Supply Chain Analytics
Wondering how your nearest grocery store is still full of your favourite black bourbon, despite the ongoing lockdown? Not to worry, we are here to clear all your doubts. The answer is Supply Chain Management, a simple concept with dynamic applications.
Supply chain management refers to managing collections of suppliers to manufacture a product for the company, right from procurement of raw material to manufacturing the final product and its distribution to the consumer.
Nucleus without analytics is incomplete and so is supply chain without analytics. This brings us to the concept of Supply chain analytics. Being an important domain in business, it helps in making the supply network more accurate, clear and insightful.
The advanced form of supply chain analytics is at a forefront in the new era of supply chain optimization. It automatically uses a large amounts of data to help an organization improve forecasting, identify inefficiencies, respond better to customer needs, drive innovation and pursue breakthrough ideas
PepsiCo is a well known company that uses huge volumes of data for an efficient supply chain management. The company’s clients provide reports that include information about their warehouse inventory and the POS inventory to the company, and this data is used to reconcile and forecast the production and shipment needs. This way, the company ensures retailers have the right products, in the right quantity and at the right time.
“Intelligence is the ability to adapt to change.”
Organisations throughout the world are intelligent, they have effectively employed supply chain analytics at the place. But why do we even need to adapt this change? Simply because we are intelligent.
Scope of Supply Chain Analytics
Different types of supply chain analytics include-
Descriptive analytics. It tells how much money is invested in inventories, customer service level, fill rate, average lead time etc. So, accurate databases are provided that are statistically processed for accurate results. Your nearby shopkeeper from his experience knows it well that you buy a jar of coffee each month, so when he is placing a order for that he considers the historical demand of each customer and orders accordingly.
Predictive analytics. Helps an organization understand the most likely outcome of a business problem or future scenario and its business implications. For example, using predictive analytics organization can project and mitigate disruptions and risks, estimate future demand etc.
Prescriptive analytics. Helps organization to collaborate with their logistic partners and other external stakeholders to maximize the business value by prescribing a strategy for the same.
Cognitive analytics. Analytics often mimics human brain and thought process. Cognitive analysis helps in answering complex business problems in natural language i.e. in the same way as a human being would do. Cognitive technologies understand, reason, learn and interact like a human, but at much larger capacity and speed. Moreover, AI has the ability to establish network orchestration, which will exponentially increase supply chain velocity by using algorithmic decision-making and automated execution.
Planning is important, not the plan. Similarly, it’s the methods of supply chain management that must be focused upon and the results will follow. Common methods of supply chain analytics are–
Existing methods ranging from basic statistical techniques to simulation modeling and machine learning algorithms help in forecasting demand etc. Finding key factors that effect supply chain by combining the strengths of unsupervised, supervised and reinforcement learning. This helps in focusing more on the important factors and improves decision making process. Optimizing freight costs, improving supplier delivery performance,determining optimum order size and minimizing supplier risk are few of the many benefits machine learning is providing in collaborative supply chain networks. Machine learning is also proving to be very effective at automating inbound quality inspection throughout logistic processes, isolating product shipments with damage and wear. The machine learning algorithms in IBM’s Watson platform is one of the most common example of supply chain analytics. It was able to determine if a shipping container or product were damaged, classify it by damage time, and recommend the best corrective action to repair the assets. Machine learning is improving production planning and factory scheduling accuracy by taking into account multiple constraints and optimizing for each.
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