LTPlabs has developed several integrated sales and operations planning (S&OP) projects over the years, with impacts ranging from increased sales forecast accuracy to more efficient operations planning. These projects have been carried out in various sectors, such as consumer goods and manufacturing, showcasing the advantages that organizations can derive from the use of analytical models in their planning processes.
New challenges and opportunities have emerged in recent times. While volatile contexts bring greater complexity to decision-makers, the emergence of new analytical techniques opens unprecedented opportunities for strengthening planning activities.
Challenges and Opportunities
The past few years have been marked by significant disruptions in supply chains, ranging from raw material shortages to the effects of the pandemic and, more recently, the inflationary crisis. Simultaneously, business models have evolved, and consequently, organizational planning has become more complex. Here are some examples:
- Increased product portfolio: On the one hand, companies are under pressure to offer greater customization and meet consumer needs. On the other hand, the emergence of healthier lifestyles has led to the proliferation of new consumer niches, resulting in the diversification of product ranges;
- Volatile and customized pricing dynamics: The expansion of promotional activities and the need to use customer information to tailor prices at a granular level have led to more volatile and customized pricing dynamics;
- New sales channels and the dissemination of the marketplace concept: Retailers and other players have emerged as aggregators of demand from multiple producers, providing consumers with access to a vast array of offerings on a single platform;
- Increased complexity in meeting demand: The rise in e-commerce and home deliveries, often on the same day, and the pressure to offer flexible return and reverse logistics options in response to customer dissatisfaction have complicated delivery operations.
Faced with these difficulties, should companies give up on the effort to achieve integrated planning across functions? Does the high level of uncertainty make convergence difficult, and does the rapid pace of change render planning efforts futile? Or, on the other hand, should companies adopt integrated planning practices that enable them to gain a competitive edge and align their responses to the market? The latter option should prevail, particularly because different opportunities have emerged to leverage planning dynamics.
Here are some examples:
- Improvement in the quantity and quality of information: Businesses generate larger volumes of information, such as customer data from loyalty programs, and have made efforts to enhance their data management assets, such as centralizing information resorting to cloud solutions used by multiple teams;
- Democratization of artificial intelligence (AI): The increased adoption of AI technologies in the business world presents opportunities for improvement in planning activities. Recent advancements in text processing techniques (e.g., OpenAI technologies) have the potential to enhance forecasting methods by utilizing textual information to increase accuracy;
- Increased computational capacity available in information systems: This allows for the exploration of new degrees of freedom, new decision variables, and an expanded solution space evaluated by analytical models;
- Willingness of organizations to adopt planning methodologies and approaches, with S&OP being prominent, as decision-makers increasingly recognize the importance of this planning layer, supported by numerous successful past implementations.
Analytical models are increasingly becoming an integral part of organizations seeking high maturity in S&OP practices, as they provide a necessary mechanism for managing highly complex problems that can be facilitated through historical data and information.
The human-model relationship as a strategy for promoting analytical S&OP models in organizations
Ensuring the successful adoption of analytical models is not an easy task. According to a recent study, only 11% of organizations were able to derive financial results from the application of analytical models . One of the mechanisms that maximizes implementation success is through the establishment of Organizational Learning with AI – creating conditions for mutual learning between human decision-makers and analytical models.
Organizations with better performance are those that utilize human-model interaction to continually refine the process and outcomes.
In the context of S&OP, this human-model interaction is equally essential. In fact, the levels of maturity that a company can aspire to, as described, encompass not only the Information Technology dimension (model dimension) but also the softer dimensions of Meetings and Collaboration and Organization, which are related to decision-making support processes (human dimension). Therefore, finding appropriate mechanisms for learning and communication is key to ensuring the successful adoption of newly developed models.
A potential alignment between humans and models in the S&OP area can be established in six steps:
- Firstly, the generation of plans should be initiated by an analytical model based on specific business objectives and constraints.
- Secondly, the Planning team should select the most relevant plans for discussion after preliminary analysis.
- In the third step, the plans should be translated into actionable data for analysis in S&OP meetings, which involves transforming the information into key indicators for initial analysis by the Executive Committee.
- In the fourth step, the plans should be analyzed and adjustments discussed in S&OP meeting.
- In the fifth step, the Planning team interacts with the model again to generate final plans, incorporating feedback from the previous step.
- Lastly, in a final S&OP meeting, the plan should undergo a final analysis and approval, to be then the guide for the organization in the next months.
