When people approach our organization – Brave New Workshop Creative Outreach – to work with them on their innovation programs, the first step is always to simply set up a call to get to know them and their situation. For more than 15 years I have started with the same question: “Can you tell me a little bit about your current innovation program?”

Although I have posed that question more than one thousand times to thousands of different people in hundreds of different industries and organization whose programs varied in their degree of development, the answers seem to have much in common and are startlingly consistent.

For often what seems like hours or even days, I listen to them share about initiatives, internal definitions written, programs launched, task forces created, company rallies, hiring and firing of chief innovation officers and third-party consultants, research and numerous dead ends.

This is the point in the conversation at which I typically drop the big question. “So, tell me about what your company is doing to help people behave more innovatively?” On the phone, there is always an awkward silence; in person, there is a bit of eye glazing and a far-off stare. In both cases the reply is usually the same: “What do you mean by behavior?”

I then get very complicated and scientific in my response, which is typically, “What I mean by behavior is, how do the people in your organization act? How do they treat each other? What does it feel like after people come up with ideas? What happens when an innovation attempt fails? How do people treat each other on conference calls or in brainstorming sessions? Does the vibe in the room ever change when certain individuals walk in? What I mean is: How do people behave?”

John Sweeney: "If the systems we create aren’t rooted in a thorough understanding of the human interaction they are supposed to support they can actually deter the experience we want to create for our customers."

We often forget about everyday behavior, because in a way it is so basic that the big thinkers – the super smart innovation architects – can assume that everyday behavior is a given that will automatically change once a great system is in place. The old saying “Everything looks like a nail to a hammer” can be an appropriate way to think about the manner in which innovation programs are structured – and often the teams who work on those programs forget a very basic ingredient of a successful innovation effort: the people – and all their fears, emotions, and humanness – who need to fuel it.

Although sometimes Steve Jobs is quoted too frequently, we are fans, and can’t help but share the way he put it: “Innovation has nothing to do with how many R&D dollars you have. When Apple came up with the Mac, IBM was spending at least 100 times more on R&D. It’s not about money. It’s about the people you have, how you’re led, and how much you get it.”

Innovation is about people and their assumptions and subconscious thought patterns (a.k.a. their mindset) and their daily actions and habits that stem from that mindset (a.k.a. their behavior). Put all those together, add some procedures, rewards and penalties, social dynamics, unspoken rules – and a pinch of stress – and you get a wonderfully messy, organic, and complex environment. An environment in which behavior, not lip service (although words are also important), drives the results. If you fail to address that daily behavior, even the greatest strategy and plan to drive innovation are doomed to fail.

If the systems we create aren’t rooted in a thorough understanding of the human interaction they are supposed to support, they can actually deter the experience we want to create for our customers.

Below are three tips to make sure you focus on people and keep them at the forefront of your innovation initiatives:

1. Be relentlessly curious. Curiosity is an aggressive, investigative, almost inexhaustible need to learn, find out and experiment. That curiosity shows itself in how people communicate with others, as well as their ability to jump in and engage with a situation – or, in the context of innovation, try things out. Curiosity fuels our ability to intently listen to our teammates, colleagues, direct reports and customers.

2. Appreciate differences. Unique views and opinions are the key drivers of innovation – embrace them, don’t throw them to the curb. More often than not, it takes many iterations to land on the best product or path forward – allowing the people in your organization to contribute to those iterations – in big or small ways – inevitably empowers your workforce, builds trust and a sense of ownership.

3. Walk the walk. First, honestly evaluate and observe your own behavior in a way that provides clear direction for what you need to do to get better at your job. Only then can you lead and contribute with a boldly realistic and open perspective. Be the first on your team to embrace a few powerful assumptions, including:

Mistakes are a great source of inspiration and learning.
Change is fuel—not an obstacle.
Ideas and honest opinions have value that we should celebrate, not judge.
We all have the power to create change and impact those around us.
We don’t need all the information just to begin.

Focusing on helping your people – including yourself – embrace a mindset of discovery will help them (and you) behave consistently through highs and lows, to recognize possibilities, to listen for all available lessons, and to move forward into the unknown with a rational sense of risk and a miraculous sense of hope.

John Sweeney is co-owner and executive producer of Brave New Workshop, America’s oldest satirical comedy theatre. He is an innovation expert, corporate leadership speaker and author (with Elena Imaretska) of the The Innovative Mindset (Wiley; Oct. 26, 2015).

Dreamstime

Modeling Processes the Monte Carlo Way

This risk-management method simulates thousands of trial runs, using random values for each factor.

Mark Sidote

May 01, 2020

We have all made decisions, whether in our personal or professional lives, based on imperfect information. How can we manage that risk and improve business outcomes? One answer is a statistical method called Monte Carlo simulation.

Monte Carlo simulation is a problem-solving technique used to approximate the probability of certain outcomes. This method simulates thousands of trial runs, using random values for each factor, based on predetermined probability distributions. Knowing the probability of certain outcomes provides better insight and leads to informed decision making. While based on statistical methods, with today’s software, Monte Carlo can be much easier to learn and apply to your business context.

Monte Carlo simulation is applicable to an array of scenarios, including:

Business case development and ROI modeling
Risk quantification associated with new product rollout and development costs
Forecasting future sales based on uncertain market conditions
Determining staffing requirements under various scenarios
Estimating volume to produce based on downstream demand
Estimating project completion date based on duration times for each activity
Estimating the total cycle time for a process based on task durations for each step in the process

For example, let’s look at a core process within a paperboard manufacturing facility where paperboard is made from a pulp derived from wood chips. For obvious reasons, it is critical that wood chips are always available for the pulp mill.

Pulp consists of a mixture of both hardwood and softwood chips. As depicted in Figure 1, some chips are purchased and sent directly to storage, and others are produced at the facility. To produce wood chips, logs are first debarked and then ground into wood chips, which are then sent to storage. Prior to use in the pulp mill, they are run through a screening system to ensure the pulp mill gets the proper sized chips.

Figure 1: Paperboard Manufacturing

Conducting the Analysis in Three Steps

Step One: Define the input variables or assumptions

This could be the variables on a process map, or simply the input variables on a spreadsheet. We’ll need to determine the probability distribution for each. If historical data exists, most statistical software packages can identify the best-fitting distribution. If data does not exist, it may need to be collected or gathered through input from knowledgeable staff. Often, a triangular distribution works very well by simply asking for information around the best-case, typical-case and worst-case scenarios.

Step Two: Determine the forecasted metric

In this example, the key metric is the ratio of available chips to the daily demand of the pulp mill. Available chips, in this case, are those purchased, produced, and currently in storage as shown on the process map in Figure 1.

Step Three: Run the simulation

There are many software packages to run Monte Carlo simulation. The software used in this example is called Companion by Minitab. To ensure sufficient availability, the organization would like to have an average of 3 times the daily pulp mill demand for each type of chips, both hardwood and softwood, but no less than 2 days and no more than 4 days. Carrying too much inventory drives up cost and can degrade quality. Analyzing only the hardwood variety, Figure 2 shows the organization can expect an average supply of hardwood chips to be 3.9 times greater than the daily demand of the pulp mill and can expect to have and inventory of greater than 4 times the demand almost 40% of the time. Assuming an average hardwood pulp mill demand of 4200 tons per day (TPD) at $60 per ton, the pulp mill consumes about $252,000 per day of hardwood chips. Carrying inventory of more than the upper specification limit of 4 times the daily demand results in a significant opportunity for cost reduction. The question now becomes, “Where do we start with improving the process?”

Figure 2: Simulation Results

Improving the Process

The next phase in the process is called parameter optimization and refers to identifying the optimal settings on inputs that can be controlled. An extruding process, for example, may have to operate at no more than 250⁰F and no less than 190⁰F before scrap product is produced. In this example, there are three input factors that can be directly controlled: 1) purchased chips, 2) log throughput and 3) beginning inventories. All of these can be optimized simultaneously or one at a time to bring the ratio of available chips to daily pulp mill demand closer to the target of 3 days with less excess inventory.

To illustrate parameter optimization, one input factor was chosen. Since purchasing chips from the outside is expensive, this was the obvious first choice. Assuming there is a minimum of 200 TPD that must be purchased to maintain favorable pricing, but no more than 500 TPD purchased, Figure 3 shows the results of parameter optimization, keeping all other input factors constant.

Figure 3: Parameter Optimization Results

Figure 3 shows a decrease in excess inventory from almost 40% in Figure 2 to about 15% and an average inventory of hardwood chips of 3.5 times the daily demand of the pulp mill, resulting in a 60% reduction in excess inventory and a significant cost reduction.

We live in a world of uncertainty and virtually every business decision has some level of risk. Most decisions have a multitude of possibilities, but knowing, understanding, and quantifying this uncertainty increases the chance of favorable outcomes. For process improvement practitioners, Monte Carlo simulation is an important tool and method to reduce risk and facilitate those decisions grounded on data and evaluation.

Mark Sidote is a Principal with Firefly Consulting, a firm specializing in innovation and operational excellence. Mark has delivered multiple lean transformation, continuous improvement, and process redesign effort. He has significant experience in Design for Lean Six Sigma, Lean Transformation, and Lean Six Sigma deployment.