• Step 3 – Prescriptive Supply Chain: moving towards the “DETERMINISTIC”
state whereas through the creation of DIGITAL TWINS, the reinforcement
learning AI solutions, we have supply planning “prescribed and recommended”
by the system (upon human final decision).
• Step 4 – Self-Driving Supply Chain: the last step in which the overall balancing of
supply vs demand as well as distribution and logistics is totally AUTONOMOUS.
Figure 2 shows the step-by-step approach:
6.2 What Is the Technological Backbone?
Nearly all Pharma companies (and not only) come from a world whereas “ERP is the
king.” ERP were the systems needed to reduce operational costs, to drive efficiencies
through effective data gathering, and to leverage synergies by standardizing processes. While this is (was) generally true, true E2E-Supply Chain-level decisionmaking was never enabled. Hence the key reason why the end-to-end digital supply
chain twin is an important milestone: these new systems do sit “above” any ERP or
data gathering source and their analytical layer provides prescriptive insights into the
interconnected decisions that are inherent in supply chains (Fig. 3).
At its core is a digital supply chain twin is:
• Connected outside-inside
• Gather and process info real-time (is always “ON”)
• Autonomous
• Intelligent
Fundamentally, such a system allows the flexibility for performing simulation
scenarios and modeling that can be evaluated without having to necessarily conform
to the design constraints of your current supply chain nor impacting the operational
activities.
As-is: Siloed
Retrospective
• Silos of raw data
• Limited visibility
• Inaccurate
Step 1
Integrated
Real-Time
• End2End visibility
• One Number
Concept
• SINGLE source of
Truth
Step 2
Predictive
Forward Looking
• Advanced Analytics
Forecast
• Machine Learning
• Demand Sensing
Step 3
Prescriptive
Deterministic
• Al driven
prescriptive supply
• Reinforcement
Learning though
DIGITAL TWIN
Step 4
Self Driving
Autonomous
• Al - Closed Loop
(CLM)
• Deep Learning
• SDO
interconnectivity
THE JOURNEY:
Fig. 2 The journey to implement the vision of a Self-Driving Supply Chain
Digital Twins: A General Overview of the Biopharma Industry
179
state whereas through the creation of DIGITAL TWINS, the reinforcement
learning AI solutions, we have supply planning “prescribed and recommended”
by the system (upon human final decision).
• Step 4 – Self-Driving Supply Chain: the last step in which the overall balancing of
supply vs demand as well as distribution and logistics is totally AUTONOMOUS.
Figure 2 shows the step-by-step approach:
6.2 What Is the Technological Backbone?
Nearly all Pharma companies (and not only) come from a world whereas “ERP is the
king.” ERP were the systems needed to reduce operational costs, to drive efficiencies
through effective data gathering, and to leverage synergies by standardizing processes. While this is (was) generally true, true E2E-Supply Chain-level decisionmaking was never enabled. Hence the key reason why the end-to-end digital supply
chain twin is an important milestone: these new systems do sit “above” any ERP or
data gathering source and their analytical layer provides prescriptive insights into the
interconnected decisions that are inherent in supply chains (Fig. 3).
At its core is a digital supply chain twin is:
• Connected outside-inside
• Gather and process info real-time (is always “ON”)
• Autonomous
• Intelligent
Fundamentally, such a system allows the flexibility for performing simulation
scenarios and modeling that can be evaluated without having to necessarily conform
to the design constraints of your current supply chain nor impacting the operational
activities.
As-is: Siloed
Retrospective
• Silos of raw data
• Limited visibility
• Inaccurate
Step 1
Integrated
Real-Time
• End2End visibility
• One Number
Concept
• SINGLE source of
Truth
Step 2
Predictive
Forward Looking
• Advanced Analytics
Forecast
• Machine Learning
• Demand Sensing
Step 3
Prescriptive
Deterministic
• Al driven
prescriptive supply
• Reinforcement
Learning though
DIGITAL TWIN
Step 4
Self Driving
Autonomous
• Al - Closed Loop
(CLM)
• Deep Learning
• SDO
interconnectivity
THE JOURNEY:
Fig. 2 The journey to implement the vision of a Self-Driving Supply Chain
Digital Twins: A General Overview of the Biopharma Industry
179