UNIT 12 JUST- IN- TIME (JIT)ObjectivesUpon completion of this unit, you will get to know:

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What is the philosophy of just-in-time(TIT) operation Characteristics of just-in-time system Pull method versus push method of operation Prerequisite for TIT manufacturingBenefits of TIT manufacturing Kanban system of manufacturing

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Structur e12. 1Introduction 12.1.1 Raw Material, WIP, Finished 12.2 Stock Points in a Production- Distribution Goods 12.3 Just-In-Time 12.4 Characteristics of Just-In-Time Systems 12.5 The Just-In-Time Manufacturing Philosophy 12.5.1 Prerequisite for JIT manufacturing 12.6 Elements of Manufacturing 12.6.1 Eliminating Waste 12.6.2 Enforced Problem Solving and Continuous Improvements 12.6.3 Benefits of JIT Manufacturing 12.7 TIT Purchasing 12.8 The Kanban System 12.9 TIT Implementation in Industries 12.10 Summary 12.11 Self-Assessment Exercises 12.12 Further Readings

12.1 INTRODUCTIONIn financial parlance, inventory is detined as the sum of the value of raw materials, fuels and lubricants, spare parts, maintenance consumables, semi-processed materials and finished goods stock at any given point of time. The operational definition of inventory would be: the amount of raw materials, fuel and lllbricants, spare parts and semi-processed material to be stocked for the smooth running of the plan-1 Since these resources are idle when kept in the stores, inventory is defined as an idle resource or any kind having an economic value. Inventories are maintained basically for the operational smoothness which they can effect by uncoupling successive stages of production, whereas the monetary value of inventory serves as a guide to indicate the size of the investment made to achieve this operational convenience. The materials management department is expected to provide this operational convenience with a minimum possible investment in inventories. The o~iectives of inventory, operational and financial, needless to say, are cont1icting. The materials department is accused of both stock outs as well as large investment in inventories. The solution lies in exercising a selective inventory control and application of inventory control techniques. Inventory control has been attracting the attention of managers in India for a long time.

12.1.1

Raw Material, WIP, Finished Goods

For control purposes, it is very essential to study the inventory in detail- raw materials, production components, work-in-progress and finished goods inventories should be segregated as the reasons for their existence and the causes frocess improvement which means variability reduction through enhancement of process capability. We will try to understand what process capability means.

TQM organizations that depend on outside suppliers often expect their suppliers to provide process capability data along with the parts that are supplied. Process capability quantifies the amount of variation inherent in the processes producing these parts thereby helping the customer organization to determine the quality of the parts supplied. If there is excessive variability in the process, it will have difficulty in meeting the specifications. Thus the process capability will be low and consequently there would be lot of defectives in the goods supplied. Similarly within the organization itself, process Capability data of the core processes are necessary upstream for the designers in determining the design specifications and tolerances. Designers should not specify tight tolerances if the products are to be manufactured in processes having excessive variability (i.e., low capability). On the other hand highly capable processes should not be underutilized (or wrongly utilized) by specifying loose tolerances. Figure 3.11 shows processes .having certain natural tolerances (or variations) and how they are performing in relation to the design tolerances (or specifications). Process A is a higWy capable one because it is well within the design limits. Process B, however, has excessive variability and hence is less capable of producing consistent quality products. For process B the natural Fig.J.ll: Design vel'!!US natural toler.l11ce tolerance is equal to the design tolerance. This can be translated into .27 defects in 100 parts that are manufactured. These are the defectives that lie outside the three sigma limits. Does not seem that bad. But what if we are producing a million parts? It translates into having 2700 defects per million. This is not an insignificant number ifthe loss per defect is very high. As some articles report, it could mean 20,000 wrong drug prescriptions a year, or 15000 babies accidentally dropped by nurses each year, or 500 incorrect surgical operations each week and so on. Hence having a design tolerance equal to the process tolerance is not a good idea when we consider the three sigma limits. Now if six sigma limits correspond with the design limits, then there is a lot of reduction in the variability of the process. Under such situations we have a highly capable process which, if centered on the mean, translates into having defectives of only 2 parts per billion. Motorola has made six sigma famous by focusing on product design and process capability improvements. Motorola's design limit of six sigma with a slight shift in the process achieved defectives up to 3.4 parts per million (ppm). Both the processes in Figure 3.11 are centered around the mean value. Thus, we can improve the process capability either by reducing variations in the process (thereby changing its natural limits) or by revising the design tolerances (thereby making changes to the design). To measure the quality of a process (and thereby its products) we need to know two things: (a) how capable is the process (measured by' Cp index) and (b) how well centered it is (measured by C k index). C determines what tile process is capable ppof based on the process standard deviation and the total design tolerance. ToleranceCp

= 6a

where, tolerance = upper specification limit - lower specification limit Cpk determines how well the process is centered by comparing the process mean with the nearest specification limit (NSL).

C = mean-NSL pk 3a

= 2. a six sigma company C p= C pk Figure 12 shows Process X having a shift in the process average. Even though it is as capable as Process Y. it has a greater chance of not meeting the USL. Both the processes are, however:, more capable than Process Z. If process Y has a standard deviation equal to . 7 units. and if the design tolerance is specified as 8 units. then the. process capability Cp =Note that when the process is centered, C = C ppk

. For

8 + 4.2 (3*.7) = . standard

=

1.9 and Cpk

1.9. However. for

=

4

+

process X (having same

deviation) the C value will

be lower than r. 9 and the C will be equal to 1.9. p

Fi&.3.12: Process Capability

design specifications are equally bad (see Figure 3.13). This is also known as the goalpost syndrome. However, according to Taguchi the target or the nominal specification is more important than the tolerance specifications (or the goal-posts). Hence, the farther away we are from the target value the greater is the loss in the.

Before discontinuing our discussion on specification limits and process performance, let us briefly look at a traditional approach. According to this approach all products meeting design specifications are equally good and those just missing the.~ Taguc-hi Loss FunctionAll products