A statistical quality control method for determining whether to accept or reject a batch of products is acceptance sampling. It entails testing a set number of products from a batch to assure the products meet predetermined standards for compliance. There are many acceptance sampling plans, and these plans differ from the others by having unique and different number of acceptance criteria. The requirements placed on any sampling plan to determine whether a lot is accepted or rejected are known as acceptance criteria. Chain sampling plans (ChSP-1) have five acceptance criteria, which make them less strict for lot sentencing. On the other hand, modified chain sampling plans (MChSP-1) have three acceptance criteria, which make them stricter for lot sentencing. The problem arises when trying to balance the strictness of lot sentencing, as ChSP-1 with five acceptance criteria may be too lenient, while MChSP-1 with three acceptance criteria may be too stringent. The former gives rise to the consumer’s risk, whereas the latter increases the producer’s risk. Thus, this research proposes the construction of new chain sampling plans (NChSP-1) with four acceptance criteria. This places NChSP-1 between ChSP-1 and MChSP-1. The NChSP-1 offers stricter decisions than MChSP-1, but less strict than ChSP-1 and, is constructed by minimizing the consumer’s risk. According to the results, NChSP-1 has a smaller sample size than ChSP-1 for every design parameter. Apart from that, for most mean ratio values, the NChSP-1 lot acceptance probability is higher than the ChSP-1 lot acceptance probability. Consequently, the NChSP-1 presents a more statistically efficient and advantageous alternative to the ChSP-1 plan under a variety of quality conditions.