Intelligent IBP – Closing the Decision Gaps

PreviewMost IBP cycles around the world are based on a 20-year-old process definition supported by 20-year-old planning concepts. This traditional IBP is not set up for a fast-changing world, where speed of decision making confers a competitive advantage. Intelligent automation will change this. In this series of short blogs, I will discuss the transition of traditional IBP towards a more intelligent IBP. In the first blogs, I discussed the rise of autonomous and unshackle the planners. In this blog: Closing the Decision Gaps

The importance of decisions

With a lot of the focus on the sequential planning process and planning capability in traditional IBP, it is good to remember that the early pioneers always designed IBP (S&OP back then) as an executive decision-making forum. So, at the core, IBP must improve decision making.

Decisions and decision practices are important. A 2010 HRB article indicates that decision practices drive 95% of business improvement and 50% of employment engagement. A more recent McKinsey survey indicates that on average, we spend 37 percent of our time making decisions, and more than half of this time is thought to be spent ineffectively. For middle management, ineffective decision making reaches 68 percent.

Speed is important, according to the same survey, organizations that make decisions quickly are twice as likely to make high-quality decisions, compared with the slow decision makers. It’s not easy to get decision making right. Even in a very high performing consumer-goods company on average, 40 percent of the people involved in the decisions contributed no value. 

Intelligent automation will improve decision making. Gartner predicts, that by 2023, more than 33% of large organizations will have analysts practicing decision intelligence.

Decisions in business planning

In a 2021 Foresight commentary, I differentiate four types of decisions in business planning

Operational During execution and the short-term operational horizon, business decisions are highly frequent, repetitive, and at a high granularity level with mostly small impact. These decisions can be highly automated in intelligent IBP.

Planning decisions beyond the operational horizon are less frequent, with lower granularity level, higher impact, and with often more complexity. In intelligent IBP, there is decision time for the human to be augmented by decision intelligence. Human & machine need to be highly collaborative in order to evaluate what-if scenarios, risk modeling, and probable outcomes together. This is where Kasparov’s Law is most impactful.

Strategic decision making is infrequent, at the lowest granularity level, with a high impact and complexity of relationships and interconnectivities. Examples would be the decision to enter a new market, close a factory or engage in a merger. The human will lead and act while decision intelligence provides augmentation, but there will be no automation in Intelligent IBP.

Cultural. Any business decision that involves values, behaviours, ethics, or virtues needs to be human centric. Defining who you want to be as a business, or understanding the social or cultural aspects of decisions, can only be done where the human is leading.

Not unsimilar, McKinsey divides decision types in Big-bet decisions, cross-cutting decisions, delegated decisions, and ad-hoc decisions. The point is; an organization needs to have a clear understanding and alignment around what type of decision they are making during a business planning cycle and what the horizon is of this decision. A strategic decision, which can have months or years of lingering impact, could very well be required next week.

GAPS in traditional IBP decision making

Due to the lack of a planner’s time and the focus on the information role, even in a reasonable steady business state, traditional monthly IBP facilitates limited strategic decisions making. In a state of disruption, the rigid weekly and monthly processes will struggle not only with strategic decisions, but also with more agile daily operational or weekly planning decisions.

COVID exposed the inability of companies to make rapid strategic choices, such as in days or weeks – impactful decisions such as closing a factory, entering or exiting a product category, or reallocating limited resources in their supply chain. Some companies actively bypassed IBP by installing executive-led COVID war rooms to more rapidly implement strategic decisions. Hereby acknowledging that existing planning and decision-making processes are not fit for purpose.

Why did this happen? Traditional IBP assumes no strategic decisions are being made in the short term, exposing a lack of strategic agility. Strategic decisions in the S&OE horizon are not part of the traditional IBP design. Traditional IBP design focuses on the 4-24 months horizon roughly once a month. Decision makers in the S&OE horizon (0-3 months) don’t have the authority to make strategic decisions.

However, in this day and age, it is absolutely absurd to think that critical business decisions will be made every Thursday in week 4, in the 1pm-5pm IBP meeting. The world doesn’t wait for your IBP cycle. It is unacceptable to simply stick to a predefined weekly or monthly decision-making process, especially when your business is in a disruptive state.

Decision making with intelligent IBP

Because of Moravec’s Paradox, the different decisions types and the different decisions horizon, there will be a clear differentiation between machine decisions, human-machine decisions, and human decisions. In my Foresight article about using new technology in planning, I distinguish decision automation from knowledge augmentation and examine the relative desirability of these features across different planning horizons and decision types. Longer planning horizons, for example, often require more human-centric decisions, while shorter-term operational decisions are more amenable to automation

In a reasonable steady state, operational and planning decisions in Intelligent IBP will be supported with high levels of planning process automation and decisions automation. This frees up the much-needed time for planners, which they can use for advanced simulations and to prepare strategic decisions. Hereby solving a key gap in traditional IBP.

In a state of disruption, automated planning and decisions will be dialed down and the human takes a more active role in planning and decisions, supported with higher levels of augmentation by the machine. Activating a combination of more human control and agility under these circumstances, and change your planning and decision approach, is a capability that was never envisioned by traditional IBP. Intelligent IBP has to close this capability gap.

To solve the traditional IBP decision gaps, if circumstances require, decisions in Intelligent IBP have to be able to decouple from the planning horizon and the sequential, predetermined IBP planning process and meeting schedule. In this way, Intelligent IBP supports a steady state as well as a disruptive state.

In a disruptive state, Intelligent IBP has to be able to make operational and planning decisions in days or a week, and strategic decisions with a short turnaround, almost on-demand. This is not easy, as strategic decisions require the highest quality level (strategically aligned and bias free).

Facilitating decisions in a disruptive state, is the new evolutionary challenge for traditional IBP. It will not only require intelligent automation to free up a planner’s time, and new roles and skillsets for planners, it also requires redesigning the organization and in specific the traditional IBP team and give them new incentives. More about that, in the next blog

4 thoughts on “Intelligent IBP – Closing the Decision Gaps

  1. Great read Neils! Thought provoking. I completely agree with your thoughts. With planning becoming more horizon-agnostic and having most of the escalations and exceptions handled on a daily basis as they come up, it will transform the review meetings and enable a much more agile cadence focused on strategic decisions and parameter changes for automation.

  2. Thanks Ruan,
    exactly, the new evolutionary challenge for planner is to facilitate:
    – more strategic decisions in a steady state
    – Faster decisions in a disruptive state
    – Higher quality decisions at any time

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