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 [email protected]

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 [email protected]

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.

If you like this blog, please share it or comment.

All the best,

Richard Sharpe
CEO – Competitive Insights

Big Data is something that companies are trying to define with regard to what it means to their operation and to their competitive landscape. When considering the growing number of sources of unstructured data (e.g. social media) and structured data, just defining the landscape of what you are talking about can be difficult. In this blog we have provided a framework for how to define Big Data, getting value from Big Data and now providing actionable points on how to turn a three headed monster into something that adds significant and ongoing business value.

In an earlier posting we identified the requirement to gain cross functional consensus with regard to how Big Data solutions are created to serve the intended purpose of solving a business problem(s). We also focused on why it is so important to take an enterprise wide perspective to maximize the value of the investment. In this posting, we will focus on the importance of data governance.

What does data governance have to do with Big Data?

Everything. Effective solutions take time and resources to build correctly. The question is do you want that investment to solve a business problem one time or to continue to support solving the business problem over time.

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.

Ok, so what does that have to do with Data Governance?

Let’s say that you have a significant business problem to solve. The information that you have in front of you is known to be consistently accurate and specific to the problem area. This is a function of the information coming from the same source, that the information has been verified by Subject Matter Experts (SMEs) and the way that it has been processed is consistent to provide the information you need. What did I just describe? Data Governance; the insurance policy for Big Data, and this insurance policy continues to provide returns as you measure the impact of those decisions over time.

Data Governance provides the rules for which you are obtaining, organizing, validating and processing the vast amounts of structured and unstructured data to gain competitive advantage. Without it, your Big Data investments are a waste of time.

If you like this blog, please share it or comment.

All the best,

Richard Sharpe
CEO – Competitive Insights

In order for any analytical solution to be meaningful, the issue of data quality needs to be addressed. The goal is to demystify the whole notion of harnessing Big Data analytics from being a seemingly impossible, daunting task to one of creating an invaluable asset that drives significant financial performance improvements.

Case Study

While I was the President of CAPS Logistics for over 8 years, the company serviced over 16% of the Fortune 500 in various forms of supply chain decision support applications. These efforts spanned the globe from supporting the expansion efforts of a worldwide soft drink company, to the redesign of supply chain networks for multiple CPG and specialty manufacturing companies and providing different transportation solutions to one of the largest companies in the waste management industry. The technology was proven, the people were bright and the focus was to provide the best solutions possible. Unfortunately, in most cases, the implementation of these carefully engineered solutions was either slowed down or even put on hold. Why? Other departments would offer objections often citing concerns about the “quality” of data used for the analysis. Typically, the nail in the coffin would be that if they would offer that their data concerns were correct, the integrity of the solution could cause the company to miss its revenue targets. Sound familiar? The end result was that the company did not attain the competitive advantage that was possible.

Let’s take this lesson to the world we live in today

In a world of exponentially growing data associated with the operation of your enterprise the problem sited above had two issues. The “quality” concern which will be addressed in a future posting on data governance. The second issue was not having cross-functional consensus. In the projects mentioned above, efforts were made to involve other departments at every stage of the project but gaining supply chain efficiencies were not their highest priority. Therefore, these other functional groups had no real buy-in in the work effort.

If you believe that Big Data has the opportunity to drive significantly recurring financial improvements, then the table stakes are even higher with regard to early organizational buy-in. Buy-in with regard to getting everyone on the same page on how to repurpose and use Big Data to empower multiple forms of new analytical and strategy development capabilities. I am guessing that many of you are saying “yea, right for our company; this would be comparable to building Noah’s Ark.” You may be right but it is a challenge that deserves careful consideration as offered by the following quote by Benjamin Franklin:

“By failing to prepare,
you are preparing to fail.”

Success will also require having the right level of Executive sponsorship and taking advantage of the power of cloud technologies and proven methodologies. In addition, other key points need to be integrated into your approach including obtaining cross functional consensus on any data issues. If this is not a mandate, you run the serious risk that organizational questions will surface which can stop the entire effort.

So why go to the effort?

Isn’t the political risk too significant and the task too big to undertake now especially given all of the mission critical projects that are already established for the company? That might be the right answer but let’s look at it another way. Is it possible that your Board and certain members of your Executive Team are wondering if the company will be left behind when your competitors harness the value of Big Data? Value that is measured by increasing financial performance and competitive advantage. Only you know the answer to that question, but if the decision is made to take the first move advantage, be sure the process you use requires cross functional organizational consensus as a cornerstone of the solution.

If you like this blog, please share it or comment.

All the best,

Richard Sharpe
CEO – Competitive Insights