A majority, 62%, of executive rely on their “gut feeling” to make decisions. Many executives seem to believe that human intuition or the “gut feeling” offers a reliable alternative to collecting and analyzing the large amounts of data often required for making the most optimal decisions in the supply chain.
Executive and demand planners will make gut decisions about the supply chain rather than collect data or run a what-if analyses. This trust in intuition is understandable. Organizations did not have the data necessary to make better decisions, so decision-making has always been more art than science.
Today, it’s essential for organizations (or those running decision-making) to avoid falling back on gut feelings. Furthermore, there’s simply no need — the data necessary for every decision exists already. When relying on intuition, an organization is likely to achieve a successful outcome as they are a disastrous one.
Intuition has its place – it should be used to guide decisions like your conscience – but to make a supply chain decision based on intuition is risky. Many demand planners and executives argue that intuition is necessary for the complex and ambiguous problems they face.
However, the exact opposite is true. The more complex an organization’s supply chain, the more data should be analyzed. The more challenges an organization faces, the less they rely on intuition.
In this article we are going to talk about how to move away from that “gut feeling” towards data-driven demand planning. We will cover some of the best practices successful organizations are implementing to improve their supply chain and demand planning.
Large supply chains need the ability to combine demand planning with other business sectors to continuously monitor and adjust the portions of the demand plan that present a variance.
This means organizations need a solution that can decide which demand to pursue, with what products or services, and with the right resource allocation while accounting for all relevant market, regulatory, physical, financial and policy constraints – we call this Integrated Business Planning (IBP).
Unfortunately, many organizations are implementing difficult-to-use enterprise solutions or basic software like Excel. Both of these solutions require a research operations guru – so nobody else knows how to use them – and are difficult to scale.
For example, CPG companies relying on spreadsheets to keep track of SKU’s will find it difficult to handle thousands of products across multiple markets on a global basis. Organizations that use Excel to track trade promotion activity will find it difficult to grow because of the disconnected systems, the static views of data, and the difficulty to run what-if scenarios.
It is no wonder many organizations have to rely on intuition. Experience has shown that some of the most effective demand planning occurs while running what-if scenarios, where people from all aspects of the organizations can play around to produce the most profitable solution.
Without the ability to run unlimited what-if scenarios, organizations will have to rely on intuition to find alternative strategies, policies, and tactics to maximize the revenue and profits while considering supply chain risks and constraints.
The most successful organizations are integrating scalable enterprise solutions that offer the ability to analyze unlimited what-if scenarios, identify opportunities across all aspects of the supply chain, and can prescribe execution. This allows organization to rely on data and not intuition.
Organizations have to use their gut feeling because of a failure to leverage the right form of analytics. Many organizations are still performing analyses using basic reporting systems like descriptive and predictive analytics. Though more organizations are moving to prescriptive analytics, but according to a report published by Gartner, only about 10% of businesses are utilizing some form of prescriptive analytics.
The shortcomings of descriptive and predictive analytics causes many executives to make their decisions based on intuition. Descriptive analytics can only show organizations what happened and why it occurred. It is unable to plan for the future, which leaves many demand planner guessing.
For example, a CPG company that ran a successful ad campaign for their products decides to run the campaign again. The problem is that descriptive analytics cannot predict the market saturation the previous ad campaign caused so an organization has no idea if the campaign that was successful will be a success again.
As a result, many organizations have been moving to predictive analytics to find what will happen if they do things like run the same ad campaign. However, the power of predictive analytics is still limited. It can predict the future, but does not offer optimal solutions in light of the future. For that, organizations need prescriptive analytics.
Prescriptive analytics is a powerful way to analyze data and reach actions to take to maximize profitable growth, given their business constraints. It answers questions that most executives and demand planners have to rely on intuition because of their complexity and ambiguity.
For example, prescriptive analytics can answer, “What should be done?” Or “What can we do to make X happen?” This is powerful. These questions are typically answered through gut feeling, but now organizations can answer these questions with data.
The most successful organizations are implementing prescriptive analytics into their demand planning. They are calculating forecasts AND translating those demand forecasts into actionable and feasible plans.
We find that the biggest reason executives still rely on intuition – even though the data is available – is because they are unable to process and analyze the data effectively. In a recent prediction by Gartner, 60% of big data projects will fail to go beyond experimentation and will be abandoned.
This means many organizations will fail to implement projects that could offer them a huge competitive advantage. The main reason most big data projects are abandoned is due to ineffective change management strategies.
Implementing new software and utilizing prescriptive analytics is a radical change for most companies, meaning organizations need a champion for change – often this starts at the top. Most organizations fail to gain enough support for the change. Even if they are lucky enough to gain company wide adoption, they fail to sustain it.
Making decisions based on data – not intuition – needs to become a part of an organization’s culture if they want to become successful. If an organization wants to move toward data-driven demand planning, they need to create urgency and ensure the software is easy enough for all employees within the organization to use. It will take time for an organization to successfully change their process from intuition to being data driven, but the rewards are high.
Companies who rely on data over their gut feeling can develop a serious competitive advantage. Executives no longer need to hope and roll the dice the dice when it comes to the success of their organization. They can find optimal solutions for maximizing profitability while accounting for constraints without using their gut.