87% of data science projects never make it beyond the initial vision into any stage of production. Even some that pass-through discovery, deployment, implementation, and general adoption fail to yield the intended outcomes. After investing all that time and money into a data science project, it’s not uncommon to feel a little crushed when you realize the windfall results you expected are not coming.
Yet even though there are hurdles to implementing data science projects, the ROI is unparalleled – when it’s done right.
Coca-Cola has used data from social media to identify its products or competitors’ products in images, increasing the depth of consumer demographics and hyper-targeting them with well-timed ads.
You can accelerate your production timelines.
GE has used artificial intelligence to cut product design times in half. Data scientists have trained algorithms to evaluate millions of design variations, narrowing down potential options within 15 minutes.
With all of that potential, don’t let your first failed attempt turn you off to the entire practice of data science. We’ve put together a list of primary reasons why data science projects fail – and a few strategies for forging success in the future – to help you avoid similar mistakes.
You lack analytical maturity.
Many organizations are antsy to predict events or decipher buyer motivations without having first developed the proper structure, data quality, and data-driven culture. And that overzealousness is a recipe for disaster. While a successful data science project will take some time, a well-thought-out data science strategy can ensure you will see value along the way to your end goal.
Effective analytics only happens through analytical maturity. That’s why we recommend organizations conduct a thorough current state analysis before they embark on any data science project. In addition to evaluating the state of their data ecosystem, they can determine where their analytics falls along the following spectrum:
Descriptive Analytics: This type of analytics is concerned with what happened in the past. It mainly depends on reporting and is often limited to a single or narrow source of data. It’s the ground floor of potential analysis.
Diagnostic Analytics: Organizations at this stage are able to determine why something happened. This level of analytics delves into the early phases of data science but lacks the insight to make predictions or offer actionable insight.
Predictive Analytics: At this level, organizations are finally able to determine what could happen in the future. By using statistical models and forecasting techniques, they can begin to look beyond the present into the future. Data science projects can get you into this territory.
Prescriptive Analytics: This is the ultimate goal of data science. When organizations reach this stage, they can determine what they should do based on historical data, forecasts, and the projections of simulation algorithms.
Your project doesn’t align with your goals.
Data science, removed from your business objectives, always falls short of expectations. Yet in spite of that reality, many organizations attempt to harness machine learning, predictive analytics, or any other data science capability without a clear goal in mind. In our experience, this happens for one of two reasons:
1. Stakeholders want the promised results of data science but don’t understand how to customize the technologies to their goals. This leads them to pursue a data-driven framework that’s prevailed for other organizations while ignoring their own unique context.
2. Internal data scientists geek out over theoretical potential and explore capabilities that are stunning but fail to offer practical value to the organization.
Outside of research institutes or skunkworks programs, exploratory or extravagant data science projects have a limited immediate ROI for your organization. In fact, the odds are very low that they’ll pay off. It’s only through a clear vision and practical use cases that these projects are able to garner actionable insights into products, services, consumers, or larger market conditions.
Every data science project needs to start with an evaluation of your primary goals. What opportunities are there to improve your core competency? Are there any specific questions you have about your products, services, customers, or operations? And is there a small and easy proof of concept you can launch to gain traction and master the technology?
The above use case from GE is a prime example of having a clear goal in mind. The multinational company was in the middle of restructuring, reemphasizing its focus on aero engines and power equipment. With the goal of reducing their six- to 12-month design process, they decided to pursue a machine learning project capable of increasing the efficiency of product design within their core verticals. As a result, this project promises to decrease design time and budget allocated for R&D.
Organizations that embody GE’s strategy will face fewer false starts with their data science projects. For those that are still unsure about how to adapt data-driven thinking to their business, an outsourced partner can simplify the selection process and optimize your outcomes.
Your solution isn’t user-friendly.
The user experience is often an overlooked aspect of viable data science projects. Organizations do all the right things to create an analytics powerhouse customized to solve a key business problem, but if the end users can’t figure out how to use the tool, the ROI will always be weak. Frustrated users will either continue to rely upon other platforms that provided them with limited but comprehensible reporting capabilities, or they will stumble through the tool without unlocking its full potential.
Your organization can avoid this outcome by involving a range of end users in the early stages of project development. This means interviewing both average users and extreme users. What are their day-to-day needs? What data are they already using? What insight do they want but currently can’t obtain?
An equally important task is to determine your target user’s data literacy. The average user doesn’t have the ability to derive complete insights from the represented data. They need visualizations that present a clear-cut course of action. If the data scientists are only thinking about how to analyze complex webs of disparate data sources and not whether end users will be able to decipher the final results, the project is bound to struggle.
You don’t have data scientists who know your industry.
Even if your organization has taken all of the above considerations into mind, there’s still a chance you’ll be dissatisfied with the end results. Most often, it’s because you aren’t working with data science consulting firms that comprehend the challenges, trends, and primary objectives of your industry.
Take healthcare, for example. Data scientists who only grasp the fundamentals of machine learning, predictive analytics, or automated decision-making can only provide your business with general results. The right partner will have a full grasp of healthcare regulations, prevalent data sources, common industry use cases, and what target end users will need. They can address your pain points and already know how to extract full value for your organization.
And here’s another example from one of our own clients. A Chicago-based retailer wanted to use their data to improve customer lifetime value, but they were struggling with a decentralized and unreliable data ecosystem. With the extensive experience of our retail and marketing team, we were able to outline their current state and efficiently implement a machine-learning solution that empowered our client. As a result, our client was better able to identify sales predictors and customize their marketing tactics within their newly optimized consumer demographics. Our knowledge of their business and industry helped them to get the full results now and in the future.
In conclusion, implementing successful data science projects can be challenging, but the potential return on investment is unparalleled when done right. By addressing common hurdles such as analytical maturity, goal alignment, user-friendliness, and industry expertise, you can increase your chances of achieving meaningful results. Don’t let a failed attempt discourage you from harnessing the power of data science. Take the next step towards success by partnering with 2nd Watch.
Schedule a whiteboard session with our experienced team to explore how we can help you navigate the complexities of data science, align your projects with your business goals, and deliver tangible outcomes. Don’t miss out on the opportunity to unlock valuable insights and drive innovation in your organization. Contact us today and let’s embark on a data-driven journey together.
Insurance providers are rich with data far beyond what they once had at their disposal for traditional historical analysis. The quantity, variety, and complexity of that data enhance the ability of insurers to gain greater insights into consumers, market trends, and strategies to improve their bottom line. But which projects offer you the best return on your investment? Here’s a glimpse at some of the most common insurance analytics project use cases that can transform the capabilities of your business.
Use your historical data to predict when a customer is most likely to buy a new policy.
Both traditional insurance providers and digital newcomers are competing for the same customer base. As a result, acquiring new customers requires targeted outreach with the right message at the moment a buyer is ready to purchase a specific type of insurance.
Predictive analytics allows insurance companies to evaluate the demographics of the target audience, their buying signals, preferences, buying patterns, pricing sensitivity, and a variety of other data points that forecast buyer readiness. This real-time data empowers insurers to reach policyholders with customized messaging that makes them more likely to convert.
Quoting Accurate Premiums
Provide instant access to correct quotes and speed up the time to purchase.
Consumers want the best value when shopping for insurance coverage, but if their quote fails to match their premium, they’ll take their business elsewhere. Insurers hoping to acquire and retain policyholders need to ensure their quotes are precise – no matter how complex the policy.
For example, one of our clients wanted to provide ride-share drivers with four-hour customized micro policies on-demand. Using real-time analytical functionality, we enabled them to quickly and accurately underwrite policies on the spot.
Improving Customer Experience
Better understand your customer’s preferences and optimize future interactions.
A positive customer experience means strong customer retention, a better brand reputation, and a reduced likelihood that a customer will leave you for the competition. In an interview with CMSWire, the CEO of John Hancock Insurance said many customers see the whole process as “cumbersome, invasive, and long.” A key solution is reaching out to customers in a way that balances automation and human interaction.
For example, the right analytics platform can help your agents engage policyholders at a deeper level. It can combine the customer story and their preferences from across customer channels to provide more personalized interactions that make customers feel valued.
Stop fraud before it happens.
You want to provide all of your customers with the most economical coverage, but unnecessary costs inflate your overall expenses. Enterprise analytics platforms enable claims analysis to evaluate petabytes of data to detect trends that indicate fraud, waste, and abuse.
See for yourself how a tool like Tableau can help you quickly spot suspicious behavior with visual insurance fraud analysis.
Now, high-powered analytics has the potential to provide insurers with a real-time understanding of loss ratios, using a wide range of data points to evaluate which of your customers are underpaying or overpaying.
As dashboards and reports become more and more complex, slow run times can present major roadblocks. Here’s a collection of some of the top tips on how to improve dashboard performance and cut slow run times when using Tableau, Power BI, and Looker.
Before getting into how to improve dashboard performance within the three specific tools, here are a few universal principles that will lead to improved performance in almost any case.
Limit logic used in the tool itself: If you’re generating multiple calculated tables/views, performing complex joins, or adding numerous calculations in the BI tool itself, it’s a good idea for performance and governance to execute all those steps in the database or a separate business layer. The more data manipulation done by your BI tool, the more queries and functions your tool has to execute itself before generating visualizations.
Note: This is not an issue for Looker, as Looker offloads all of its computing onto the database via SQL.
Have the physical data available in the needed format: When the physical data in the source matches the granularity and level of aggregation in the dashboard, the BI tool doesn’t need to execute a function to aggregate it. Developing this in the data mart/warehouse can be a lot of work but can save a lot of time and pain during dashboard development.
Keep your interface clean and dashboards focused: Consolidate or delete unused report pages, data sources, and fields. Limiting the number of visualizations on each dashboard also helps cut dashboard refresh time.
Simplify complex strings: In general, processing systems execute functions with strings much more slowly than ints or booleans. Where possible, convert fields like IDs to ints and avoid complex string calculations.
Take advantage of built-in performance tracking: Always the sleek, powerful, and intuitive leading BI tool, Tableau has a native function that analyzes performance problem areas. The performance recorder tells you which worksheets, queries, and dashboards are slow and even shows you the query text.
Execute using extracts rather than live connections: Tableau performs much faster when executing queries on extracts versus live connections. Use extracts whenever possible, and keep them trimmed down to limit query execution time. If you want to stream data or have a constantly refreshing dataset, then extracts won’t be an option.
Again, limit logic: Tableau isn’t built to handle too much relational modeling or data manipulation – too many complex joins or calculations really slow down its processing. Try to offload as many of these steps as possible onto the database or a semantic layer.
Limit marks and filters: Each mark included on a visualization means more parsing that Tableau needs to perform, and too many filters bog down the system. Try instead to split complex worksheets/visualizations into multiple smaller views and connect them with filter actions to explore those relationships more quickly.
Further Sources: Tableau’s website has a succinct and very informative blog post that details most of these suggestions and other specific recommendations. You can find it here.
Understand the implications of DirectQuery: Similar in concept to Tableau’s extract vs. live connection options, import and DirectQuery options for connecting to data sources have different impacts on performance. It’s important to remember that if you’re using DirectQuery, the time required to refresh visuals is dependent on how long the source system takes to execute Power BI’s query. So if your database server is flushed with users or operating slowly for some other reason, you will have slow execution times in Power BI and the query may time out. (See other important considerations when using DirectQuery here.)
Utilize drillthrough: Drillthrough pages are very useful for data exploration and decluttering reports, but they also have the added benefit of making sure your visuals and dashboards aren’t overly complex. They cut down query execution time and improve runtime while still allowing for in-depth exploration.
Be careful with row-level security: Implementing row-level security has powerful and common security use cases, but unfortunately, its implementation has the tendency to bog down system performance. When RLS is in place, Power BI has to query the backend and generate caching separately for each user role. Try to create only as many roles as absolutely necessary, and be sure to test each role to know the performance implications.
Further Sources: Microsoft’s Power BI documentation has a page dedicated to improving performance that further details these options and more. Check it out here.
Utilize dashboard links: Looker has a wonderful functionality that allows for easy URL linking in their drill menus. If you’re experiencing long refresh times, a nifty remedy is to split up your dashboard into different dashboards and provide links between them in drill menus.
Improve validation speed: LookML validation checks the entire project – all model, view, and LookML dashboard files. Increased complexity and crossover between logic in your files lead to longer validation time. If large files and complex relationships make lag in validation time problematic, it can be a good idea to break up your projects into smaller pieces where possible. The key here is handling complex SQL optimally by utilizing whatever methods will maximize SQL performance on the database side.
Pay attention to caching: Caching is another important consideration with Looker performance. Developers should be very intentional with how they set up caching and the conditions for dumping and refreshing a cache, as this will greatly affect dashboard runtime. See Looker’s documentation for more information on caching.
Optimize performance with Persistent Derived Tables (PDTs) and Derived Tables (DTs): Caching considerations come into play when deciding between using PDTs and DTs. A general rule of thumb is that if you’re using constantly refreshing data, it’s better to use DTs. If you’re querying the database once and then developing heavily off of that query, PDTs can greatly increase your performance. However, if your PDTs themselves are giving you performance issues, check out this Looker forum post for a few remedies.
Further Sources: Looker’s forums are rich with development tips. These two forum pages are particularly helpful to learn more about how to improve dashboard performance using Looker:
With your experience in the insurance industry, you understand more than most about how the actions of a smattering of people can cause disproportionate damage. The $80 billion in fraudulent claims paid out across all lines of insurance each year, whether soft or hard fraud, is perpetrated by lone individuals, sketchy auto mechanic shops, or the occasional organized crime group. The challenge for most insurers is that detecting, investigating, and mitigating these deceitful claims is a time-consuming and expensive process.
Rather than accepting loss to fraud as part of the cost of doing business, some organizations are enhancing their detection capabilities with insurance analytics solutions. Here is how your organization can use insurance fraud analytics to enhance fraud detection, uncover emerging criminal strategies, and still remain compliant with data privacy regulations.
Recognizing Patterns Faster
When you look at exceptional claim’s adjusters or special investigation units, one of the major traits they all share is an uncanny ability to recognize fraudulent patterns. Their experience allows them to notice the telltale signs of fraud, whether it’s frequent suspicious estimates from a body shop or complex billing codes intended to hide frivolous medical tests. Though you trust adjusters, many rely on heuristic judgments (e.g., trial and error, intuition, etc.) rather than hard rational analysis. When they do have statistical findings to back them up, they struggle to keep up with the sheer volume of claims.
This is where machine learning techniques can help to accelerate pattern recognition and optimize the productivity of adjusters and special investigation units. An organization starts by feeding a machine learning model a large data set that includes verified legitimate and fraudulent claims. Under supervision, the machine learning algorithm reviews and evaluates the patterns across all claims in the data set until it has mastered the ability to spot fraud indicators.
Let’s say this model was given a training set of legitimate and fraudulent auto insurance claims. While reviewing the data for fraud, the algorithm might spot links in deceptive claims between extensive damage in a claim and a lack of towing charges from the scene of the accident. Or it might notice instances where claims involve rental cars rented the day of the accident that are all brought to the same body repair shop. Once the algorithm begins to piece together these common threads, your organization can test the model’s unsupervised ability to create a criteria for detecting deception and spot all instances of fraud.
What’s important in this process is finding a balance between fraud identification and instances of false positives. If your program is overzealous, it might create more work for your agents, forcing them to prove that legitimate claims received an incorrect label. Yet when the machine learning model is optimized, it can review a multitude of dimensions to identify the likelihood of fraudulent claims. That way, if an insurance claim is called into question, adjusters can comb through the data to determine if the claim should truly be rejected or if the red flags have a valid explanation.
Detecting New Strategies
The ability of analytics tools to detect known instances of fraud is only the beginning of their full potential. As with any type of crime, insurance fraud evolves with technology, regulations, and innovation. With that transformation comes new strategies to outwit or deceive insurance companies.
One recent example has emerged through automation. When insurance organizations began to implement straight through processing (STP) in their claim approvals, the goal was to issue remittances more quickly, easily, and cheaply than manual processes. For a time, this approach provided a net positive, but once organized fraudsters caught wind of this practice, they pounced on a new opportunity to deceive insurers.
Criminals learned to game the system, identifying amounts that were below the threshold for investigation and flying their fraudulent claims under the radar. In many cases, instances of fraud could potentially double without the proper tools to detect these new deception strategies. Though most organizations plan to enhance their anti-fraud technology, there’s still the potential for them to lose millions in errant claims – if their insurance fraud analytics are not programmed to detect new patterns.
In addition to spotting red flags for common fraud occurrences, analytics programs need to be attuned to any abnormal similarities or unlikely statistical trends. Using cluster analysis, an organization can detect statistical outliers and meaningful patterns that reveal potential instances of fraud (such as suspiciously identical fraud claims).
Even beyond the above automation example, your organization can use data discovery to find hidden indicators of fraud and predict future incidents. Splitting claims data into various groups through a few parameters (such as region, physician, billing code, etc., in healthcare) can help in detecting unexpected correlations or warning signs for your automation process or even human adjusters to flag as fraud.
Safeguarding Personally Identifiable Information
As you work to improve your fraud detection, there’s one challenge all insurers face: protecting the personally identifiable information (PII) of policyholders while you analyze your data. The fines related to HIPAA violations can amount to $50,000 per violation, and other data privacy regulations can result in similarly steep fines. The good news is that insurance organizations can balance their fraud prediction and data discovery with security protocols if their data ecosystem is appropriately designed.
Maintaining data privacy compliance and effective insurance fraud analytics requires some maneuvering. Organizations that derive meaningful and accurate insight from their data must first bring all of their disparate data into a single source of truth. Yet, unless they also implement access control through a compliance-focused data governance strategy, there’s a risk of regulatory violations while conducting fraud analysis.
One way to limit your exposure is to create a data access layer that tokenizes the data, replacing any sensitive PII with unique identification symbols to keep data separate. Paired with clear data visualization capabilities, your adjusters and special investigation units can see clear-cut trends and evolving strategies without revealing individual claimants. From there, they can take their newfound insights into any red flag situation, saving your organization millions while reducing the threat of noncompliance.
Are you ready to take your data science initiatives to the next level? Partner with 2nd Watch, the industry leader in data management, analytics, and data science consulting. Our team of experts will guide you through the entire process, from building the business case to data preparation and model building. Schedule a data science readiness whiteboard session with us today and unlock the full potential of data science for your business. Don’t miss out on the opportunity to enhance fraud detection, uncover emerging criminal strategies, and remain compliant with data privacy regulations. Get started now and experience the transformative power of insurance fraud analytics with 2nd Watch by your side.
Real-time analytics. Streaming analytics. Predictive analytics. These buzzwords are thrown around in the business world without a clear-cut explanation of their full significance. Each approach to analytics presents its own distinct value (and challenges), but it’s tough for stakeholders to make the right call when the buzz borders on white noise.
Which data analytics solution fits your current needs? In this post, we aim to help businesses cut through the static and clarify modern analytics solutions by defining real-time analytics, sharing use cases, and providing an overview of the players in the space.
Real-time or streaming analytics allows businesses to analyze complex data as it’s ingested and gain insights while it’s still fresh and relevant.
Real-time analytics has a wide variety of uses, from preventative maintenance and real-time insurance underwriting to improving preventive medicine and detecting sepsis faster.
To get the full benefits of real-time analytics, you need the right tools and a solid data strategy foundation.
What is Real-Time Analytics?
In a nutshell, real-time or streaming analysis allows businesses to access data within seconds or minutes of ingestion to encourage faster and better decision-making. Unlike batch analysis, data points are fresh, and findings remain topical. Your users can respond to the latest insight without delay.
Yet speed isn’t the sole advantage of real-time analytics. The right solution is equipped to handle high volumes of complex data and still yield insight at blistering speeds. In short, you can conduct big data analysis at faster rates, mobilizing terabytes of information to allow you to strike while the iron is hot and extract the best insight from your reports. Best of all, you can combine real-time needs with scheduled batch loads to deliver a top-tier hybrid solution.
Real-time analytics is revolutionizing the way businesses make decisions and gain insights. With streaming analytics, organizations can analyze complex data as it is ingested, enabling faster and more informed decision-making. Whether it’s detecting anomalies in manufacturing processes, optimizing supply chain operations, or personalizing customer experiences in real-time, streaming analytics is transforming various industries. By leveraging advanced technologies and powerful analytics platforms, businesses can unlock the full potential of real-time data to drive growth, improve operational efficiency, and stay ahead in today’s fast-paced business landscape.
How does the hype translate into real-world results?
Depending on your industry, there is a wide variety of examples you can pursue. Here are just a few that we’ve seen in action:
Next-Level Preventative Maintenance: Factories hinge on a complex web of equipment and machinery working for hours on end to meet the demand for their products. Through defects or standard wear and tear, a breakdown can occur and bring production to a screeching halt. Connected devices and IoT sensors now provide technicians and plant managers with warnings – but only if they have the real-time analytics tools to sound the alarm.
Azure Stream Analytics is one such example. You can use Microsoft’s analytics engine to monitor multiple IoT devices and gather near-real-time analytical intelligence. When a part needs a replacement or it’s time for routine preventative maintenance, your organization can schedule upkeep with minimal disruption. Historical results can be saved and integrated with other line-of-business data to cast a wider net on the value of this telemetry data.
Real-Time Insurance Underwriting: Insurance underwriting is undergoing major changes thanks to the gig economy. Rideshare drivers need flexibility from their auto insurance provider in the form of modified commercial coverage for short-term driving periods. Insurance agencies prepared to offer flexible micro policies that reflect real-time customer usage have the opportunity to increase revenue and customer satisfaction.
In fact, one of our clients saw the value of harnessing real-time big data analysis but lacked the ability to consolidate and evaluate their high-volume data. By partnering with our team, they were able to create real-time reports that pulled from a variety of sources ranging from driving conditions to driver ride-sharing scores. With that knowledge, they’ve been able to tailor their micro policies and enhance their predictive analytics.
Healthcare Analytics: How about this? Real-time analytics saves lives. Death by sepsis, an excessive immune response to infection that threatens the lives of 1.7 million Americans each year, is preventable when diagnosed in time. The majority of sepsis cases are not detected until manual chart reviews conducted during shift changes – at which point, the infection has often already compromised the bloodstream and/or vital tissues. However, if healthcare providers identified warning signs and alerted clinicians in real time, they could save multitudes of people before infections spread beyond treatment.
HCA Healthcare, a Nashville-based healthcare provider, undertook a real-time healthcare analytics project with that exact goal in mind. They created a platform that collects and analyzes clinical data from a unified data infrastructure to enable up-to-the-minute sepsis diagnoses. Gathering and analyzing petabytes of unstructured data in a flash, they are now able to get a 20-hour early warning sign that a patient is at risk of sepsis. Faster diagnoses results in faster and more effective treatment.
That’s only the tip of the iceberg. For organizations in the healthcare payer space, real-time analytics has the potential to improve member preventive healthcare. Once again, real-time data from smart wearables, combined with patient medical history, can provide healthcare payers with information about their members’ health metrics. Some industry leaders even propose that payers incentivize members to make measurable healthy lifestyle choices, lowering costs for both parties at the same time.
Getting Started with Real-Time Analysis
There’s clear value produced by real-time analytics but only with the proper tools and strategy in place. Otherwise, powerful insight is left to rot on the vine and your overall performance is hampered in the process. If you’re interested in exploring real-time analytics for your organization, contact us for an analytics strategy session. In this session lasting 2-4 hours, we’ll review your current state and goals before outlining the tools and strategy needed to help you achieve those goals.
Real-time analytics is revolutionizing the way businesses operate, providing valuable insights and enabling faster decision-making. With its ability to analyze complex data in real-time, organizations can stay ahead of the competition and make data-driven decisions. At 2nd Watch, we understand the importance of real-time analytics and its impact on business success.
Get Started with Real-Time Analytics
If you’re ready to leverage the power of real-time analytics for your business, partner with 2nd Watch. Our team of experts can help you develop a comprehensive analytics strategy, implement the right tools and technologies, and guide you through the process of unlocking the full potential of real-time analytics. Contact us today to get started on your real-time analytics journey and drive meaningful business outcomes.
Professionals in the supply chain industry need uncanny reflexes. The moment they get a handle on raw materials, labor expenses, international legislation, and shipping conditions, the ground shifts beneath them and all the effort they put into pushing their boulder up the hill comes undone. With the global nature of today’s supply chain environment, the factors governing your bottom line are exceptionally unpredictable. Fortunately, there’s a solution for this problem: predictive analytics for supply chain management.
This particular branch of analytics offers an opportunity for organizations to anticipate challenges before they happen. Sounds like an indisputable advantage, yet only 30% of supply chain professionals are using their data to forecast their future.
Though most of the stragglers plan to implement predictive analytics in the next 10 years, they are missing incredible opportunities in the meantime. Here are some of the competitive advantages companies are missing when they choose to ignore predictive operational analytics.
Enhanced Demand Forecasting
How do you routinely hit a moving goalpost? As part of an increasingly complex global system, supply chain leaders are faced with an increasing array of expected and unexpected sales drivers from which they are pressured to determine accurate predictions about future demand. Though traditional demand forecasting yields some insight from a single variable or small dataset, real-world supply chain forecasting requires tools that are capable of anticipating demand based on a messy, multifaceted assembly of key motivators. Otherwise, they risk regular profit losses as a result of the bullwhip effect, buying far more products or raw materials than are necessary.
For instance, one of our clients, an international manufacturer, struggled to make accurate predictions about future demand using traditional forecasting models. Their dependence on the historical sales data of individual SKUs, longer order lead times, and lack of seasonal trends hindered their ability to derive useful insight and resulted in lost profits. By implementing machine learning models and statistical packages within their organization, we were able to help them evaluate the impact of various influencers on the demand of each product. As a result, our client was able to achieve an 8% increase in weekly demand forecast accuracy and 12% increase in monthly demand forecast accuracy.
This practice can be carried across the supply chain in any organization, whether your demand is relatively predictable with minor spikes or inordinately complex. The right predictive analytics platform can clarify the patterns and motivations behind complex systems to help you to create a steady supply of products without expensive surpluses.
Smarter Risk Management
The modern supply chain is a precise yet delicate machine. The procurement of raw materials and components from a decentralized and global network has the potential to cut costs and increase efficiencies – as long as the entire process is operating perfectly. Any type of disruption or bottleneck in the supply chain can create a massive liability, threatening both customer satisfaction and the bottom line. When organizations leave their fate up to reactive risk management practices, these disruptions are especially steep.
Predictive risk management allows organizations to audit each component or process within their supply chain for its potential to destabilize operations. For example, if your organization currently imports raw materials such as copper from Chile, predictive risk management would account for the threat of common Chilean natural disasters such as flooding or earthquakes. That same logic applies to any country or point of origin for your raw materials.
You can evaluate the cost and processes of normal operations and how new potentialities would impact your business. Though you can’t prepare for every possible one of these black swan events, you can have contingencies in place to mitigate losses and maintain your supply chain flow.
Formalized Process Improvement
As with any industry facing internal and external pressures to pioneer new efficiencies, the supply chain industry cannot rely on happenstance to evolve. There needs to be a twofold solution in place. One, there needs to be a culture of continuous organizational improvement across the business. Two, there need to be apparatuses and tools in place to identify opportunities and take meaningful action.
For the second part, one of the most effective tools is predictive analytics for supply chain management. Machine learning algorithms are exceptional at unearthing inefficiencies or bottlenecks, giving stakeholders the fodder to make informed decisions. Because predictive analytics removes most of the grunt work and exploration associated with process improvement, it’s easier to create a standardized system of seeking out greater efficiencies. Finding new improvements is almost automatic.
Ordering is an area that offers plenty of opportunities for improvement. If there is an established relationship with an individual customer (be it retailer, wholesaler, distributor, or the direct consumer), your organization has stockpiles of information on individual and demographic customer behavior. This data can in turn be leveraged alongside other internal and third-party data sources to anticipate product orders before they’re made. This type of ordering can accelerate revenue generation, increase customer satisfaction, and streamline shipping and marketing costs.
Incorporating predictive analytics into supply chain management can be a game-changer for businesses, providing them with a competitive edge in today’s dynamic and unpredictable market environment. With the expertise and support of 2nd Watch, a leading provider of advanced analytics solutions, organizations can harness the power of predictive analytics to drive better decision-making, optimize operations, and stay ahead of the competition.
By leveraging cutting-edge technologies and machine learning algorithms, 2nd Watch helps businesses enhance their demand forecasting capabilities, enabling them to accurately predict future demand based on a holistic analysis of key motivators and variables. This empowers supply chain leaders to make informed decisions and avoid profit losses resulting from the bullwhip effect, ensuring optimal inventory management and efficient resource allocation.
Moreover, 2nd Watch enables organizations to adopt smarter risk management practices by auditing every component and process within the supply chain. By leveraging predictive analytics, businesses can identify potential disruptions and bottlenecks, proactively mitigate risks, and maintain a seamless flow of operations. Whether it’s accounting for natural disasters in specific regions or evaluating the impact of geopolitical factors on the supply chain, 2nd Watch helps businesses stay resilient and agile in the face of uncertainties.
Additionally, 2nd Watch plays a crucial role in driving formalized process improvement within the supply chain industry. With its expertise in predictive analytics, the company uncovers hidden inefficiencies, identifies bottlenecks, and provides actionable insights for streamlining operations. By automating the process of seeking out greater efficiencies, organizations can create a standardized system for continuous improvement and innovation, ensuring they stay ahead in a rapidly evolving market.
Incorporating predictive analytics into supply chain management with the support of 2nd Watch offers numerous advantages, from optimized demand forecasting to smarter risk management and formalized process improvement. Don’t miss out on the transformative potential of predictive analytics. Contact 2nd Watch today to learn more about their advanced analytics solutions and unlock the full power of predictive analytics for your supply chain.