This posting is part of a three part series on smartly avoiding the AI Hype Cycle. Part 1 of this series “Have a Clear, Intentional Focus on the Business Problem You Want to Solve” can be accessed here.

Creating a foundation of repeatable data integrity is absolutely essential to have a sustainable AI solution that provides ongoing value. However, companies continue to struggle with data issues. According to Gartner:

“Less than half of data and analytics (D&A) leaders (44%) reported that their team is effective in providing value to their organization.”

Companies have made investments to improve their data environment but still have not realized the attributes that they need. For data to be actionable it must be Accurate, Specific, Trusted and Repeatable. Instead, companies discover that their data remains Siloed, Fragmented, Missing or the right data is Difficult to Obtain. The required transformation is depicted in the following diagram:

Lisa Harrington, President of the Harrington Group, summarizes the issue with the following:

“Harnessing the true power of data driven insights is the holy grail of future business. A wealth of this data comes from the supply chain. But, while the information is there, companies are not yet capitalizing on its real value as a source of insight capable of shaping the future of the enterprise.”

Let’s further define the characteristics in the diagram above that are required to create a foundation of repeatable data integrity. The data must be:

• Accurate – use a 3-step process incorporating Machine Learning to ensure the accuracy of supply chain data;
• Data Recognition – repeatable data sources have been agreed upon
• Validation – detailed data values and data patterns have been analyzed
• Verification – functional transactional data has been approved by SME’s
• Specific - minimize data approximations or allocations whenever possible
• Trusted - take the time to verify the organization trusts the information that you are using for your AI solution
• Repeatable - require your data solution to provide for frequent refreshes that meet the other three requirements

One aspect of AI, Machine Learning (ML), positions companies to harness the full potential of their supply chain related data. In the attached video from a recent Georgia Tech conference, Brian Greene offered the following:

The frustration associated with data issues can be solved. Ask any Executive if they would like to have better, more insightful, cost and profit performance visibility based on trusted and repeatable data. I guarantee the answer will be YES!

The power of AI can deliver cost and profit performance visibility that continually adds “disruptive” competitive advantage.

The third part of this series will address Manage the Adoption of AI/ML Solutions to Provide Rapid, Repeatable and Actionable Results.

We would love to hear your thoughts and comments. Please feel free to reach me directly at rsharpe@ci-advantage.com or visit our website at www.ci-advantage.com.

All the best,

Richard Sharpe
CEO – Competitive Insights

Many innovative companies are supporting their supply chain leaders in spearheading approaches to capitalize on Artificial Intelligence (AI) and Machine Learning (ML) to reduce costs and protect operating margins.

However, laggard companies are experiencing internal resistance to adopting solutions that deliver accurate and specific SKU, Customer and Channel cost and profit performance insights.

The following statements come from hundreds of frustrated Supply Chain Executives:

1. Our data is bad, missing, fragmented, siloed and managed in different systems.
   The power of AI and ML can be harnessed to eliminate the burden of connecting, validating and transforming data from multiple operating systems. In addition, significant data validation can be done efficiently by pinpointing possible data issues to Subject Matter Experts within a company.
2. We have a hold on outside expenditures.
   Cost containment is a primary mandate for companies during inflationary business cycles. This unfortunately means that companies try to maintain their current operation by reducing any possible discretionary expense. This position indirectly discourages the use of innovation to reduce costs. Having the knowledge of where to focus on the most effective cost reductions will provide far more opportunities than just “tightening the belt”.
3. We are just too busy and have very limited resources.
   AI / ML advancements can greatly simplify the ability to have unified and trusted information that can be used cross-functionally to make better enterprise-based decisions instead of departmental, siloed decisions.
4. We can do this ourselves.
   Companies that undertake this type of development effort often experience unanticipated, longer development timeframes and higher personnel costs. Business Intelligence (BI) tools are very useful but are not designed to handle the required volume of transactional data (typically 4 billion+ transactions). The result is often organizational frustration and extended timeframes to achieve significant ROIs.
5. We tried doing this before and it didn’t work.
   Only one or two companies have the supply chain, data analytics and data governance knowledge and experience. Add the need for AI/ML knowledge and it clear why internal efforts fail.

The companies that are successful at creating specific, accurate and actionable cost and profit performance insights will be the winners in the next decade. Companies must overcome organizational roadblocks to taking advantage of ongoing AI/ML advancements like generating specific, accurate and actionable cost and profit performance insights.

The reality is that having this type of information on a continuous basis has become table stakes to accelerating a company’s growth and gaining market share. Allowing excuses to prohibit the adoption of these strategic advancements is a dangerous competitive disadvantage.

Please comment on this posting or email me at rsharpe@ci-advantage.com

All the best,

Richard Sharpe
CEO – Competitive Insights

“Harnessing the true power of data driven insights is the holy grail of future business. A wealth of this data comes from the supply chain. But, while the information is there, companies are not yet capitalizing on its real value as a source of insight capable of shaping the future of the enterprise.”

Lisa Harrington – President, lharringtongroup.com

Companies are struggling to use data and analytics to continually find ways to reduce operating costs and protect margins. According to Gartner, “less than half of data and analytics (D&A) leaders (44%) reported that their team is effective in providing value to their organization”.

So why are so many companies still struggling with the adoption of Machine Learning / Artificial Intelligence (ML/AI) technologies to handle today’s inflationary pressures? The reasons can vary but some of the most common complaints are:

Our data still sits in silos and it is difficult to integrate.

We have pulled all our data together but people still don’t trust it.

As a large company, we have a long way to go to be able to support advanced analytics with the current state of our data.

Case In Point: A meeting was held with the CFO, COO and SVP of Supply Chain for a well-known apparel company. They knew that they needed to build analytical capabilities, but were skeptical because of their perception of the current state of their data. Fortunately, the SVP of Supply Chain had previous experience in working with a solutions provider in tackling this issue. He convinced the others to take a first step that would demonstrate that their data could be turned from a liability to an asset to produce meaningful insights on opportunities to reduce costs and increase profit margins.

Action: A Project Team was assembled and all sources for their supply chain and sales transactional data were identified. Data Subject Matter Experts were involved to address any data issues. Consensus was reached by the Team on how the data should be intentionally transformed to build a foundation for SKU and Customer specific cost and profit performance information. ML/AI technology was then used to further validate data quality problems and to create specific and actionable financial performance insights that could be refreshed periodically. The Project team found a potential inventory working capital reduction in excess of $10 million dollars.

Data integrity issues can be proactively addressed to ensure that operating data becomes a valuable asset. An asset that allows for visibility into actionable insights that drive trusted, fact-based decisions. The companies that take this seriously will consistently move ahead of their competition and drive additional cost reductions and profitable performance.

Please comment on this posting or email me at rsharpe@ci-advantage.com

All the best,

Richard Sharpe
CEO – Competitive Insights

Summary: It is rare to talk to a company that believes they have very little challenges with their data. Unfortunately, many companies use their data integrity issues as a crutch to justify why they are not getting true value from business analytics.

Case In Point: Recently, Competitive Insights (CI) facilitated a meeting with the CFO, COO and SVP of Supply Chain for a well-known apparel company. The Executives knew that to proactively address inflationary pressures to reduce costs and complexity, they needed accurate and specific Cost-to-Serve and Profit Performance insights by Product, Customer and Channel but were skeptical because of concerns about the current state of their data.

Action: Fortunately, the SVP of Supply Chain convinced the other Executives to take a first step that would demonstrate that their data could be turned from a liability to an asset and produce meaningful financial insights to reduce costs and increase profit margins. He emphasized the need to efficiently:

• Obtain, validate and transform all relevant end-to-end transactional data
• Ensure cross-functional buy-in to the Cost-to-Serve and Profit performance insights
• Provide for monthly updates accessible in an easy and intuitive format

Problems are solved by business leaders and managers making decisions that positively impact the efficiency and financial impact of the operation. Decisions that are fact based and that are actionable.

Results: Working with CI, all relevant transactional data was identified. Consensus was reached on how the data should be intentionally validated and transformed to build accurate and specific Product (SKU) and Customer Cost to Serve and Profit information that was far more granular than provided by their P&L. The following actionable financial opportunities were identified:

• Out of 142,493 Products using 875 Colors, only 53 colors provided 80% of their profits
• Segmenting Products and Customers by financial performance revealed that a very small number of Products and Customers contributed 96% of their true operating margin
• Significant opportunities were identified to reduce operating costs by focusing on the large number of Unprofitable and Marginal Customers buying unprofitable Products
• Inventory Working Capital reduction opportunities in excess of $10 million dollars were identified

Takeaway: Data integrity issues can be proactively handled to ensure that operating data becomes a valuable asset. An asset that allows for visibility into actionable insights that drive trusted, fact-based decisions. The companies that take this seriously will consistently move ahead of their competition and drive additional cost reductions and profitable performance.

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All the best,

Richard Sharpe
CEO – Competitive Insights