Transform Your Supply Chain Planning and Marketing Strategies with Google Cloud and SAP Integration
July 13, 2023 | Premangsu Bhattacharya
Blog / From Reactive to Proactive: A Practical Guide to Implementing Decision Intelligence Platforms
As the world grapples with heightened unpredictability, is your supply chain operation equipped to respond to swift market fluctuations? Have you evaluated your readiness to shift from a reactive to a proactive approach to managing market uncertainties? These questions are no longer a part of a hypothetical future. With the emergence of AI-enabled technologies, this future is already here, empowering businesses to reimagine traditional supply chain paradigms and encouraging them to be more agile.
The global economic landscape is more volatile now than ever before. Supply chains, the backbone of organizations, are continually affected by unexpected disruptions, be it political unrest, environmental disasters, or sudden shifts in consumer demand. Traditional Supply Chain Management (SCM) software, despite its sophistication, is inherently reactive and often lags in the face of rapid market changes. They function within the confines of their predefined models, limiting the scope of their effectiveness when it comes to dealing with uncertainties.
Take, for instance, a logistic planner nestled in her Chicago office, overseeing a vast supply network. Unexpectedly, a port strike emerges in Baltimore, a disruptor that could dismantle her meticulously devised logistics plans. Unaware of this sudden development, her only option is a reactive, scramble-to-contain response, leading to significant logistical delays, escalating costs, and dwindling customer trust.
Similarly, imagine an unexpected surge in demand for fan gear following an NBA match. As the final whistle echoes through the arena, fans rush online to grab commemorative gear, leading to a sudden surge in Google search trends. However, these real-time demand signals are beyond the perception of traditional Supply Chain Management systems.
Without the capability to integrate these external data sources, the SCM system stays oblivious to the sudden increase in demand.
By the time traditional methods of demand sensing catch up, the golden opportunity might have already passed. Warehouses are unable to adjust their inventory in real-time to meet the sudden spike, and the gap between demand and supply widens. This situation frequently results in stockouts, unsatisfied customers, lost sales, and a significant amount of unrealized revenue – all resulting from the disconnect between real-world dynamics and your SCM.
Often, businesses find themselves at the crossroads of crucial decisions, contemplating if additional investment in consultancy or recruitment of new analysts is the right path. However, the answer to many of these tough questions might already exist – not in external resources but within your own data.
Modern businesses are repositories of vast amounts of data generated by various systems like SCM, ERP, and CRM. But a common challenge faced by many is the existence of data silos, where each system functions independently. This leads to a lack of a consolidated, real-time view of data, making it difficult to identify valuable insights that are hidden in these silos.
Consider an example: Your CRM system registers a spike in customer inquiries related to a particular product. Simultaneously, your SCM detects an abnormal delay in the delivery of this product. Ideally, these insights should be instantly correlated to foresee potential dissatisfaction and churn. But in a siloed environment, these insights remain isolated, and the opportunity to proactively address a brewing issue is lost.
Moreover, these data silos also slow down the decision-making process. Each department, armed with its own set of data, has a limited view of the situation. This leads to decisions made in isolation that could be at odds with the broader business strategy or the current market scenario.
A decision-maker needs more than just data; they need insights that can guide action. However, an SCM merely provides static data points, leaving it up to the decision-maker to decipher them, understand the trends, connect the dots, and make a decision. The problem here is the time consumed and the lack of context, which leaves room for human error and delays.
The SCM system, for example, might tell a decision-maker how many units of a product are left in the inventory. But what it doesn’t tell is how quickly these units are moving, whether a restock is necessary right now, or if a sudden market event might influence demand. Thus, even with all the data, decision-makers are often left in the dark, making choices based on best guesses or gut feelings.
In contrast, what decision-makers need in today’s dynamic world is a system that not only provides data but can also interpret it to offer real-time insights, recommendations, and predictions. The inability of traditional SCM systems to deliver these aspects makes them less than ideal for a world that values proactive decision-making.
In an era where swift decision-making is more crucial than ever, Decision Intelligence Platforms are emerging as powerful enablers, empowering businesses with the intelligence they need to navigate a world rife with unpredictability. Providing a new dimension to business decisions, Decision Intelligence Platforms bridge the gap between vast amounts of data and meaningful insights, allowing businesses to act proactively and decisively.
These platforms offer advanced analytical capabilities commonly known as Artificial Intelligence (AI) that, when combined with traditional SCM systems, enhance their operational prowess. Before diving deeper into their value proposition, let’s bust a couple of misconceptions surrounding what these platforms are and what they aren’t
Far from being another data organizer, Decision Intelligence Platforms are the drivers of innovation in today’s complex supply chain landscape. Built on advanced technologies like Generative AI, these platforms interpret data, not merely organize it.
Pluto7’s Planning in a Box is a stellar example of this. It allows users to ask questions about their data and provides insightful answers, thus transforming raw data into strategic insights. By dismantling data silos and incorporating external data, Decision Intelligence Platforms offer a comprehensive, multi-dimensional view of your supply chain operations. The end result? A treasure trove of actionable insights for swift and informed decision-making.
Decision Intelligence Platforms are not designed to replace your SCM systems. Instead, they augment the functionalities of these systems by providing a layer of Generative AI-powered intelligence.
SCM systems are adept at collecting and organizing transactional data but often fail to generate actionable insights. This is precisely where Decision Intelligence Platforms begin their work. They take the raw data from SCM systems, apply AI algorithms, and provide a robust, insightful understanding of trends, forecasts, and opportunities.
Consider this scenario: Your SCM system flags an unexpected dip in inventory levels. Pluto7’s Planning in a Box platform not only recognizes this trend but goes a step further. It identifies the cause, predicts the potential impact on future sales, and suggests optimal replenishment strategies. Hence, while your SCM system ensures smooth operations, the Decision Intelligence Platform provides the strategic intelligence you need to navigate market unpredictability.
Unlike traditional SCM systems that are generally backward-looking (looks at historical data), Decision Intelligence Platforms take a forward-looking approach (looks at historical + real-time data).
Leveraging predictive analytics and machine learning, these platforms analyze historical data and market trends to anticipate future demand accurately. Pluto7’s Planning in a Box, for instance, uses machine learning algorithms to forecast customer demand across various market scenarios. This predictive insight empowers businesses to plan their inventory and logistics efficiently, significantly reducing the risk of stockouts and overstocks.
In the conventional SCM setup, one of the significant challenges is insight sharing. Data is often isolated within various systems and environments, with team members resorting to disparate spreadsheets to compile and analyze information. This process is time-consuming, error-prone, and it fails to deliver the real-time insights necessary for swift decision-making in today’s fast-paced market dynamics.
In contrast, Decision Intelligence Platforms are engineered not just to gather data but to create meaningful connections between different data sources, delivering an integrated, insightful view of your supply chain.
For example, let’s say you’re dealing with a sudden surge in demand. In a traditional setup, recognizing this trend would first require data collation from different systems followed by rigorous analysis, all while the golden window of opportunity could be closing.
However, with a Decision Intelligence Platform, such as Pluto7’s Planning in a Box, this entire process is drastically simplified. The platform is constantly analyzing real-time data from various sources. Upon detecting a demand surge, it doesn’t just stop at identification. It shares these valuable insights instantly with all relevant teams – sales, production, and logistics.
If you are using SAP IBP, for example, the insights generated can be easily shared on SAP Analytics Cloud (SAC) or Datasphere or even plugged back into your spreadsheets.
This allows for a swift, coordinated response that can mean the difference between capitalizing on an opportunity and watching it slip by.
In the complex landscape of data analysis and business decision-making, Pluto7’s Planning in a Box stands out as a state-of-the-art, Generative AI-enabled Decision Intelligence Platform. Built on the robust infrastructure of Google Cloud, it acts as a dynamic bridge between raw data and actionable insights, enabling businesses to navigate through the intricacies of the supply chain with ease.
Unique Aspects of Planning in a Box:
Let’s delve into how this platform operates. Planning in a Box gathers and harmonizes disparate data sources, creating a solid data foundation. Leveraging the powerful analytical capabilities of Google Cloud tools like BigQuery and Vertex AI, it processes this data and feeds the derived insights back into the source system.
What results is a conversion of raw, unstructured data into insightful, actionable intelligence. As an end-user, the platform offers an interactive and intuitive experience, allowing you to converse with your data.
Consider the role of a demand planner. With Planning in a Box, they can directly ask questions such as:
The result is a decision-making process that is not just responsive but also predictive, proactive, and able to address the unpredictability inherent in today’s fast-paced business environment.
When it comes to supply chain management software, SAP Integrated Business Planning (IBP) is a widely adopted solution. Yet, through our experiences with numerous SAP users, we’ve identified several user-centric challenges. However, when paired with Pluto7’s Planning in a Box, SAP IBP evolves into a demand planning powerhouse, able to fully harness AI and ML capabilities for insightful data analysis and interpretation. Let’s explore how the combined force of SAP IBP and Planning in a Box tackles everyday use cases in the life of a demand planner:
|Challenges with SAP IBP
|Solutions with SAP IBP + Planning in a Box
Difficulty in Combining Data Sources: Demand planners often struggle to combine data from diverse business operations, leading to an incomplete view of the supply chain.
Unified Data View: Planning in a Box integrates seamlessly with SAP IBP and other data sources, providing a comprehensive, real-time view of the supply chain.
Predictive Limitations: Demand planners find SAP IBP’s reliance on historical data insufficient for capturing the dynamics of rapidly changing markets.
Real-Time Predictive Capabilities: Planning in a Box supplements historical data with real-time internal and external data sources for dynamic market insights.
Complexity of forecast error calculations: SAP IBP uses advanced statistical measures like MAD, MAPE, and others for its forecasting error calculations. This complexity can lead to confusion and misinterpretation of the results.
Simplified Error Interpretation: Planning in a Box leverages advanced AI, Machine Learning, and Large Language Models (LLMs) to provide real-time, contextual insights, potentially simplifying the interpretation of forecast errors and improving forecast accuracy.
Scalability and Reliability Concerns: System scalability and reliability are crucial for demand planners. Any disruptions can affect demand forecast delivery.
Enhanced Scalability & Reliability: Built on the Google Cloud Platform, Planning in a Box ensures high system performance, reliability, and scalability.
Limited Scope of Data Analysis: While SAP IBP offers robust demand planning and forecasting capabilities, it does not offer extensive data analytics capabilities across multiple business functions like marketing and sales.
Unified Analytics: By leveraging Google Cloud’s BigQuery and Vertex AI, Planning in a Box offers advanced data analytics capabilities across multiple business functions, connecting supply chain with marketing and sales analytics for more comprehensive insights.
If you are looking to delve into the world of Generative AI-enabled Decision Intelligence Platforms and grasp their transformative potential, we welcome you to our exclusive Pluto7 Workshop powered by Google Cloud. Not only will this workshop provide a comprehensive understanding of how these systems, like our Planning in a Box solution, operate in a real-world scenario, but it will also give you the opportunity to launch a pilot, delivering actionable results in 4 weeks or less.