Category Archives: Business Intelligence

Transforming Supply Chain Management with Intelligent Software

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Robotic Process Automation (RPA) technology to automate business tasks with AI. Concept with expert setting up automated software on laptop computer. Digital transformation and change management.

Supply chain project management has evolved, shifting from a focus on efficiency to navigating a complex landscape influenced by globalization, technology, and changing consumer preferences. The vulnerabilities exposed during events like the COVID-19 pandemic underscore the need for adaptability. The pandemic posed challenges, disrupting production, leading to closures, and causing delays and increased costs in global transportation.

Additionally, unpredictable shifts in consumer behavior created demand fluctuations, impacting industries differently. Inventory management became more challenging, resulting in shortages or excess inventory. Supplier reliability and labor shortages further strained production capacity.

The crisis highlighted the necessity for digital transformation, remote work, and technology adoption in supply chain management. Regulatory changes and economic downturns added complexity to cross-border supply chains. Financial strain emphasized the importance of robust risk management, leading to a renewed focus on building resilient and agile supply chains. Businesses now invest in technology, diversify suppliers, and reassess inventory strategies.

Intelligent software enhances decision-making and risk management, facilitating collaboration throughout the supply chain. For instance, during sudden demand changes due to lockdowns, the software swiftly analyzes data, enabling real-time adjustments to inventory, production, and distribution. This adaptability ensures a responsive and agile supply chain, surpassing traditional approaches for efficiency and customer satisfaction.

The promise of intelligent software

In the current technological realm, intelligent software signifies more than just automation; it melds advanced algorithms, artificial intelligence, and machine learning to emulate human cognitive abilities. Unlike its conventional counterparts, this software learns, adapts, and autonomously recommends actions, excelling in data analysis and trend prediction. Its continuous adaptation based on feedback refines its performance over time.

How intelligent software could make a difference in specific situations

1. Demand volatility amidst global events.

The COVID-19 pandemic triggered significant demand shifts, straining supply chains. Intelligent software, with real-time analytics, could have monitored consumer behaviors, identified disruptions, and gauged inventory levels. Such insights would have refined demand forecasts, allowing organizations to adjust production and prioritize shipments, mitigating stockout risks and excess inventory costs.

2. Supply chain disruptions due to geopolitical tensions.

Geopolitical uncertainties can disrupt supply chains. Intelligent software could pre-emptively identify vulnerabilities, highlighting dependencies on specific regions or suppliers. Through simulations and alternative sourcing evaluations, it would have enabled organizations to devise resilient strategies, ensuring uninterrupted operations amid external disruptions.

3. Quality control and recall management.

Product recalls pose financial and reputational risks. Intelligent software, with advanced analytics, monitors production for deviations from quality standards. Using predictive analytics, it could anticipate issues, facilitating timely interventions, minimizing recall extents, and preserving brand reputation.

4. Transportation and logistics optimization.

Efficient transportation is crucial for supply chain success. Intelligent software, leveraging predictive analytics, would analyze factors like traffic and weather to optimize transportation strategies. This would reduce delays, enhance resource use, and boost supply chain effectiveness.

5. Inventory management in seasonal industries.

Seasonal industries face inventory challenges due to fluctuating demand and product perishability. Intelligent software, utilizing machine learning, analyzes sales trends and market dynamics to offer precise demand forecasts and inventory recommendations. This ensures optimal inventory levels, reduces holding costs, and capitalizes on market opportunities.

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Why decision intelligence is essential for overcoming supply chain constraints

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The current supply chain disruption is one of the many types of crises the marketplace has faced over the years. Even before COVID-induced challenges had cargo ships anchored off of ports across the globe and store shelves barely stocked, supply chain leaders have been in a race to keep up with changing consumer demands, a shifting competitive landscape, and technological advances.

Yet, as the development, reach and success of businesses has become highly dependent on tightly linked supply chains, the structure of those connections has become increasingly fragile and intricately connected.

Over the last two years, an unprecedented supply chain crisis has unfolded. With networks spanning multiple continents, global supply chains have broken down. From COVID-19 and the war in Ukraine to a sideways freighter that blocked the Suez Canal for a week and a growing list of environmental disasters, the upheaval has created a new benchmark for business-as-usual. A survey from the UK Office for National Statistics showed that 40% of businesses in the wholesale and retail trade industry reported global supply chain disruptions at the end of the first quarter this year.

This disruption is closely tied to a failure of foresight and planning built into supply chain systems.

Asking the right questions

Many companies tackling supply chain disruption see themselves as “data-driven,” when in fact, most are not. A Gartner report shows that less than half of organizations have actively started to build a roadmap for supply chain digitization transformation, despite it being a key priority for most leaders. Another survey showed only two-thirds of supply chain organizations felt the strategy and execution of their supply chains were well aligned.

Business intelligence (BI) and analysis tools were the promised future, where business users could easily access and transform huge volumes of corporate-wide data to predict business outcomes and future demand. However, the reality is that traditional BI solutions and ERP systems are static and can only provide a snapshot of the present or past.

Decision intelligence rests on prescriptive analytics

Such foresight comes from adding a prescriptive analytics layer to a firm’s supply chain management. This layer answers the question “what should happen” and becomes the basis for generating decisions, not just insights. This approach elevates the level of analytic inquiry, using machine learning and optimization models to propose a course of action based on data, analytics and business models.

Ultimately, this can dramatically transform how companies manage the flow of goods throughout their supply chains because it resolves the question how to proceed to achieve the targeted outcome.

Decision intelligence and the future of the supply chain

Taking a new approach to supply chains relies on a new vision for data in an organization. Data is the engine of growth and the source of intelligence that will allow businesses to get a grip on their supply chains.

This means drawing on data from a wider variety of sources than ever before. Businesses need more actionable, real-time data from across their supply chains. They need to quickly and securely access multiple data sources across on-premises data centers and multiple clouds. To plan for future shocks, businesses need to learn from this historic moment and feed this information into predictive and prescriptive analytics modeling.

A new tomorrow

Supply chain management solutions based on decision intelligence and real-time prescriptive analytics models are potent instruments in the fight against the supply chain crisis. Such systems can improve overall processes throughout the enterprise and build resilience into demand forecasts. They can reduce costs associated with overstocking, inventory stockouts, and product obsolescence — even in the face of widespread crises.

Read more at Why decision intelligence is essential for overcoming supply chain constraints 

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Unlocking the Value of Artificial Intelligence (AI) in Supply Chains and Logistics

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Speed in decision-making. Speed in reducing cycle-times. Speed in operations. And, speed in continuous improvement. The use of Artificial Intelligence in the supply chain is here to stay and will make huge waves in the years to come.

According to Gartner, supply chain organizations expect the level of machine automation in their supply chain processes to double in the next five years. At the same time, global spending on IIoT Platforms is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.

In today’s connected digital world, maximizing productivity by reducing uncertainties is the top priority across industries. Plus, mounting expectations of supersonic speed and operational efficiencies further underscore the need to leverage the prowess of Artificial Intelligence (AI) in supply chains and logistics.

Accelerating Supply Chain Success with AI in Supply Chains & Logistics

AI in supply chains can deliver the powerful optimization capabilities required for more accurate capacity planning, improved demand forecasting, enhanced productivity, lower supply chain costs, and greater output, all while fostering safer working conditions.

The pandemic and the subsequent disruptions has demonstrated the dramatic impact of uncertainties on supply chains and has established the need for smart contingency plans to help companies deal with these uncertainties in the right way.

But is AI the answer? What can AI mean for companies as they struggle to get their supply chain and logistics back on track? Let’s find out.

ACCURATE INVENTORY MANAGEMENT

Accurate inventory management can ensure the right flow of items in and out of a warehouse. Simply put, it can help prevent overstocking, inadequate stocking and unexpected stock-outs. But the inventory management process involves multiple inventory related variables (order processing, picking and packing) that can make the process both, time consuming and highly prone to errors.

WAREHOUSE EFFICIENCY

An efficient warehouse is an integral part of the supply chain. AI-based automation can assist in the timely retrieval of an item from a warehouse and ensure a smooth journey to the customer. AI systems can also solve several warehouse issues, more quickly and accurately than a human can, and also simplify complex procedures and speed up work. Also, along with saving valuable time, AI-driven automation efforts can significantly reduce the need for, and cost of, warehouse staff.

ENHANCED SAFETY

AI-based automated tools can ensure smarter planning and efficient warehouse management, which can, in turn, enhance worker and material safety. AI can analyze workplace safety data and inform manufacturers about any possible risks. It can record stocking parameters and update operations along with necessary feedback loops and proactive maintenance. This helps companies react swiftly and decisively to keep warehouses secure and compliant with safety standards.

REDUCED OPERATIONS COSTS

Here’s one benefit of AI systems for the supply chain that one simply can’t ignore. From customer service to the warehouse, automated intelligent operations can work error-free for a longer duration, reducing the number of human oversight-led errors and workplace incidents. Additionally, warehouse robots can provide greater speed and accuracy, achieving higher levels of productivity – all of which will reflect in reduced operations costs.

ON-TIME DELIVERY

As we discussed above, AI systems help reduce dependency on manual efforts, thus making the entire process faster, safer and smarter. This helps facilitate timely delivery to the customer as per the commitment. Automated systems accelerate traditional warehouse procedures, removing operational bottlenecks along the value chain with minimal effort to achieve delivery targets.

 

Read more at Unlocking the Value of Artificial Intelligence (AI) in Supply Chains and Logistics

How business intelligence is helping global businesses succeed

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In today’s always connected and hyper-competitive world, growing market share is a challenge for any organisation. Industry leaders understand that to get an edge over their competitors they need new and better insights into their business to help identify hidden risks and mitigate them and inform their decision making in everything from finding supply chain inefficiencies to uncovering new avenues for sales.

Having actionable intelligence can be the difference between success and failure, but sorting through the wealth of data every modern global business generates is a complex task and it is too easy to miss a critical information point. Business intelligence platforms help companies sift, sort, and process this data into different types of reports with actionable insights that highlight any weaknesses to address and strengths that should be built upon.

Here are four ways business intelligence can help a global business find success.

Help with product pricing

Pricing a product or service can be a difficult question. Business leaders need to find a price point within a specific market that is sufficiently low to attract new customers away from their competitors, but sufficiently high that they can drive profits and create a growing and successful company.

It is difficult enough to try and determine the best price within a single geographic market, such as the UK, but when you look to expand the business abroad, each geographical market has its own unique set of economic and market factors that need to be taken into account. The company will need to set a different price targeted to each local market conditions, and business intelligence can bring together all the relevant data and help stakeholders find the best pricing strategy for wherever they plan to launch.

Identify supply chain efficiencies and weaknesses

Modern supply chains cross continents, and a few days delay at a port in China or a ship that has taken a wrong turn can have roll on impacts throughout a business. The data at each point in this supply chain may be stored in different systems that may be region or sector specific, and trying to combine all that data can be a slow and laborious process.

Business intelligence platforms can help companies integrate these disparate sources of data and create insights such as where in the supply chain is the weakest link and where companies should possibly look for backup solutions should issues arise, and also where stock is left sitting in warehouses for long periods, costing money for storage and creating expensive inefficiencies.

Nurture customer loyalty

The sales and marketing investment required to find new customers is expensive, and so all businesses need to nurture their relationship with the current customers to maintain profitability. These relationships are more challenging to maintain for businesses with overseas operations, as priorities and social niceties vary significantly from region to region, with what some would consider good manners in one country sometimes considered rude in another.

The leader of well established global company understands that standardised customer engagement programmes often do not work well across borders, and local solutions need to be devised. However, maintaining a variety of different programmes around the world is burdensome and it can be a struggle to compare how each performs.

Read more at How business intelligence is helping global businesses succeed

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The Role of Business Intelligence in the Supply Chain

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Supply Chain BI Dashboard - Warehouse Order Performance

Supply Chain BI Dashboard – Warehouse Order Performance

Business intelligence enhances supply chain management by making real-time data analytics accessible. Self-service BI takes this a step further by allowing users to run their own queries and create their own reports, even if they don’t have a background in statistical analysis.

Here, we’ll discuss how BI can provide real-time insights into supply chain emerging risks, inefficiencies, and anomalies, allowing organizations to quickly isolate and resolve potential problems.

Supply Chain Disruptions

We saw unprecedented disruption to supply chains in 2020 that caused problems for companies and consumers. The Federal Reserve reports continued supply chain and logistics disruptions in 2021 are emerging at the same time demand is increasing.

For companies struggling to manage supply chains, it’s a significant issue. Supply chains represent as much as half of the value of a company’s products or services.

Failing to manage the supply chain efficiently, leads to ongoing problems, including:

  1. Less resilient to market changes
  2. Less efficient
  3. Decreased inventory
  4. Inability to meet demand
  5. Decreased financial performance

Managing the Supply Chain with Embedded BI

Embedded BI integrates business intelligence reporting tools into everyday apps. Embedded business intelligence tools provide ad hoc reporting, interactive dashboards, scheduling, and distribution tools within your custom apps.

When you embed business intelligence tools into your decision chain, it provides quick access to the insights team members need. Potential supply chain problems can be spotted in real-time for faster resolution.

Visualizing Demand and Inventory

Data visualization makes it easier to manage inventory by providing a visual reference for current inventory levels and pending orders. This makes it easier to forecast inventory needs and set reorder points.

Visualizing Distribution

You can also visualize the movement of goods and material through your supply chain into your inventory and out the door to customers. By monitoring order status, you can also see potential disruptions in your supply chain or your processes.

Read more at The Role of Business Intelligence in the Supply Chain.

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Role of Business Intelligence in Supply Chain

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Role of Business Intelligence in Supply Chain | Bold BI

Role of Business Intelligence in Supply Chain

Supply chain management plays a vital role in the emerging world market. According to the Harvard Business Review, in 2018, the U.S. supply chain made up 37 percent of all jobs, employing 44 million people in the U.S. To stay competitive in the supply chain management business, you need to recognize the potential weaknesses of your organization and form ideas to overcome them. Business intelligence (BI) helps you identify potential risks associated with your business and enables managers to take timely corrective action. BI gives you the required organization and visualization of the data stored in your business’s data banks needed for insight into its patterns. In this blog, I am going to discuss why supply chain management needs business intelligence and how BI paves the path to the growth of your business.

Why supply chain management needs BI

  1. BI helps key decision-makers monitor internal inefficiencies and gives them the metric-driven insight to take appropriate actions to overcome these inefficiencies.
  2. BI tools, such as scorecards and dashboards, provide detailed breakdowns of reports on your company’s performance with many available metrics and KPIs. These help you monitor the progress of your company growth, like whether quarterly goals are achieved or not, as well as forecast future results based on your previous performance data.
  3. Since supply chain management involves many departments, there is a lack of visibility and lots of data spread across the departments. BI collects all of your company’s data into a single platform.
  4. With the detailed and specific data from every step of production, you can go through the process from transporting raw materials to delivering your final products to customers and strategically enhance each part.

Various aspects of BI in supply chain management

The supply chain comprises various elements, such as operations management, logistics, procurement, and IT. It acts like the wheels of a vehicle. If anyone of them fails, the entire vehicle cannot move. BI coordinate each aspect with the others and helps you to run a more successful business.

  1. Demand and inventory management
  2. Distribution and communication management
  3. Supplier and vendor association
  4. Forecasting

Bold BI’s business intelligence dashboards for supply chain management

With Bold BI’s supply chain management dashboards, you can achieve the objectives of your company by tracking the important KPIs (such as cash-to-cycle time, perfect order rate, customer order cycle time, inventory turnover), drilling down into the key metrics with a detailed analysis in every widget, and identifying the risks in your process and mitigating those risks with action plans.

Read more at Role of Business Intelligence in Supply Chain

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EU Launches Estimated €400M Blockchain, AI Fund to Avoid Lagging US, China

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A new fund has been set up with the aim of preventing the EU falling behind nations like the U.S. and China on blockchain and artificial intelligence (AI) innovation.

The European Investment Fund (EIF) and the European Commission have together put up €100 million (over $110 million) for a dedicated investment scheme that will make capital available to AI and blockchain projects via VC funds or other investors, EIF, an EU agency set up to indirectly fund SMEs, said in a blog post on Wednesday.

With the “cornerstone” funding in place, the EIF said private investors are expected to bring up to €300 million ($331 million) into the fund, while the total could rise further from next year, with national promotional banks being able to co-invest under the scheme.

Sifted reports that the fund could ultimately raise up to €2 billion ($2.2 billion) under the InvestEU Programme.

According to the post, the EU already spends plenty on blockchain (expected spending for 2019 is $674 million), but that is mostly directed toward research and proof-of-concepts.

The U.S. is the biggest spender, with a $1.1 billion expected spend, and China is second with $319 million, according to cited numbers from the International Data Corporation.

The new fund is aimed to address the fact that not so much is spent in the EU on developing “larger scale projects.

“Investing in a portfolio of innovative AI and blockchain companies will help develop a dynamic EU-wide investors community on AI and blockchain. By involving national promotional banks, we can scale up the volume of investments at a national level,” the EIF said.

Read more at EU Launches Estimated €400M Blockchain, AI Fund to Avoid Lagging US, China

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Scientist Ng brings AI to manufacturing

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Artificial intelligence pioneer Andrew Ng launched a new AI company Landing.ai on Thursday.

On the same day, the company announced a strategic cooperation with electronics contractor Foxconn to develop a program that aims to bring AI and machine learning technologies to the manufacturing industry.

According to Ng’s statement, his company is developing a series of programs to help enterprises transform for the age of AI, including providing new technologies to optimize companies’ organizations structures, train employees, and more. The company’s businesses will start in the manufacturing industry.

Ng said the AI technology is conductive to manufacturing enterprises to improve quality testing process, shorten products’ design cycle, remove bottleneck of supply chain, reduce waste on materials and energy and raise output.

AI will revitalize manufacturing industry and generate jobs in the industry, he said. I In the age of AI, the employees need to accept new skills training to fit jobs that will be more complex than before, Ng added.

Landing.ai will provide solutions to some employees who are likely to be laid off, Ng said. Currently, the company is discussing the training plan with some potential partners including local governments.

Read more at Scientist Ng brings AI to manufacturing

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All in with online, can J.C. Penney get up to digital speed?

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I had a few occasions chatting with the IT people of the company in the past few years. They were reluctant to adapt to the on-line trend of the retail market. One year, they wanted to expand their on-line catalog business; the next year, they closed the on-line catalog business and moves the majority of their IT people overseas in the following years. This time, it appears that the new SVP, Mike Amend, hired from Home Depot, is ready to face the on-line retail business challenges.

This article highlights a lot of positive actions for the company to transition itself from a traditional retail business to an on-line one.

  1. Recognizing its market strength: Research from comScore tells Penney that its customers have household incomes of $60,000 to $90,000, and they tend to be hardworking, two-income families living both in rural and urban settings. They don’t have the discretionary income to commit to membership fees.
  2. Last month, Penney added the ability to ship from all its stores, which immediately made about $1 billion of store inventory available to online customers and cut the distance between customer and delivery.
  3. About 80 percent of a store’s existing inventory is eligible for free same-day pickup.
    Last week, it offered free shipping to stores with no minimum purchase. Large items like refrigerators and trampolines are excluded.
  4. JCPenney.com now stocks four times the assortment found in its largest store by partnering with other brands and manufacturers.
  5. More than 50 percent of its online assortment is drop-shipped by suppliers and doesn’t go through Penney’s distribution. Categories added range from bathroom and kitchen hardware to sporting goods, pets and toys
  6. JCPenney.com now has one Web experience regardless of the screen: phone, tablet or desktop.
  7. Its new mobile app and wallet include Penney’s new upgraded Rewards program. Customers can book salon appointments on it. The in-store mode has a price-check scanner.
  8. Penney set out to “democratize access to the data,” so that not only the technical staff could understand it, now dashboards and heat maps allow the artful side of the business — the merchants — to measure such things as sales to in-stock levels or pricing to customer behavior.

Read more at All in with online, can J.C. Penney get up to digital speed?

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One-Page Data Warehouse Development Steps

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Data warehouse is the basis of Business Intelligence (BI). It not only provides the data storage of your production data but also provides the basis of the business intelligence you need. Almost all of the books today have very elaborated and detailed steps to develop a data warehouse. However, none of them is able to address the steps in a single page. Here, based on my experience in data warehouse and BI, I summarize these steps in a page. These steps give you a clear road map and a very easy plan to follow to develop your data warehouse.

Step 1. De-Normalization. Extract an area of your production data into a “staging” table containing all data you need for future reporting and analytics. This step includes the standard ETL (extraction, transformation, and loading) process.

Step 2. Normalization. Normalize the staging table into “dimension” and “fact” tables. The data in the staging table can be disposed after this step. The resulting “dimension” and “fact” tables would form the basis of the “star” schema in your data warehouse. These data would support your basic reporting and analytics.

Step 3. Aggregation. Aggregate the fact tables into advanced fact tables with statistics and summarized data for advanced reporting and analytics. The data in the basic fact table can then be purged, if they are older than a year.

Read more at One-Page Data Warehouse Development Steps

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