Every company is trying to translate the complexities of the outside world to make the right decisions for the organization as a whole. To achieve that, the different departments need to be aligned. These departments have grown apart because of different incentives and performance metrics. Several factors contribute to this disconnect, including decentralized sales teams versus a centralized supply chain, and misaligned systems that hinder a cohesive view of the world for sales, supply chain, marketing, and finance, exacerbated by the challenge of remote work post-COVID. Inadequate alignment between customer relationship management (CRM) or supplier relationship management (SRM) systems and supply chain taxonomy further compound the issue, leading to the necessity of a time-consuming and costly Sales & Operations Planning (S&OP) process.
To flourish, organizations must radically rethink this reality, enabling faster, less hierarchical, and more accurate decision-making while staying aligned with their objectives. Navigating the constantly changing world demands nimbleness and agility through distributed thinking.
So, how do we achieve this?
The outcome of a new approach should align with the company's long-term strategy, whether it's centered around growth, customer intimacy, cost leadership, or a combination thereof across various departments. Setting goals necessitates understanding what truly drives progress, considering an organization's markets, historical performance, potential innovations, and ensuring alignment across the rest of the organization.
To begin this radical transformation, we need to shift from an analog to a digital understanding of the driving factors. Artificial Intelligence (AI) is the key to discern the demand drivers by translating the world into events, competition activities, and customer behavior. AI can establish relationships between these factors under human supervision. Once the effects and their relationships to reality are established, we have the language and foundation to align the entire organization around a plan.
Plans for different departments can then be constructed based on these assumptions and tailored to each function's purpose and form. For sales, this may include budget, growth, customer base, pipeline, and tenders. Marketing focuses on market share, procurement on logistics, and so forth. AI will then monitor signals captured by humans, or externally sensed information translating them into quantifiable data while linking them to assumptions. This process simplifies understanding through ChatGPT, eliminating the need for extensive training or multiple dashboards and reports.
When real-world changes occur, they will alter assumptions and subsequently impact the numerical data. However, everyone receives the same message and the best actionable insights at any given moment. Sales teams act as sensors for changes at the customer level, while order consumption or sell-in and sell-out information serve as sensors for the supply chain. Unforeseen correlations or parameters can also drive change. Historical correlations with economic indicators or competitor data can accumulate over time—consider factors like inflation, demographics, income growth, and weather.
With all data digitized, ChatGPT becomes an enabler for real-time data collaboration. During cross-functional meetings everyone has access to up-to-date demand insights through ChatGPT, facilitating seamless collaboration, discussion, and decisions based on real-time data. This, for the first time, allows departments to understand each other's data at a pace closer to that of the market. Continuous forecasting becomes possible, as Garvis continuously updates demand forecasts using the latest information and market trends. This forward-looking perspective enables proactive responses to demand fluctuations.
For the short-term impact, departments can collaborate around goals, validating underlying assumptions and focusing on objectives. Here, the decision-making power can be more distributed, substantially reducing latency.
For the longer term, demand shaping takes precedence. Starting from the best possible forecast, constantly updated drivers allow immediate measurement of strategic assumptions' deviations. By evaluating what works and aligning assumptions, corrective measures can be promptly implemented.
This approach allows problems to be identified and solved in real time through a chat or messaging format, replacing the need for a centralized control tower with a network-based approach.
Furthermore, "what-if" scenarios can be conducted for uncertain outcomes, such as persistent inflation, new product launches, or capacity expansions. Scenario planning and efficient resource allocation become achievable with enhanced demand visibility, resulting in optimized resource distribution, reduced safety inventory, and ultimately reduced costs.
If you want to discover how we tackle this, you can still register for our webinar on September 14th, 2023.