Not long after its inception at Motorola in the mid 1980’s, Six Sigma’s practitioners began to realize that there is often a practical limit to how much existing products or processes can be improved. This limit is sometimes referred to as the “five sigma barrier” because it is in this region of process performance that the financial gain of further improvement is frequently outweighed by the cost of implementation. At approximately 233 defects per million (dpm), a five sigma process is well above average when compared to most manufacturing processes.
Consequently, improving an “average” process to this level of performance will typically yield significant financial benefit. Nevertheless, a 5 sigma process may not be sufficient for many critical process steps. In fact, in the pharmaceutical industry, even the “world class” six sigma standard (3.4 dpm) is inadequate with respect to the occurrence of some critical defects. Given the limitations of the 5 sigma barrier, how can we achieve an appropriate level of process performance while maintaining a competitive operating cost?
Fortunately, the Six Sigma pioneers at Motorola, General Electric and other companies realized that the five sigma barrier often occurs as a result of product features which are an inherent part of the product design. If the design team did not perform a sufficient number of studies to develop a thorough understanding of the product or process, it is possible that they may have inadvertently designed limitations, or possibly even quality issues, directly into the product. Although the adverse affects of these design issues were certainly unintentional, often these problems cannot be remedied unless we completely redesign the existing product. The cost of redesign is often the road block which results in the 5 sigma barrier.
It was also recognized by these pioneers, that even if these design flaws were identified prior to product launch, the later in the development cycle that they were discovered, the more costly they became to correct. While this fact is probably true for all kinds of products, it is particularly true in the pharmaceutical industry. Although Scale-Up and Post Approval Changes (SUPAC) guidelines provide direction regarding regulatory requirements to support post approval formulation modifications, changes to process parameters and other registered details; in some cases the cost of these studies can become an insurmountable barrier which may prevent a company from implementing the improvements. Unfortunately, the penalty for inaction is often equally daunting as the product could be subject to frequent batch rejections, extensive investigative testing and possibly even a product recall. All of these events have a significant financial cost but they could also potentially cause irreparable damage to brand and corporate reputation as well. Certainly, as in other industries, the pharmaceutical industry has a well vested interest in developing quality products which require little or no modification after registration and commercial launch. How can we identify and avoid these design flaws? In other words, how do we make sure that we “get it right the first time” without spending extraordinary amounts of time and money in development?
Recognition of the costs associated with poor product design led early Six Sigma practitioners to the development of an additional set of tools and techniques which became known as Design for Six Sigma (DfSS). Among the primary objectives of DfSS is the identification of these shortcomings early in the product development cycle with the intent to eliminate them before the cost of change becomes overwhelming.
Conventional Six Sigma uses various methods to identify and eliminate the root causes of variation which adversely affect the performance of existing products and processes. Essentially, it helps us to improve what we are already doing by ensuring we do it more consistently and with fewer mistakes. As its name implies, the focus of DfSS is further upstream in the product lifecycle. It is applied during the early stages of concept development and the design of a new product or, alternatively, during the redesign of an existing product.
The primary goals of DfSS are to clearly understand the customers requirements and to design a product which is highly capable of meeting or exceeding those requirements. Additionally, DfSS provides the tools and a structured approach to efficiently create these new products by helping to minimize effort, time and costs required to design and eventually manufacture the new product on an on-going basis. The fundamental premise behind DfSS is that in order to effectively achieve these goals, we must thoroughly understand the process and product such that critical material and process parameters are identified and appropriately controlled. The DfSS toolbox has a wide variety of tools and methods, some of which are shared with conventional Six Sigma, to help achieve these goals.
The savvy reader has probably already recognized that the primary objectives and principles underpinning DfSS are completely aligned with the fundamental philosophy of QbD. Guidance documents published by ICH, FDA and other regulatory bodies frequently refer to the need for pharmaceutical R&D teams to develop an “enhanced” level of process knowledge using sound scientific methods and experimentation. This is why these two approaches compliment each other so well.
This paper will take a closer look at this overlap between Design for Six Sigma and Quality by Design, as well as introduce DfSS tools—including Monte Carlo Simulation, Parameter (robust) Design, and Tolerance Allocation—which can also be used to support a QbD program.
Inputs and Outputs
According to ICH Q8, “The aim of pharmaceutical development is to design a quality product and its manufacturing process to consistently deliver the intended performance of the product.” This objective can be attained by using an “empirical approach or a more systematic approach to product development.” DfSS and QbD are very much focused on the latter. Both philosophies are founded upon the definition and understanding of the product, its performance requirements, and the processes by which it is manufactured and packaged.