# 12.4.2 Comparison of Four Different Batch-Sizing Policies, Namely Dynamic Optimization, Part Period Balancing (PPB), the Period Order Quantity (or Optimum Length of Order Cycle), and the Economic Order Quantity (EOQ)

### Intended learning outcomes: Explain, using an example, the comparison of various batch-sizing policies, namely dynamic optimization, part period balancing (PPB), the period order quantity (or optimum length of order cycle), and the optimum batch size (economic order quantity, EOQ).

Batch-sizing policies 7, 6, 5, and 4 described in Section 12.4.1 are compared below. These policies are

• Dynamic optimization
• Part period balancing (PPB)
• The optimum length of order cycle or the optimum order interval
• The optimum batch size (economic order quantity, EOQ)

The following assumptions apply:

• Net requirement: 300 units of measure divided between six periods (for example, 2-month periods) giving 10, 20, 110, 50, 70, 40 units
• Batch-size-independent production or procurement costs: 100 cost units
• Carrying cost
• Per unit of measure and period: 0.5 cost units
• Per unit of measure over six periods: 3 cost units
• An order receipt is assumed at the start of a period. Carrying cost is always incurred at the start of the next period.

Based on these assumptions, you can thus calculate the following values:

• Optimum batch size using the economic order quantity (EOQ) (see Figure 11.4.2.4):
• Optimum length of order cycle or the optimum order interval (see Figure 11.4.2.6):

In Figure 12.4.2.1, the total setup and ordering costs as well as the carrying cost are calculated for the various batch-sizing policies.

Fig. 12.4.2.1          Comparison of various batch-sizing policies.

Every policy yields a different result in specific cases, although this is not necessarily so in the general case. The results obtained with these techniques tend to improve in the order given above. Indeed, the optimum batch-size technique can be used only if the quantity of the last batch does not exceed the net requirement. But, even under these circumstances, the technique produces unsatisfactory results when applied deterministically.

## Course section 12.4: Subsections and their intended learning outcomes

• ##### 12.4 Batch Sizing, or Lot Sizing

Intended learning outcomes: Explain combining net requirements into batches. Differentiate between different batch-sizing policies.

• ##### 12.4.1 Combining Net Requirements into Batches by Policies such as Lot-for-lot, Economic order quantity (EOQ), the Period Order Quantity (or Optimum Length of Order Cycle), Part Period Balancing (PPB), Dynamic Optimization

Intended learning outcomes: Disclose various batch sizing policies, namely lot-for-lot, optimum number of (merged) demands, optimum number of partial lots, optimum batch size (economic order quantity, EOQ), the period order quantity (or optimum length of order cycle), part period balancing (PPB), dynamic optimization. Explain the “blowthrough” technique linked with the lot-for-lot sizing policy.

• ##### 12.4.2 Comparison of Four Different Batch-Sizing Policies, Namely Dynamic Optimization, Part Period Balancing (PPB), the Period Order Quantity (or Optimum Length of Order Cycle), and the Economic Order Quantity (EOQ)

Intended learning outcomes: Explain, using an example, the comparison of various batch-sizing policies, namely dynamic optimization, part period balancing (PPB), the period order quantity (or optimum length of order cycle), and the optimum batch size (economic order quantity, EOQ).