Quick Thinking

The process industries need facilitated decision making for investment in innovation, says Robert Peeling

WHY is it that the introduction of innovative process technologies appears to be so slow in the process industries? For example, the benefits of implementing flow chemistry at smaller commercial scales have been discussed for over a decade, and yet the reality is that new products continue to be realised through batch processes.  Economies of scale and the two-thirds rule dominate the approach to large-volume, commodity chemicals, leading to highly centralised production, reliance on long-established process routes, and incremental improvements.

In-house project management procedures typically envisage a project as proceeding through a series of stage gates, or decision points. However, there is rarely specific guidance on how to make the stage gate decision. Generally, it will take the form of a report or presentation to the decision makers or budget holders. 

This article describes a methodology that fits within this by supplying a more consistent and auditable basis for the decision makers to use, for all project stage gates. Facilitated decision making on whether or not to invest in innovative process ideas, particularly in the early stages before formal project management techniques are typically applied, and will help a business to better understand the uncertainties and risks.

A brief history

The process industries seem to display conservatism and risk aversion when it comes to investment in new technology. This behaviour may be rational to some extent if it is a response to previous bad experiences, but to what extent were those bad experiences self-inflicted? In 1998, McNulty gathered 41 case histories in the mineral processing sector.¹ He charted the rate at which each project achieved design capacity and divided his sample into four types of project, of which type I (non-innovative, but achieved capacity within 12 months of commissioning) and type IV (involving substantial innovation, but failed to achieve 60% of capacity within 36 months of startup) are of interest here. McNulty highlighted four causes  of failure (see Table 1), the first two deriving directly from management actions, and the other two relating to lack of process understanding.

This work is 20 years old, but little has changed since. It is proposed that the reasons for failures of innovative projects are systemic and common across many organisations in the process industries.

Common problems

One problem that can lead to unhelpful management decisions is a disconnect between an organisation’s business functions and its R&D community. “Commercial will never attend, they are too busy” – a response heard by Britest facilitators trying to establish the business case during initial screening analysis of a development project. The disconnect can also be seen in substantial R&D hours being expended on an interesting problem that has little relevance to business needs. Communication between business and R&D is further hindered by a lack of understanding of business drivers and economics in one direction and requirements to achieve the technical targets expected in the other direction. Unrealistic timelines and resource constraints are major contributors to the failure to develop sufficient process understanding.

Uncertainty within the data upon which a complex decision is based is also a serious problem for decision makers. This is particularly a concern at the very early stages of a project where the decision is about whether or not to commit resources to developing one or more options. Uncertainties in estimating the market opportunity, the project costs and the technical risk involved will be at their maximum at this point in the project lifecycle. With the current culture towards making major decisions with perhaps only a few minutes discussion time and on the basis of a single-page summary, a tendency for conservatism to dominate over innovation is not very surprising. The other impact of this high-pressure approach is that the basis of the decision may be instinctive rather than evidential and is typically incompletely recorded and irreproducible. In this situation a business cannot learn lessons from failure nor learn how to further propagate success.

One consequence of (inevitable) uncertainty, is the need to establish a strategy for mitigating risk. In this context, all types of risk should be considered, not just those associated with safety and environmental protection (extending to patient safety within the pharmaceutical sector). The organisation’s in-house use of language can be a strong barrier to taking an all-risks approach. One particular risk mitigation strategy that is frequently absent, is having a clear understanding of when to either kill or proceed with (and continue investing in) a project.

Towards a method for early-stage decision making

A structured approach (ideally including a facilitator to lead the team of stakeholders) to making early-stage decisions on investing in innovation is needed. It should provide:

• independence and objectivity – ensuring that the decision-making process is data/evidence driven and dispassionate;
• traceability – recording and clearly presenting a recommendation for decision outcome in an auditable and reproducible way;
• adaptability – whilst being aimed particularly at early-stage decisions from initial idea to engineering feasibility, the methodology should be useful for project stage gate decisions later in the cycle as the project moves to implementation;
• compatibility - seamless integration with usual project management practices;
• complementarity – the risk mitigation strategy should be complementary to, rather than a replacement for, existing, well established and more detailed risk assessment techniques (such as HAZOP, FMEA);
• a unified approach - combined commercial and technical criteria (including safety and environmental factors) in a single analysis;
• the ability to capture and assess uncertainty in the available data;
• the means to enable communication and understanding between business and R&D communities of each other’s respective needs and delivery;
• targets - setting specific development targets that are aligned with requirements for commercial success; and
• a systematic probe of whole process knowledge and understanding.