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Measurement of Instability In Planning Systems

Explore the concept of nervousness in MRP systems, uncover its causes, effects, and the need for research on instability measurement. Discover strategies to dampen nervousness and maintain a stable production plan.

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Measurement of Instability In Planning Systems

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  1. Measurement of Instability In Planning Systems Arslan ÖRNEK K. Erkan KABAK Department of Industrial Engineering Dokuz Eylul University erkan_kabak@yahoo.com

  2. OUTLINE • INTRODUCTION • NERVOUSNESS • MAJOR CAUSES OF NERVOUSNESS • UNDESIRABLE EFFECTS OF NERVOUSNESS • STABILITY, FLEXIBILITY, ROBUSTNESS • THE NEED FOR A RESEARCH ON NERVOUSNESS • MEASUREMENT OF INSTABILITY • INSTABILITY MEASURES • IN MRP ENVIRONMENT • A NUMERICAL EXAMPLE TO MEASURE INSTABILITY • IN SUPPLY CHAIN ENVIRONMENT

  3. OUTLINE (Cont’d) • PROPERTIES OF AN IDEAL METRIC MEASURING INSTABILITY • TYPES OF CHANGES LIKELY TO OCCUR IN AN MRP ENVIRONMENT • A NEW METRIC FOR MEASURINGINSTABILITY IN MULTI-LEVEL MRP SYSTEMS • FACTORS AFFECTING INSTABILITY • STRATEGIES TO DAMPEN THE MRP NERVOUSNESS • CONCLUDING REMARKS • REFERENCES

  4. INTRODUCTION • Nervousness The MRP systems could have some problems with nervoussness,which is often defined as, • Significant changes in production plans, which occur even with minor changes in higher-level MRP records (or MPS), (Vollmann et al. 2004), • However, in literature different terms such as schedule instability or stability terms are used interchangeably to imply thenervousness in MRP plans.

  5. INTRODUCTION • Major Causes of Nervousness • Major causes of nervousness in schedules are: • Uncertainties about supply and/or demand, • Dynamic lot-sizing, • Rolling planning. (Steele 1975, Whybark and Williams 1976, Mather 1985, Kadipasaoglu and Sridharan 1997)

  6. INTRODUCTION • A snapshot of causes of instability ( Rejected Material 3% Supplier Allocation Changes 11% Gain & Losses 22% Material Transfers 5% Over/Under Production 1% Over/Under Shipments 19% Scrap 19% Service Requirements 1% Unscheduled disbursements 1% Engineering Changes 19% Figure 1: A snapshot of causes of instability (Inman and Gonsalvez,1997).

  7. INTRODUCTION • Undesirable effects of Nervousness (incoming) • Some of the undesirable effects of nervousness in the company are: • Increased production and inventory costs, • Increases in throughput times • Reduced productivity, • Decrease in capacity utilization, • Lower customer service levels, • Confusion on the shopfloor, • Loss of confidence to planning system, • Low morale, etc.

  8. INTRODUCTION • Undesirable effects of Nervousness (outgoing) • From the supply chain point of view, instability forces suppliers to react to unexpectedly changing requirements, thus increasing cost through increased: • overtime, • undertime, • Inventory, • premium freight, • changeovers, • material handling, • record keeping, • disruptions in quality, • throughput improvement efforts and, • managerial intervention (Inman and Gonsalvez,1997).

  9. INTRODUCTION • Stability • A stable schedule is one that does not often change with time as additional requirements data are added to the planning horizon (Sridharan et al. 1988), • Actually, stability implies a given production plan will be followed, whereas instability means not following the plan, • Besides stability, there are other stability-related concepts to evaluate the sensivity of planning methods e.g., flexibility and robustness.

  10. INTRODUCTION • Flexibility • Generally, the ability of a system to respond to unexpected situations is defined as “flexibility” and it comes out as an important competitiveness factor for companies (Heisig,2002). • Robustness • Heisig (2002) defines robustness as the invariability of initial decisions, or the persistence of complete decision sequence. However, from the planning point of view, it is understood as the insensitivity of the planning systems when the parameters change in stochastic input data.

  11. INTRODUCTION • Stability is a matter of balancing flexibility and robustness • If the MPS is stable, stable production plans are obtained, and in consequence stable production, • If too few changes are made, this will result in bad customer relations and increased inventory becaused of poor flexibility, • On the contrary, if too many changes are made, this will result in a reduced productivity,

  12. INTRODUCTION • Measurement of instability • Measurement of instability would enable performance comparison of different planning procedures for managing the production plans under a variety of operating conditions, • In constructing a measure for schedule stability, it is easier to measure schedule instability. The frequency and magnitude of changes in orders over planning horizon could provide a measure of schedule instability.

  13. INTRODUCTION • Need for a Research on Nervousness • Maintaining a stable production plan is an important competitive factor in material requirements planning system, • The American Production and Inventory Control Society (APICS) has listed “improved MRP” systems as one of the top 10 topics of concern to its over 80,000 members in 2000.

  14. INTRODUCTION • Need for a Research on Nervousness • The size of the market for MRP-based production planning and control software is over US $ billions, • With regard to the supply chain management (SCM), Heisig (2002) states that one of the most important enablers for efficient supply chain operations is schedule stability. Moreover, in a Just-in Time (JIT) environment, it causes significant problems for suppliers, • These facts justify the need for additional research for improving the performance of MRP systems,

  15. Measurement of Instability “Before stability can be improved, it must be measured” (Inman and Gonsalvez, 1997) “You can not control what you do not measure” (Mather, 1994) “As with any management endeavour, a prequisite to success is measurement” (Johnson and Davis,1995)

  16. INSTABILITY MEASURES • In MRP environment: • BKM metric (Blackburn, Kropp, Millen, 1986), • SBU metric (Sridharan, Berry, Udayabhanu, 1988), • Unahabhokha, Schuster, Allen, (2002), • KS metric (Kadipasaoglu and Sridharan, 1996), • Kimms (1998), • De Kok and Inderfurth (1997), • Heisig (2002), • Pujawan (2004),

  17. INSTABILITY MEASURES • In supply chain environment: • Inmann and Gonsalvez (1997), • Van Donselaar, K., Van den Nieuwenhof, J., and Visschers, J.,(2000) and, • Meixell (2003)

  18. BKM Metric • It is proposed by Blackburn, Kropp, ve Millen (1986), • Instability is defined as the number of times an unplanned order is made in the first period when the schedule is rolled forward, • Shortcomings of BKM metric: • It fails to assess changes in open orders within the cumulative lead time. Thus, considering only the immediate period schedule changes is not adequate, • It depends on enumeration and as the planning horizon length increases, it quickly approaches to the desired value of zero, i.e., as the horizon length is increased, instability artificially is reduced, • It is assumed that demand is deterministic and lead times are zero, therefore the need for open order rescheduling is questionable.

  19. SBUMetric • It is proposed by Sridharan, Berry ve Udayabhanu (1988), • Instability is defined as the weighted average of schedule changes in order quantity per order over subsequent planning cycles , • Differences between SBU and BKM metric: • SBU metric applies a weighting procedure to schedule changes, whereas BKM metric does not, • Decreasing weights are used to represent the increased ability to respond increasing uncertainty of demands, • Weights can be varied, for example a small value for the weighting parameter α can be placed for rapidly decreasing weights, whereas a larger value for α can be placed for nearly equal weights so that the instability measure could be adjusted to reflect the importance of the immediate future.

  20. SBU Metric t = time period, = scheduled order quantity for period t during planning cycle k, = beginning of planning cycle k, N = planning horizon length, S = total number of orders over all planning cycles, and = a weight parameter to represent the criticality of changes in schedule; 0<α<1

  21. SBU Metric • Furthermore, they state the instability measure in terms of the proportion of an average MPS order that is changed (A). As approximating for this average they use the wellknown Economic Order Quantity (EOQ) formula. The modified instability measure is then given as • This standardized measure is based on the assumption that the average order cycle, or natural cycle (T), is equal to the ratio of the EOQ and average or expected period requirements (R), respectively, i.e.

  22. SBU Metric • Shortcomings of SBU metric • The single-level product structure, i.e., this measure of instability is intended for single-item, single-level situations, but the components at lower levels must also be considered.However, Zhao and Lee (1993) used this metric for multi-items, • It isbiased. Because this metric divides total instability by the total number of orders, measures instability as “weighted instability per order”.Also, number of orders depends on the cost structure of the item, and on the ratio between the setup cost and the holding cost (S/h),

  23. SBU Metric • Shortcomings of SBU metric • The instability would be less if the S/h ratio is smaller and the it would be greater if the S/h ratio is larger. Because when the ratio S/h is small the total number of orders is greater, and the less instability, • The instability measure is not normalized between values of 0 and 1, i.e., maximum stability and maximum instability.

  24. Unahabhokha et al. (2002) • Unahabhokha, Schuster and Allen (2002), use a version of the SBU metric presented by Sridharan et al. (1988), • In this measure, weight function is changed by weighting factor and instability is measured for multi items, i = item number,i= 1,….,n t = time period, weeks, =scheduled production for item i, period t during planning cycle 2, H = planning horizon length, H=1…52, k= multiple factor (=100), Wt= weighting factor, Exp(1/t)-1, B = total production for planning cycle 2,

  25. KS Metric • Kadipasaoglu and Sridharan (1996) have extended the previous instability measure SBU metric and eliminated some shortcomings of it by adding a weight parameter β to assign decreasing weights to the levels, j = item level, j = 0, ..., m (based on low level coding), i = item i at level j , i = 1, ...nj, = the order quantity (open and/or planned) for item i at level j in period t during planning cycle k, =weight parameter for all levels; 0< β<1.

  26. KS Metric • This metric defines instability as the weighted change in order quantities for all items at all levels through the subsequent planning cycles, • Shortcomings of KS metric: • It is still not standardized between a minimum and maximum value of nervousness, • In addition, different kinds of change are not still differentiated.For example, the weights for the setup and quantity changes are the same.

  27. Kimms (1998) • He considers a T-period problem on the MPS level which is rolled n>0 times, • As different from other measures, he adds a frozen zone,ΔT. The plan for the first ΔTperiods is implemented and a new plan is then generated for the periods ΔT+1,…, ΔT+T which coins the name rolling horizon, • For two subsequent planning cycles i and i-1, instability is the weighted difference between the production plans for periods t+(i-1)ΔT and t+iΔT,

  28. Kimms (1998) Instability for an item j, can be defined as the maximum instability: or the mean instability:

  29. Kimms (1998) i= number of run, =weighted production quantities for item j,temporarily scheduled ,i =1,…,n-1, = weighted production quantities for item j,after rescheduling in overlapping periods, i =2,…,n-1, = production quantity for item j in period t, = item-specific weights for item j in period t ,

  30. Kimms (1998) Some possibilities of the instability measure for the overall production plan: • the maximum of the maximum instabilities: • the mean of the maximum instabilities: • the maximum of the mean instabilities: • the mean of the mean instabilities:

  31. Kimms (1998) • Relations these four performance measures are given in the following two inequalities: (1) (2)

  32. Kimms (1998) • Shortcomings: • Kimms gives a different weight parameter, it is item independent but this weight can not be varied as Sridharan’s parameter α does, • Only the end item level is considered, but the components at lower levels also must be considered since their schedules can significantly impact both material and capacity plans, • From the standardization point, although the differences in quantities are divided by a maximum in the equation, Kimms’ measure can take on values greater than 1.

  33. De Kok and Inderfurth (1997) • They separate the short-term planning stability with long-term stability, • Furthermore, stability examined on the aspect of pure qualitative changes or quantitative changes of order decisions,

  34. De Kok and Inderfurth (1997) Setup stability; Quantity stability; = the expected value, = stationary inventory control rule, = cumulative demand function with a stochastic iid demand D. For For

  35. De Kok and Inderfurth (1997) • It is concluded that the reorder point s does not influence stability whereas the lot size determining parameters Q, S-s, and T can have a considerable impact. • Shortcomings: • The stability measures are restricted to planned order deviations only referring to the most imminent period in each planning cycle, i.e., they describe short-term stability, • Stability measures are not for multi items and do not take into account the impact of the product structure.

  36. Heisig (2002) • Heisig (2002), proposes the modified setup and quantity instabilities, Setup oriented instability: = replenishment order size in period t as planned in cycle j, N = number of planning cycles,and α = a weight parameter to represent the criticality of plan revisions; 0<α<1

  37. Heisig (2002) Quantity oriented instability: = maximum quantity that can be changed between two successive planning cycles, = maximum reasonable demand per period,

  38. Pujawan (2004) • Pujawan (2004) presents a case study of schedule instability based on a field observations and a model to quantify the instability, • In his model, there are three types of changes that could happen in the day-to-day production schedules: (1) Changes in the production start time, (2) Changes in the specification/design of items being ordered and, (3) Changes in the quantity committed to be delivered.

  39. Pujawan (2004) (1) (2) i = type of changes (i.e., 1: production start time; 2: specification/design; 3: quantity), j = period in the planning horizon, t = planning cycle, k = order, wi= weight of typeichange, = the quantity of orderkwhich in the previous cycle was scheduled to be produced in periodjbut then experiencing typeichange when observed in planning cyclet, = aggregated instability observed in planning cycle t from all orders where in the previous planning cycle they were scheduled in period j, = total instability observed in planning cycle t,

  40. Pujawan (2004) • In determining the weights, the managers were asked to do pair-wise comparison for the three types of changes. The results then converted into weights using the logic of analytical hierarchy process (AHP). The final weights are obtained as follows: w1= 0.10, w2= 0.62 and w3= 0.28, • When a change is involved in a more than one type, the one with the larger weight is considered.

  41. Pujawan (2004) Figure 2: Initial production schedule (for week 39) Figure 3: Updated production schedule (for week 40) From figures 2 and 3, aggregated instability observed in planning cycle of week for all orders previously scheduled in weeks 40 to 43 as follows: I(40,40) = 0.28*200 =56, I(40,41) = 0.1*430 =43, I(40,42) = 0 + 0.1*500 =50, I(40,43) = 0 I(40) = 56 + 43 + 56 =149

  42. A Item Master Bill Of Material B 2C P-NO LT ST SS LLC LSR LS Parent Comp. Q-P A 3 0 0 0 POQ 4 A B 1 B 3 0 0 1 LFL 1 A C 2 C 3 0 0 1 LFL 1 P-NO OH 1 2 3 4 5 6 7 8 9 10 11 12 A 163 341 B 27 304 C 54 608 A Numerical Example To Measure Instability In MRP Environment • To test the metrics, an example providedby Kadipasaoglu et al. (1997), is considered, • The demand forecast is generated from normal distribution with a mean of 100 units andstandard deviation of 20 units. Table 1:Item Master and Bill of Material (BOM) data. Figure 4: Product structure Table 2:On-hand andScheduled Receipts data for the first planning cycle.

  43. Table 3(a): MRP records in the beginning of the first and second planning cycles (actual demand in period 1 is 31 units more than anticipated). Planning cycle 1 Planning cycle 2

  44. Table 3(b): MRP records in the beginning of the second and third planning cycles (actual demand in period 2 is 79 units more than anticipated). Planning cycle 2 Planning cycle 3

  45. Table 3(c): MRP records in the beginning of the third and fourth planning cycles (actual demand in period 3 is 51 units less than anticipated). Planning cycle 3 Planning cycle 4

  46. Number of total orders* 5 Number of new orders 5 Number of cancelled orders 4 Number of open orders** 2 Number of open orders scheduled in the first cycle 1 Number of open orders expedited 1 Number of open orders postponed Number of planned orders*** 3 Number of planned orders released in the first period of the cycle 1 Number of planned orders in the last cycle 2 A Numerical Example to Measure Instability Table 4(a):Planned and open orders for item A inall cycles. Table 4(b): Summary of orders for item A inall cycles.

  47. Number of total orders 6 Number of new orders 4 Number of cancelled orders 3 Number of open orders 3 Number of open orders scheduled in the first cycle 1 Number of open orders expedited 2 Number of open orders postponed 1 Number of planned orders 3 Number of planned orders released in the first period of the cycle 2 Number of planned orders in the last cycle 1 A Numerical Example to Measure Instability Table 5(a):Planned and open orders for item Binall cycles. Table 5(b): Summary of orders for item Binall cycles.

  48. Number of total orders 6 Number of new orders 4 Number of cancelled orders 3 Number of open orders 3 Number of open orders scheduled in the first cycle 1 Number of open orders expedited 2 Number of open orders postponed 1 Number of planned orders 3 Number of planned orders released in the first period of the cycle 2 Number of planned orders in the last cycle 1 A Numerical Example to Measure Instability Table 6(a):Planned and open orders for item Cinall cycles. Table 6(b): Summary of orders for item Cinall cycles.

  49. Number of total Orders 17 Number of total new orders 13 Number of cancelled orders 10 Number of total Openorders 8 Number of total open orders scheduled in the first cycle 3 Number of open orders expedited 5 Number of open orders postponed 3 Number of total released planned orders 9 Number of planned orders released in the first period of thecycle 5 Number of planned orders in the last cycle 4 A Numerical Example to Measure Instability Table 7:Summary of total orders for all items in four subsequent planning cycles

  50. Measurement of instability with the BKM metric • In regard to BKM metric, instability is measured by counting the number of times new orders are scheduled in an imminent period of the planning cycle, • In the example, the BKM value for instability would be three, due to an unplanned order for item A in period 2 during planning cycle 2 and the expedited orders for items B and C in period 3 during planning cycle 3.

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