The Power of Process Orchestration: A Game-Changer for Supply Chain Professionals

Executive Summary

In the dynamic world of supply chain and operations management:

• Process orchestration has emerged as a game-changer for achieving operational excellence and customer satisfaction.
• Process orchestration is the coordination and optimization of various interdependent moving parts (people, systems, devices, processes, data, assets) within a supply chain. It goes beyond traditional process automation, enabling end-to-end visibility, real-time adaptability, and intelligent decision-making.
• Process orchestration aims to synchronize these moving parts, enabling seamless communication, data exchange, and decision-making to achieve the desired outcomes and deliver value to customers.

This is made possible with Metronome by combining real-time data ingestion, a graph model embedding a powerful workflow engine and AI-powered solvers to optimize and streamline end-to-end processes.

Key Advantages:

1. Agility and Adaptability: Real-time data integration enables quick responses to changing conditions, ensuring optimal performance in a fast-paced environment.
2. Efficient Resource Utilization: Process orchestration optimizes resource allocation, reducing delays and boosting productivity across the supply chain.
3. Intelligent Decision-Making: AI capabilities provide data-driven insights, enhancing process efficiency and driving better outcomes.
4. Continuous Optimization: The system learns from historical data, refining workflows for continuous improvement over time.
5. Scalability and Complexity Handling: The combination of a graph model and AI allows for seamless management of complex supply chain processes.

Process orchestration is an essential tool for supply chain professionals aiming to excel in today's rapidly changing environment. By embracing real-time data and AI-driven insights, businesses can achieve supply chain excellence, ensuring streamlined operations and satisfied customers.

Metronome offers a practical approach to achieving operational excellence.

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Introduction

In today's fast-paced and ever-evolving business landscape, achieving operational excellence while ensuring customer satisfaction has become a monumental challenge. Amidst the complexities of supply chain management and operational workflows, companies are seeking innovative solutions to streamline processes and adapt swiftly to dynamic market conditions. Enter "Process Orchestration" – a game-changing concept that promises to revolutionize the way businesses operate and grow.

In this playbook, we delve into the realm of process orchestration, exploring its key components, benefits, and the potential it holds for driving growth in mid-size and large companies. We will draw insights from real-world examples to showcase the transformative impact of process orchestration on businesses.

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Table of Content

• Chapter 1 - Unraveling Process Orchestration
• Chapter 2 - The Symphony of Success: Key Components of Process Orchestration
• Chapter 3 - Benefits that Echo: Amplifying Growth with Process Orchestration
• Chapter 4 - Implementing the Maestro's Vision in the Real World: Success Stories
• Conclusion - The Crescendo of Transformation

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Chapter 1

Unraveling Process Orchestration

In this chapter, we embark on a journey to understand the essence of process orchestration. We explore its definition, fundamental concepts, and how it stands apart from traditional process automation.

What is the problem faced by supply chain professionals on a daily basis?

Supply chain professionals grapple with the relentless pace of the modern business landscape, where unpredictability and rapid shifts are the norm. In this dynamic environment, even the most meticulously crafted plans often crumble when confronted with the stark reality of operations. Efficiency falters, and the finest strategies can swiftly become irrelevant due to unforeseen events that can't be anticipated at a high level of detail. The challenge intensifies as the need arises to reallocate tasks, a process hindered by the presence of silos and intricate interdependencies. The resulting manual workarounds drain valuable time and resources, adding to the complex web of challenges. In this whirlwind of constant change, the same issues tend to resurface repeatedly, amplifying the need for a more streamlined, adaptive approach that can navigate this intricate maze with agility and precision.

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The importance of process orchestration will first be felt in fast-paced industries where large teams meet capital intensive environments with many unforeseen events (airport, logistics and distribution, production, hospitals, …)

Here are three examples:

For instance, in the realm of ecommerce fulfillment centers, the immediate tasks to be addressed within the next hour are intricately tied to the real-time orders placed on the website. The ability to proactively plan based on these incoming orders is of paramount importance for optimal team sizing and the alignment of requisite competencies.

Similarly, consider the scenario within an assembly lines manufacturing facility. An unexpected equipment malfunction at a particular station can set off a cascading series of disruptions downstream. This chain reaction may inadvertently delay multiple customers' orders that were previously progressing on schedule.

Moving on to the aviation sector, particularly within the context of airport operations encompassing catering, cleaning, and luggage management services, seemingly minor factors like weather conditions or slight fluctuations in aircraft arrival times can significantly impact the orchestration of staff allocation. Continuous monitoring and the agile redeployment of ground agents become imperative in order to curtail lateness and ensure the optimal utilization of human resources. The dynamics at play in this setting closely resemble an intricate game of musical chairs, where constant adjustments are the key to maintaining harmony.

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Understanding the concept of process orchestration, why is this needed?

Imagine process orchestration as akin to an orchestra, where each musician harmonizes with the conductor to create a coherent musical piece. The conductor's role is to establish the rhythm, ensuring the delivery of demands On Time In Full, aligned with gesture understood by every member of that orchestra.

In the context of your end-to-end supply chain, envision the musicians as disparate elements encompassing siloed systems, data, people, and supplies. The conductor emerges as the process and demand orchestrator, bringing order to the ensemble.

In many organizations, process orchestration unfolds subtly within the intricate dance of team members, marked by reflexes and individual approaches to addressing challenges. This dance is seen in real life with manual Excel extractions, reconciliations through "vlookups", and too many manual email exchanges. Have you ever questioned why Excel remains a prevalent tool despite the advancement of enterprise systems like ERPs, APS, and WMS? Essentially, the daily tasks of front-line planners and managers often involve compensating for the absence of an overarching conductor in the organization. They nudge teams to stay synchronized, much like keeping a drumbeat.

Consider a practical scenario with and without orchestration:

• Status Quo: At 9:20 am on a Tuesday, a material planner discovers a delayed supplier replenishment through a manual SAP check. Swiftly, the planner contacts a friendly contact to her at the supplier. Relying on years of firefighting experience to maintain production, they devise a workaround by adjusting production to accommodate the material shortage. While the impact is minimized, next week's factory efficiency will suffer, and add even more anxiety to operational staff.
• Process Orchestration: By 8:00 am on that Tuesday, two salespersons receive automated alerts on their smartphones regarding orders endangered by insufficient raw materials. The finished goods stock isn't adequate to fulfill these orders. One salesperson confirms he cannot do much, while the other promptly contacts the customer, transferring the order to a product with surplus stock for a small discount. No emails exchanged, resolved within 10 minutes. The production plan remains unaffected, and the supply chain remains unburdened. The key distinction: a seamlessly connected layer that enables swift collaboration among all stakeholders.

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Key differences between process automation and process orchestration?

Contrasting process automation with process orchestration unveils a crucial departure. Traditional process automation revolves around the automation of singular tasks and straightforward linear sequences, often devoid of interconnectedness. In contrast, process orchestration diverges from process automation on two pivotal fronts:

• Horizontally: Process orchestration transcends mere automation by weaving together end-to-end processes and systems. In contrast, conventional automation confines itself to the automation of isolated segments within the chain.
• Vertically: Process orchestration possesses the capacity to apprehend the intricate interdependencies that link processes, flows, assets, and personnel. This depth of comprehension extends far beyond the realm of automation's capabilities.

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Chapter 2

The Symphony of Success: Key Components of Real Time Process Orchestration

Discover the symphony of success as we dissect the essential components of process orchestration. Real-time data ingestion, the power of workflows onto a graph model, and AI-driven solvers form the backbone of this transformational approach.

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‍Real-time Data Ingestion onto a Graph Model: The role of real-time data in driving adaptive decision-making

In the dynamic and rapidly changing landscape of supply chains, the value of real-time data cannot be overstated. Swift access to current information is essential for nimble decision-making. By continuously gathering and processing real-time data from diverse sources, organizations can achieve unparalleled operational visibility, empowering them to take proactive actions.

However, a common misconception prevails: the idea that installing real-time dashboards into cutting-edge data lakes, synchronized across systems, can solve all challenges, particularly in ensuring timely customer order fulfillment or new product launches. In reality, such advancements often fall short. While connected data is advantageous, it does not inherently address the broader issue. Processes, routines, and tasks typically remain trapped in emails, spreadsheets, or isolated legacy systems, devoid of comprehensive end-to-end connectivity. The crux of the challenge rests in effectively interlinking, contextualizing, and weaving data with the processes and actions imperative for meeting customer demands punctually and comprehensively. This is precisely where the graph model emerges as a crucial asset.

Consider this scenario: Acme Corp receives an order from Customer Inc. via CRM (e.g. Salesforce) for delivery on January 2nd. Following a conversation with the customer, the salesperson adjusts the date to January 1st.

Approach A (data synchronization with no process connectivity via a graph model): The alteration in CRM triggers an automatic update in the ERP system, thus prompting the production planner to reschedule production within the previously determined timeframe. Given the volume of such changes, it's conceivable that the production planner might not recall this specific shift, nor remember the contact person to consult for clarification. Consequently, a cycle of manual interventions and coordination between the factory planner and material planner ensue, resulting in a production batch being rescheduled by day's end. This creates substantial noise.

Approach B (process orchestration): The adjusted data initiates an alert involving the relevant production planner, raw material stock manager(s), and potentially marketing teams for new products. This smart workflow captures the change history, showcases the data values from both the ERP (reflecting January 1st) and CRM (reflecting January 2nd), highlighting the misalignment. The accountable party (Sales) is invited to validate the rationale, while the responsible party (Production planner) is urged to discuss and validate the proposed course of action. This process unveils that the customer requires the product earlier, but it can be supplied using an alternative reference temporarily, accompanied by a slight discount. Importantly, this course of action has zero impact on production and reduces stock levels of soon-to-be-obsolete items sold at a discount.

The underlying insight here is that automating exception management processes, particularly for orders, can expedite decision-making without cumbersome email exchanges. This emphasis on process orchestration rather than just data synchronization is key.

In summary, the marriage of data synchronization with process orchestration amplifies the potency of both. Data synchronization in isolation often falls short. Without process orchestration, IT and tech teams are inundated with exceptions from various functional units, creating a complex black box that hinders rather than facilitates rapid and effective collaborative decisions.

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‍Workflow based on Graph Model: How the graph model enables a holistic view of interconnected data and processes

At the heart of modern supply chain innovation lies the graph model, that offers a profound understanding of interconnected assets and processes. Contrasting with conventional linear supply chain models, the graph model unveils the intricate relationships that define a supply chain network. It intricately captures the dynamic interplay between diverse components like orders, assets, locations, suppliers, and customers. Through nodes representing individual elements and edges symbolizing their connections, this model forms a network that mirrors the true complexities of your supply chain through ontologies.

Elevating Workflow Dynamics

Elevating the graph model's prowess, a robust workflow engine takes center stage. Traditionally, workflows entail sequential steps aimed at achieving a specific outcome. Yet, with the graph model, the scope broadens significantly. Imagine the scenario of managing luggage in an airport: the model grasps not only the physical queues but also comprehends intricate details, including future paths of elements. This knowledge enriches the workflow engine, allowing it to act as the conductor taking preventive and corrective actions without adverse impacts in the medium term.

The Symphony of Process Orchestration

Critical path analysis becomes effortless under this orchestration, enabling the identification of bottlenecks, potential risks, and optimal routes for seamless process execution. The integration of the graph model and the workflow engine empowers supply chain professionals with real-time insights into the status of every component and its ramifications for the holistic system. This holistic view empowers organizations to respond rapidly to shifts, anticipate disruptions, and optimize performance in ways previously unattainable.

Adapt and Thrive in Uncertainty

The graph model evolves into a living representation of your supply chain, offering adaptability in the face of complexity. Its innate ability to capture the network's intricacies forms the foundation for leveraging artificial intelligence (AI) effectively. The contextual information inherent to the graph model enhances AI's capabilities and supports linear solver strategies without requiring large amount of data. This blend of tech ingredient creates a solution where businesses optimize in real time despite uncertainty and complexity.

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‍AI-Powered Supply Chain Processes: Leveraging AI capabilities for intelligent decision-making and optimization

AI is a powerful technology that can be harnessed once the data model using graph technology is deployed. While delving into every facet is beyond this article, we illuminate the various types of AI that fuel different facets of process orchestration:

• AI as Solver: In the realm of advanced technologies, AI-powered solvers emerge as a dynamic catalyst for process orchestration. These intelligent algorithms can find the optimum by testing different options and comparing them. Many such solvers are called AI, yet they exist for decades. Here the challenges in the past was to us solvers accessing live data. This is now possible with the right computing power and data model.
• AI as Critical Paths Selector: This form of AI empowers the system to make intelligent decisions grounded in intricate business logic. It excels in navigating complex webs of business rules and critical paths, ensuring that the orchestration remains aligned with the overarching strategic objectives.
• AI as Machine Learning: Machine learning, a now well recognized facet of AI, excels in discerning patterns within data. For example, machine learning can predict, based on contextual cues, that the processing time for a specific order is forecasted at 2 minutes, diverging from the average processing duration of 5 minutes leveraging the context at play. This form of AI becomes an indispensable tool when we are in the realm of execution.
• AI as Language and Learning Models (LLM): Powering systems akin to ChatGPT and Bing, AI-driven language and learning models enable users to engage with the system using natural language. Yet, the full potential of these AI-driven capabilities hinges on the context they are fed. Understanding the intricate interdependencies between orders, assets, personnel, capacities, availabilities, and more empowers AI to unfold its potential. This synergy finds its potential when combined with Metronome's graph model and real-time data ingestion capabilities, culminating in process orchestration at its best.

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