit Limit requested 21 Cards existing 5 Date of application 3 Preferred method of payment 42 Others 46 Determine the major causes of missing information, and suggest appro- priate techniques to use in form redesign to reduce the incidence of missing information. 8 A company which operates with a four-week accounting period is experiencing difficulties in keeping up with the preparation and issue of sales invoices during the last week of the accounting period. Data collected over two accounting periods are as follows: Accounting Period 4 Week 1 2 3 4 Number of sales invoices issued 110 272 241 495 Accounting Period 5 Week 1 2 3 4 Number of sales invoices issued 232 207 315 270 Examine any correlation between the week within the period and the demands placed on the invoice department. How would you initiate action to improve this situation? Process problem solving and improvement 307 Worked examples 1 Reactor Mooney off-spec results A project team looking at improving reactor Mooney control (a measure of viscosity) made a study over 14 production dates of results falling ±5 ML Points outside the grade aim. Details of the causes were listed (Table 11.6). Table 11.6 Reactor Mooney off-spec results over 14 production days Sample Cause Sample Cause 1 Cat. poison 41 Instrument/analyser 2 Cat. poison 42 H.C.L. control 3 Reactor stick 43 H.C.L. control 4 Cat. poison 44 Feed poison 5 Reactor stick 45 Feed poison 6 Cat. poison 46 Feed poison 7 H.C.L. control 47 Feed poison 8 H.C.L. control 48 Reactor stick 9 H.C.L. control 49 Reactor stick 10 H.C.L. control 50 H.C.L. control 11 Reactor stick 51 H.C.L. control 12 Reactor stick 52 H.C.L. control 13 Feed poison 53 H.C.L. control 14 Feed poison 54 Reactor stick 15 Reactor stick 55 Reactor stick 16 Reactor stick 56 Feed poison 17 Reactor stick 57 Feed poison 18 Reactor stick 58 Feed poison 19 H.C.L. control 59 Feed poison 20 H.C.L. control 60 Refridge problems 21 Dirty reactor 61 Reactor stick 22 Dirty reactor 62 Reactor stick 23 Dirty reactor 63 Reactor stick 24 Reactor stick 64 Reactor stick 25 Reactor stick 65 Lab result 26 Over correction F.109 66 H.C.L. control 27 Reactor stick 67 H.C.L. control 28 Reactor stick 68 H.C.L. control 29 Instrument/analyser 69 H.C.L. control 30 H.C.L. control 70 H.C.L. control 31 H.C.L. control 71 Reactor stick 32 H.C.L. control 72 Reactor stick 33 H.C.L. control 73 Reactor stick 34 H.C.L. control 74 Reactor stick 35 Reactor stick 75 B. No. control 36 Reactor stick 76 B. No. control 37 Reactor stick 77 H.C.L. control 38 Reactor stick 78 H.C.L. control 39 Reactor stick 79 Reactor stick 40 Reactor stick 80 Reactor stick Table 11.7 Reactor Mooney off-spec results over 14 production dates: Pareto analysis of reasons Reasons for Mooney off-spec Tally Frequency % of total Cum % Reactor sticks | | | | | | | | | | | | | | | | | | | | | | | | | | 32 40 40 H.C.L. control | | | | | | | | | | | | | | | | | | | | 24 30 70 Feed poisons | | | | | | | | 10 12.5 82.5 Cat. poisons | | | | 4 5 87.5 Dirty stick reactor | | | 3 3.75 91.25 B. No. control | | 2 2.5 93.75 Instruments/analysers | | 2 2.5 96.25 Over-correction F.109 | 1 1.25 97.5 Refridge problems | 1 1.25 98.75 Lab results | 1 1.25 100 Process problem solving and improvement 309 Using a ranking method – Pareto analysis – the team were able to determine the major areas on which to concentrate their efforts. Steps in the analysis were as follows: 1 Collect data over 14 production days and tabulate (Table 11.6). 2 Calculate the totals of each cause and determine the order of frequency (i.e. which cause occurs most often). 3 Draw up a table in order of frequency of occurrence (Table 11.7). 4 Calculate the percentage of the total off-spec that each cause is responsible for. e.g. Percentage due to reactor sticks = 32 80 ϫ 100 = 40 per cent. Figure 11.14 Pareto analysis: reasons for off-spec reactor Mooney 310 Process problem solving and improvement Figure 11.15 Income rank/contribution rank chart 5 Cumulate the frequency percentages. 6 Plot a Pareto graph showing the percentage due to each cause and the cumulative percentage frequency of the causes from Table 11.7 (Figure 11.14). 2 Ranking in managing product range Some figures were produced by a small chemical company concerning the company’s products, their total volume ($), and direct costs. These are given in Table 11.8. The products were ranked in order of income and contribution for the purpose of Pareto analysis, and the results are given in Table 11.9. To consider either income or contribution in the absence of the other could lead to incorrect conclusions; for example, Product 013 which is ranked 9th in income actually makes zero contribution. One way of handling this type of ranked data is to plot an income– contribution rank chart. In this the abscissae are the income ranks, and the Process problem solving and improvement 311 Table 11.9 Income rank/contribution rank table Code number Description Income rank Contribution rank 001 Captine 20 20 002 BHD-DDB 10 10 003 DDB-Sulphur 11 8 004 Nicotine-Phos 5 4 005 Fensome 17 17 006 Aldrone 3 2 007 DDB 19 18 008 Dimox 18 16 009 DNT 1 1 010 Parathone 2 7 011 HETB 15 15 012 Mepofox 14 11 013 Derros-Pyrethene 9 19 014 Dinosab 7 3 015 Maleic Hydrazone 16 12 016 Thirene-BHD 4 5 017 Dinosin 6 9 018 2,4-P 8 6 019 Phosphone 13 13 020 Chloropicrene 12 14 Table 11.8 Some products and their total volume, direct costs and contribution Code number Description Total volume ($) Total direct costs ($) Total contribution ($) 001 Captine 1 040 1 066 26 002 BHD-DDB 16 240 5 075 11 165 003 DDB-Sulphur 16 000 224 15 776 004 Nicotine-Phos 42500 19 550 22 950 005 Fensome 8 800 4 800 4 000 006 Aldrone 106821 45 642 61179 007 DDB 2600 1 456 1 144 008 Dimox 6 400 904 5 496 009 DNT 288 900 123 264 165 636 010 Parathone 113 400 95 410 17 990 011 HETB 11 700 6 200 5 500 012 Mepofox 12 000 2 580 9 420 013 Derros-Pyrethene 20 800 20 800 0 014 Dinosab 37 500 9 500 28 000 015 Maleic Hydrazone 11 300 2 486 8 814 016 Thirene-BHD 63,945 44 406 19 539 017 Dinosin 38 800 25 463 13 337 018 2,4-P 23 650 4 300 19 350 019 Phosphone 13 467 6 030 7 437 020 Chloropicrene 14400 7 200 7 200 . H.C.L. control 49 Reactor stick 10 H.C.L. control 50 H.C.L. control 11 Reactor stick 51 H.C.L. control 12 Reactor stick 52 H.C.L. control 13 Feed poison 53 H.C.L. control 14 Feed poison 54 Reactor. 91. 25 B. No. control | | 2 2 .5 93. 75 Instruments/analysers | | 2 2 .5 96. 25 Over-correction F .109 | 1 1. 25 97 .5 Refridge problems | 1 1. 25 98. 75 Lab results | 1 1. 25 100 Process problem solving and. BHD-DDB 16 240 5 0 75 11 1 65 003 DDB-Sulphur 16 000 224 15 776 004 Nicotine-Phos 4 250 0 19 55 0 22 950 0 05 Fensome 8 800 4 800 4 000 006 Aldrone 106 821 45 642 61179 007 DDB 2600 1 456 1 144 008 Dimox