Supply Chain

Using AI to Drive Down Supply Chain Costs and Safeguard Customer Fulfillment Experiences

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The top driver for change in supply chains is the need to retain customers yet the least important element of a supply chain is the quality of service, according to a 2016 IDC Manufacturing Insights supply chain survey. Looking at priorities through 2017, reducing costs and eliminating waste was the top priority and customer-centricity was ranked fourth (60% versus 28%).

Improving retention through a superior customer experience has been a top mandate in enterprises for several years, so it isn’t surprising that the sentiment is also expressed by supply chain executives. But it is surprising to see the low priority placed on becoming more customer-centric. After all, a customer’s most immediate perception of a business is the quality of its service. To a physical supply chain, that means accurate and on-time fulfillment.

In a business network when cost and service level commitments are roughly the same among competitors, the sourcing decision tilts to the trading partner with the best reputation for meeting its commitments.

Click here to learn about how IBM is bringing Watson to the supply chain.

Supply Chains Focused on Preventing Mistakes Improve Customer Retention and Lower Costs

If a supply chain is engineered for customer-centricity, preventing mistakes would be a top priority. Measured by end-to-end performance, top line benefits from improved retention and improved reputation would grow revenue and the top supply chain priority of reducing costs would also be met.

Fulfillment mistakes are expensive because they:

  • Slow down cycle times for the customer and all trading partners involved with the transaction.
  • Increase labor costs when the mistake causes idle time.
  • Increase labor costs because investigating the mistake and determining how to correct it are manual activities that involve communications between multiple parties.

Even something as simple as identifying an ASN that doesn’t have a matching acknowledgement causes idle time that is entirely preventable with customer-centric design.

And measuring the performance of trading partner SLAs is also captured with a customer-centric supply chain. Providing SLA visibility to trading partners creates a climate of continuous improvement while also making it easier to make sourcing decisions.

Cognitive Computing Enables Affordable Customer-Centric Focus

It’s easy to write about customer-centric design, but anyone with experience attempting this for the supply chain knows that constructing the models that capture performance has traditionally been complex and expensive, and continuous data collection is challenging.

Advances in AI along with AI-based software-as-a-service solutions have changed the economics of building systems that produce insights about performing against customer SLAs. This is particularly beneficial in business networks that automatically collect a rich set of data about individual business transactions.

Whether the documents exchanged with trading partners use EDI, JSON, digital fax, or PDF, AI is used to extract relevant data, correlate the data and provide insights and predictions about end-to-end performance. The earlier a mistake is detected using AI, the greater the possibility that problems can be remediated without causing a customer-facing mistake.

This is light years ahead of previous efforts to gain insights into business network performance. These efforts were based on manually constructing models, manually populating the models with the contractual SLAs across all trading partners, investing in expensive technology to transform data into a normalized format for use by the models and then manually managing the system to accommodate changes that occur continuously.

Unlike models that are constructed through the development of fixed metrics and assumptions, the quality of AI-based insights improves as more and a greater variety of data pour into the system. Examples of use of AI to improve customer fulfillment experiences:

  • Automatic computation of trading partner SLAs.
  • Making predictions downstream that signal a problem in meeting upstream service.
  • AI is used to identify what actions to take to solve the problem. Depending on the confidence about the next best action prediction, automation may be triggered, or supply chain experts and customer relationship managers may review a series of options generated by the analytics to determine what sequence of steps should be taken to improve the outcome and manage the customer relationship.
  • Continuous assessment of partner quality helps to identify which partners to use under different conditions in the short term and which ones to keep or drop over time.

By leveraging and supplementing existing sources of trading data with cognitive analytics, enterprises have an opportunity to differentiate by focusing on the world class delivery of customer service — not just by being nice to people when they call in with a problem — but by preventing the problem from occurring and making good proactively when a bad outcome is unavoidable.

Click here to learn more about how IBM is applying its cognitive technology to supply chain visibility.

Program Vice President, Business Process Management and Middleware

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