The Hidden Cost of Ignoring Process Optimization

Digitalization can deliver huge benefits for businesses, from streamlined operations to better data analytics and improved customer experiences. With the rise of cloud computing, the Internet-of-Things (IoT), data analytics and robotic process automation, it is vital to jump on the digital bandwagon. And it is now also clear that those who fall behind on AI will soon have a serious competitive disadvantage.

However, rushing into digitalization without first optimizing your underlying processes can be a costly and damaging misstep. Investing in advanced digital tools while neglecting process optimization is like building a house on shaky foundations—no matter how advanced the tools, the structure won’t stand strong.

Here’s why optimizing your processes first is crucial for maximizing your overall return on investments and securing long-term digitalization success:

1.     The Foundation Flaw

One of the biggest issues with digitalizing unoptimized operations is the increased difficulty of fixing foundational problems later. Once a digital layer is added, it integrates deeply into the workflow, making it more challenging and costly to address underlying issues without dismantling the entire system.

Think of it like building a house. If the foundation is unstable, adding new floors will only make it worse. Fixing these foundational problems later is far more expensive and disruptive than addressing them from the start. Similarly, in the context of business operations, this means higher costs, longer downtimes, and a significant risk of failure when trying to fix foundational issues after digital tools have been implemented. This is even more true for SMEs, where the resources that have integrated the new digital systems are often external. A flawed strategy here can lead to inefficiencies that are difficult to untangle and costs that will last long into the future.

2.     Trusting Faulty Information

Another big reason why digitalizing sub-optimal operations is illogical is the integrity of the data. When processes have flaws, they produce inaccurate, incomplete, or misleading data. Implementing state-of-the-art digital tools on this unreliable foundation means the insights and decisions based on this data will also be flawed.

For example, if your inventory management system has hidden waste and inefficiencies, the data fed into your new digital platform will reflect these issues. As a result, automated reorder points, demand forecasts, and inventory levels will be wrong, potentially leading to overstock, stockouts, and increased carrying costs. The old saying “garbage in, garbage out” holds true: the new systems will in a sense be ‘calibrated’ on a flawed starting point, and bad data will always lead to bad outcomes, no matter how advanced the new tool. The same flaws are still there—but now they are automated flaws.

3.     Invisible Problems

Digital tools often create a layer of abstraction between management and physical operations. This digital layer can obscure physical inefficiencies, making it harder to notice and address issues on the ground.

For instance, a digital dashboard might show that production targets are being met, but it won’t reveal that machines are being overworked, maintenance schedules are being ignored, or workers are struggling with outdated equipment. Over time, these physical issues can lead to increased wear and tear, unexpected breakdowns, and a gradual decline in overall efficiency. By focusing on digital solutions without first addressing physical realities, companies risk creating a disconnect that can lead to long-term problems.

4.     Compounding Complexity

And it’s not just that the digital layer can hide the physical issues below it—these issues themselves can make the data even more problematic. Adding digital tools to an already complex and inefficient system can actually amplify existing problems. Each new digital layer introduces additional variables and dependencies, increasing the overall complexity of the system. If the underlying processes are not streamlined and efficient, this added complexity can lead to more frequent errors, higher maintenance costs, and greater difficulty in troubleshooting

Consider a supply chain with inconsistent lead times, poor inventory management, and unreliable supplier performance. Implementing a sophisticated digital supply chain management system on top of these issues won’t solve them; it will just add another layer of complexity that must be managed. The result is a more cumbersome system that is harder to optimize and prone to greater failures.

5.     Wasting Time and Money

Investing in digital tools without first optimizing operations is not just an inefficient use of resources; it can be a costly mistake. Digital transformation initiatives require significant investment in terms of time, money, and human resources. If these initiatives are built on a flawed foundation, the returns on investment will be below expectations, and much of the resources spent will have been wasted.

On top of that, companies may find themselves continuously investing in patches and workarounds to address the symptoms of deeper issues that could have been resolved through process optimization. This reactive approach is far more costly and less effective than taking a proactive stance and ensuring that the foundational processes are robust before digitalizing. A sensible leader will recognize the strategic insight in addressing these foundational issues early: Fix Operational Flaws First, and spend your money wisely.

Conclusion: The Urgent Need for Process Optimization

In conclusion, the rush to digitalize without first optimizing foundational operations is not just illogical but potentially damaging. Hidden wastes, unreliable data, and unaddressed physical realities undermine the effectiveness of digital tools, while increasing complexity and misallocated resources make the problem worse.

For SMEs, the financial implications are clear. Optimizing operations before digitalizing really is a no-brainer: it ensures that digital tools are built on a solid foundation, leading to more reliable data, smoother integrations, and greater overall efficiency. The lasting efficiency gains and cost savings achieved through process optimization will quickly pay for their initial investment and create a robust foundation for your digital tools.

No matter where your company already is on the path of digitalization, the earlier the underlying operations are properly checked and optimized, the better. The forward-thinking strategist ensures that their digitalization efforts do not suffer a false start by implementing them on sub-optimal operations, to unlock their full potential. When is the last time your operations were accurately assessed and any inefficiencies were identified? Embrace the tried-and-tested methods for process optimization, and watch as your business reaps the rewards of both immediate improvements and long-term digital success.

Supplements

1.   Examples

2.   Approaches for optimization

3.   FAQ

Examples

The above issues of digitizing sub-optimal operations are a risk for companies at any scale. Here are some examples that show that also major professional players have sometimes learned this the hard way…

1. Lidl’s failed SAP Implementation (2011-2018)

Lidl, the German supermarket chain, attempted to replace its legacy inventory management system with a new SAP system. The project faced significant issues because the underlying business processes were not adequately optimized for such a transition. Lidl’s insistence on customizing the SAP software to fit its existing processes, rather than optimizing the processes, led to complications and inefficiencies. After seven years and nearly €500 million invested, Lidl ultimately abandoned the SAP project due to the ongoing issues and lack of expected improvements.

Source: Handelsblatt: “Lidl’s failed SAP project” (July 2018)

2. Ford’s ERP Implementation (2004-2006)

Ford attempted to streamline its global operations by implementing a centralized ERP system. However, the project faced major setbacks due to unoptimized processes and misaligned business practices across different regions. The ERP system struggled to handle these inconsistencies, leading to operational disruptions and delays. Ford ultimately scaled back the project, acknowledging that the lack of standardized and optimized processes contributed to the failure of the digitalization effort.

Source: InformationWeek: “Ford’s ERP system implementation issues” (April 2006)

3. Nike’s Inventory System Overhaul (2000)

Nike attempted to implement a new demand-planning software system to manage its inventory and supply chain. The goal was to improve forecasting accuracy and reduce inventory costs. However, the project faced major issues because the underlying supply chain processes were not fully optimized. The new system magnified existing problems, leading to significant inventory mismatches and missed sales targets. Nike’s failure to address these foundational issues before digitalization resulted in a reported $100 million in lost sales and a 20% drop in stock price.

Source: Computerworld: “Nike’s supply chain project failure” (June 2001)

4. Hershey’s ERP Implementation (1999)

Hershey implemented an Enterprise Resource Planning (ERP) system to integrate its supply chain, manufacturing, and customer order processes. However, the implementation coincided with several other major projects, such as new warehousing facilities and new processes. The simultaneous changes overwhelmed the company’s capacity to manage them effectively, leading to severe operational disruptions. The ERP system, built on these unoptimized and concurrently changing processes, resulted in delivery delays and order fulfillment issues during the critical Halloween and Christmas seasons. Hershey’s stock price dropped 8%, and they reported an 18% decrease in quarterly profits.

Source: CIO: “Hershey’s Halloween candy ERP failure” (November 1999)

5. Target Canada’s Supply Chain Issues (2013-2015)

When Target expanded into Canada, it aimed to use advanced digital tools for inventory and supply chain management. However, foundational issues such as incorrect data entry and poor inventory management practices led to disastrous results. The digital systems could not compensate for these basic flaws, resulting in stockouts, excessive inventory, and widespread customer dissatisfaction. The flawed operations led to Target Canada filing for bankruptcy in 2015, just two years after launching.

Source: CBC News: “Target’s supply chain and inventory management issues in Canada” (January 2015)

6. Boeing’s Production Scheduling Issues (2012)

Boeing experienced production delays and bottlenecks with the 787 Dreamliner due to inefficiencies and inaccuracies in their supply chain and production processes. The issues were partly due to inaccurate data from their global supply chain being fed into their planning systems, leading to scheduling and production challenges. This resulted in significant delays and increased costs, highlighting the importance of having optimized and accurate data processes before implementing advanced digital scheduling tools.

Source: Reuters: “Boeing 787 production issues” (2012)

7. Chipotle’s Quality Control and Reporting Issues (2015)

Chipotle faced significant quality control issues that led to multiple foodborne illness outbreaks at the end of 2015. The company’s quality control processes were inconsistent, and the flawed data collected did not trigger a reaction and was not effectively used to prevent the outbreaks. This resulted in a major public health crisis and financial losses for the company. The incident underscored the need for robust and optimized quality control processes and data management systems before relying on digital tools for monitoring and reporting.

Source: Fortune: “Chipotle’s E. coli outbreak” (2015)

Approaches for optimization

To address foundational issues, data inaccuracies, and flawed operations specifically before digitalization, several approaches can be effective.

Even though, unsurprisingly, many of these are part of LEAN methodologies, these approaches focus especially on identifying and correcting errors, inconsistencies, and gaps rather than on efficiency, as the most urgent areas to tackly before digitalization. Having said that, taking a more LEAN approach on your operations overall will not only be beneficial in general terms, but usually also simplify your digitalization projects.

1. Root Cause Analysis (RCA)

• Objective: Identify the underlying causes of problems and errors in operations.
• Approach: Use tools like the Five Whys, Fishbone Diagram (Ishikawa), and Failure Mode and Effects Analysis (FMEA) to systematically investigate issues.
• Benefit: Ensures that the fundamental problems are identified and addressed rather than just treating symptoms.

2. Total Quality Management (TQM)

• Objective: Focus on long-term success through customer satisfaction by continuously improving processes, products, and services.
• Approach: Implement comprehensive quality management practices across the organization, ensuring all employees are involved in the quality improvement process.
• Benefit: Helps in identifying and correcting quality issues that could lead to inaccurate data and operational flaws.

3. Gemba Walks

• Objective: Gain a deep understanding of the actual work processes and identify areas of concern directly on the shop floor.
• Approach: Managers and executives regularly visit the place where work is done, observe processes, and engage with employees to identify issues.
• Benefit: Direct observation and employee engagement help in uncovering inefficiencies and inaccuracies that might not be visible in reports.

4. Data Validation and Cleansing

• Objective: Ensure data integrity and accuracy before feeding it into digital systems.
• Approach: Implement data validation rules, regular audits, and data cleansing protocols to correct or remove corrupt or inaccurate records from a database.
• Benefit: Provides a reliable data foundation for digital tools, preventing the propagation of errors.

5. Standard Operating Procedures (SOP) Review

• Objective: Ensure that all processes are documented, standardized, and followed correctly.
• Approach: Review and update SOPs regularly, involve frontline employees in the review process, and conduct training sessions to ensure adherence.
• Benefit: Standardized procedures reduce variability and errors, ensuring consistent and accurate operations.

6. Benchmarking

• Objective: Identify best practices and set performance standards by comparing with industry leaders.
• Approach: Conduct comparative analysis with top-performing companies in the industry to identify gaps and improvement opportunities.
• Benefit: Helps in setting realistic improvement goals and identifying specific areas needing optimization.

7. Compliance Audits

• Objective: Ensure that all processes meet regulatory and industry standards.
• Approach: Conduct regular compliance checks and audits to ensure adherence to standards and regulations.
• Benefit: Helps in identifying non-compliance issues that could lead to operational flaws and inaccurate data.

8. Error Proofing (Poka-Yoke)

• Objective: Design processes in a way that prevents errors from occurring.
• Approach: Implement simple, fail-safe mechanisms in processes to prevent mistakes (e.g., jigs, fixtures, or software checks).
• Benefit: Reduces the likelihood of errors and defects, leading to more accurate and reliable operations.

9. Process Mapping and Analysis

• Objective: Understand and document all steps in a process to identify and correct issues.
• Approach: Create detailed process maps (flowcharts, SIPOC diagrams) to visualize and analyze each step.
• Benefit: Helps in identifying bottlenecks, redundancies, and areas where errors may occur.

10. Kaizen Events Focused on Stability

• Objective: Focus on creating stability in operations before seeking efficiency improvements.
• Approach: Conduct focused, short-term projects aimed at stabilizing processes, reducing variability, and addressing basic operational issues.
• Benefit: Provides a stable foundation for further process improvements and digitalization efforts.

By applying these methodologies, businesses can address foundational flaws, improve data accuracy, and stabilize operations, thereby creating a solid foundation for successful digitalization.

These approaches ensure that digital tools and systems are built on reliable processes, maximizing their effectiveness and return on investment.

FAQ

1. Digital Tools That Optimize Operations Automatically

“How about a digital product that also analyses my operations and identifies inefficiencies for me? Wouldn’t it make sense to implement that first?”

It is important to differentiate between process efficiency optimization and process reliability optimization. For digitalization, it is especially the latter that is crucial. While it’s true that some advanced digital tools come with built-in analytics to identify inefficiencies, they are most effective when they operate on clean and accurate data. If your underlying processes are flawed, the data feeding these tools will be unreliable, leading to inaccurate analysis and suboptimal recommendations. Optimizing the reliability of your processes first ensures that the data and the insights generated by digital tools are trustworthy, maximizing their effectiveness.

2. Urgency to Keep Up with Competitors

“We need to digitalize quickly to keep up with our competitors. Can we really afford to wait?”

Rushing into digitalization without optimizing your processes may provide a short-term competitive edge, but it can lead to long-term problems that are costlier and more difficult to fix. Competitors who build their digital strategies on optimized processes will ultimately achieve greater efficiency and reliability, putting them ahead in the long run. Taking the time to optimize first will save you from potentially disruptive and expensive fixes down the road.

Plus, many process optimization projects take just months, not years. And often, the information gathered during such projects can be helpful input to a following digitalization project, making its implementation more efficient.

3. Complexity of Process Optimization

“Optimizing processes sounds complex and time-consuming. Isn’t it easier to just go digital and fix problems as they come up?”

While process optimization requires effort, it is a well-established practice with many tried-and-tested methodologies that can yield fast results. The complexity and cost of fixing problems after digitalization are often much higher than addressing them upfront. A strategic approach to process optimization will streamline your operations and make the subsequent digitalization smoother and more effective.

4. Belief in the Power of Technology

“With the rapid advancements in technology, aren’t modern digital tools sophisticated enough to handle any inefficiencies?”

Modern digital tools are indeed powerful, but their effectiveness is fundamentally tied to the quality of the processes they are built upon. If underlying processes are flawed, even the most advanced tools can struggle to deliver optimal results. Ensuring that your processes are reliable and optimized allows these tools to function at their best, providing accurate insights and achieving the intended benefits.

5. Resource Constraints

“We have limited resources and can’t afford to invest in process optimization and digitalization separately. Which one should we prioritize?”

Optimizing processes should be prioritized because it not only lays the groundwork for successful digitalization, but often the initial investment in process optimization will quickly pay for itself through cost savings and efficiency gains, making it easier to justify and finance subsequent digitalization efforts. By fixing operational flaws first, you ensure that your limited resources are used wisely, maximizing the return on investment from digital tools.

6. Immediate Benefits of Digital Tools

“We need quick wins and immediate benefits. Won’t digital tools provide those faster than process optimization?”

You also need these wins to be lasting wins. Digital tools can offer quick wins, but these are often superficial and short-lived if the underlying processes are flawed. Fixing process reliability might not always provide instant results, but it creates a stable foundation that ensures the benefits from digital tools are sustainable and long-lasting. Quick wins from digital tools without process optimization can lead to bigger problems down the line, resulting in wasted resources and efforts.

7. Vendor Promises

“Our digital tool vendors assure us that their solutions can handle inefficiencies and optimize processes. Shouldn’t we trust their expertise?”

While vendors may promise that their tools can handle inefficiencies, they will usually admit that they rely on the assumption that your data and processes are already relatively clean and stable. Vendors’ tools are designed to enhance and optimize, not to fix foundational issues. It’s important to critically evaluate these claims and ensure your processes are optimized so that you can fully leverage the vendor’s technology effectively.

8. Custom Solutions

“Can’t we develop custom digital solutions tailored to our specific inefficiencies? Why optimize first?”

Custom digital solutions can be highly effective, but they are only as good as the data and processes they are built on. If foundational issues are not addressed, custom solutions might be designed around flawed processes, leading to persistent inefficiencies. Optimizing your processes first ensures that any custom solutions developed are addressing real, well-understood problems and not just symptoms of deeper issues.

9. Incremental Improvement

“Can’t we optimize processes incrementally while implementing digital tools? Why separate the two efforts?”

While incremental improvement is a valid strategy, in line with the Continuous Improvement methodology, trying to optimize processes while simultaneously implementing digital tools can be chaotic and counterproductive. It can lead to confusion, conflicting priorities, and implementation delays. A phased approach, where process optimization is tackled first, provides a clear roadmap and stable foundation for digitalization, leading to more seamless and effective implementation.

10. Perceived Redundancy

“Isn’t process optimization redundant if our goal is to overhaul everything with digital transformation?”

Process optimization is not redundant; it is a critical step in ensuring that digital transformation efforts are successful. Without optimized processes, a digital overhaul can lead to automating inefficiencies and creating more complex problems. Process optimization provides clarity and a streamlined workflow that makes the digital transformation smoother, more efficient, and more likely to succeed. It ensures that the overhaul addresses the right problems and leverages technology to its fullest potential.