Supply Chain Optimization

Data-driven optimization is the new age supply chain optimization. Let’s consider the example of a hair salon. If it can keep the history of its existing customers, sales as per the time slots, missed opportunities and can collect the data of weather, local events, they would be able to predict the demand. Then they can schedule their stylists accordingly.
So if a hair salon has dozens of data points to consider, imagine the scope for a retail company (Walmart/Amazon) or auto manufacturer (Tesla/GM). Data management is the only possible way to achieve supply chain optimization.

Challenges in dealing with data

Any organization uses various applications to manage its operations. A company stores sales data in an ERP system, customer information is in a CRM system, and employee information is in the HR system. If a company has recently merged with another company, it makes matters more complicated. Each of them has their systems so sales data may exist in two different ERP systems of varying brand like SAP, Oracle, Peoplesoft, etc.

So existing Supply chain data-warehouse or data lake might not have all the data required for creating a predictive model. Even though when the data is there, it is not structured correctly and understood. Data Scientists and analysts have to spend many hours discovering data and incorporate it into the model.

We have divided the challenges into two categories:

 

  • Challenges in developing or enhancing the supply chain optimization model
  • Challenges in operationalizing a model into production

Challenges in developing or enhancing the supply chain optimization model

  • A company stores data in myriad systems
  • Not all employees know their data and how to interpret it
  • There are differences in working and opinion between the application team and the supply chain optimization team
  • Who is a data steward of a specific dataset (table or file) is not common knowledge
  • Getting support across the organization is difficult within the project timeline.
  • Once the data is found, it takes a long time to move it to a warehouse where it can be analyzed
  • Involvement of IT in data movement makes dependencies larger and things slow
  • Cleaning up all the data is a daunting task

Challenges in operationalizing the model

  • The Supply chain data warehouse is not able to scale, and queries are taking a long time to complete
  • Data movement takes a long time
  • If a query fails by any reason, its impact is not known

The Solution

An organization can deal with most of the challenges faced in creating a model by incorporating a data catalog. The team should devote a week or two to organizing all the data in a data catalog. Next, the organization should conduct a workshop for the SCO team.
If the data catalog is an IT department initiative then multiple teams (Supply Chain team, M&A team, Analytics team) can benefit from it. The more teams use the data catalog, the better for the whole organization.
A data catalog enables the supply chain team to look at the data comprehensively and understand it quickly. Now the team can smoothly discover the data sources they need. Once they have identified these sources and data, they can bring all the data into a central and dynamic data lake.  Then they can perform an in-depth analysis.

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