How to Read the Financial Statements for Supply Chain and Procurement Professionals

How to Read the Financial Statements for Supply Chain and Procurement Professionals

There are three main financial statements:

  • Cash flow statement
  • Income statement or Profit and Loss statement (P&L)
  • Balance sheet


Cash Flow Statement

The cash flow statement shows the inflows and outflows of funds—the “show me the money” of the business. It covers a period of time. It can be a year, a quarter, a month, etc. A cash flow statement can be historical or forward-looking, a projection or an estimate.

There are three distinct sections:

1. Operating activities —they are the business’s bread and butter. They are the regular business operations that generate revenue and expenses.

2.  Investing activities— they are not normal (an adjective that becomes popular in Covid times) or part of the regular operations of a business. For example, selling machines or equipment.

3. Financing activities— these activities include both debt and equity for the business to support operations.

There are two ways or recipes to build the cash flow statement:

  1. Direct method—This is to start from scratch. The person preparing the cash flow statement gathers all the transactions for a specific period and categorizes the transactions into the 3 buckets: operating, investing, and financing activities.
  2. Indirect method— Without getting into details that only accountants find charming, the person building the cash flow statement starts with the P&L or income statement and makes some adjustments to reflect cash only without the need to start from the ground up.


A P&L can be historical or forward-looking, a projection or an estimate.

Both the P&L and cash flow statements are fundamental. Showing only one is like eating mac and cheese without either the macaroni or the cheese, a peanut butter and jelly sandwich without the peanut butter or the jelly.

The P&L shows income and expenses for a period of time and follows the accrual accounting method. This means that finance records the transactions based on receipts and deliveries, not payments or money ownership exchanges. The profits (or loss) are in the P&L. The P&L shows the performance of a company. It has a similar structure as that of the cash flow statement— operating, financial, and tax activities. Supply chain and procurement have a huge impact on revenue, COGS, gross profits, and all operating expenses.

Let’s take a look at its key components.

Revenue —This is the top line of the P&L. Traditionally, supply chain is a synonym for cost containment and optimization. While savings are a top priority, supply chain goes beyond cost reduction.

Supply chain plays an essential role in business’ growth, orchestrating the multiple moving parts from raw material acquisition until delivery to the customer we want to delight. Not considering the impact of supply chain on revenue is leaving money on the table.

Marketing may sell the sun, the moon, and the stars with delivery in the next 24 hours, but with no product available, there are no sales; instead, there are disappointed and angry customers, like angry birds kicking and screaming for worms. Nasty.

Cost of Goods Sold—All roads lead to Rome, so as to COGS.

COGS is all costs related to getting your products ready to sell to your customers. It includes the ingredients, raw materials, components, the packaging, freight-in cost, plant labor, and factory overhead.

Production or factory costs are split into direct and indirect. Direct costs are those connected with a specific product. An example of a direct cost is when the line runs to produce barbecue chips, as that cost can be assigned to these spicy sweet chips. An example of indirect cost is the salary of factory supervisors because they can’t be related to a particular product. Another example is the factory utilities that are included in the overhead cost.

Production or factory costs also split into variable and fixed costs.  Variable costs increase or decrease in the same proportion as production does. If the company doubles production, the cost of ingredients, packaging, and assembly-line worker wages will double too.

With production increases or decreases, fixed costs do not change. For instance, such changes in production don’t have an effect on factory supervisor cost. Her salary remains the same, so there is no change in cost. There are some situations in which fixed costs change, but not in the same ratio as production. In the example, the plant supervisor may need to work overtime.


Gross Profits

Gross profit is the difference between revenues and COGS. Companies also calculate the gross margin, the percentage of gross profit over revenue (gross profit divided by revenue).




Gross profit is not what the company gets. There are other costs businesses face. Remember that COGS covers production costs only.


Operating expenses

Operating expenses (OPEX) include the costs to support operations such as general & administrative, marketing, rent, utilities, and services. Indirect purchasing or indirect procurement has a profound impact on OPEX when they negotiate service contracts on technology—examples are SAP S4 or JD Edwards—and professional services, among others.


Operating profit or operating income

The operating profit is the net earnings from the core business. The focus is on performance. Its calculation is as follows:



A positive operating profit means that the company is performing. A negative result from operations means that costs are higher than revenues. Not good.


E – earnings

B – before

I – interest

T – taxes

EBIT is not included in the income statement or P&L, but it is a widely used term. It is not the same as operating profit or operating income. While both consider revenues, COGS, OPEX, depreciation and amortization, EBIT also includes non-operating or other income like that coming from bonds and stocks.

Other income

The income listed in this line item of the income statement included in the EBIT calculation does not come from its core business. For instance, the proceeds from selling a packing machine are other income, if this is not the company’s main activity.



E – earnings

B – before

I – interest

T – taxes

D – depreciation

A – amortization


EBITDA is not included in the income statement either.

It doesn’t include depreciation and amortization. Depreciation is the loss of value of a fixed asset (machine, computers, etc.) over its useful life because of wear and tear. Amortization is the same concept applied to intangible assets like goodwill, patents, and trademarks.


In summary:

Interest and Taxes

A company finances its operations with a combination of debt and equity. This combination is the capital structure. When the company uses debt like bond issues or loans, the P&L shows interest expenses.

Before a business can take the earnings after considering the interest expense, there are expenses for federal and state income taxes (Yeah! A portion goes to the IRS).


Net profits—Bottom line

The net profits or bottom line is the finished line. The moment of truth. The “To be, or not to be” of William Shakespeare’s play, Hamlet. Net profits ARE what the business makes after all costs, including taxes. If the result is positive, the business can distribute or reinvest. A negative result means that the company is not profitable; its costs are higher than its income.

The last core financial statement to cover is the balance sheet.


Balance Sheet

The balance sheet is like a snapshot—a Kodak moment—of a business’s finances. The balance sheet can be historical or forward-looking, a projection or an estimate.

These are the three big buckets.

  1. Assets
  2. Liabilities
  3. Shareholders’ equity


The balance sheet gets its name because of the fundamental equation:



It is the equilibrium between assets on the left side of the seesaw and liabilities and shareholders’ equity on the right side. Liabilities and shareholders’ equity finance assets.



Assets are resources that a business owns or controls. The business expects assets to provide current and future benefits, to generate sales.

There are two pockets: 1. current assets and 2. non-current assets

The most liquid assets go first in each of these pockets—current and non-current assets.

Current means that the assets can turn into cash sooner than in 12 months. Current assets include:

  1. cash and cash equivalents
  2. marketable securities
  3. accounts receivable (AR)
  4. inventory
  5. supplier prepayments
  6. prepaid expenses


  1. Cash and cash equivalents: These are the most liquid of all assets. Available. Ready to use.
  2. Marketable securities: A company can convert these assets into cash with short notice. They have a maturity of three months or less.
  3. Accounts receivable (AR): Sales revenue on credit. When a company sells to retailers, the company doesn’t get paid until months later, after the delivery of the goods. The amount owed is in AR. When the company gets the payment, it reduces AR and increases cash and cash equivalents for the same amount.
  4.  Inventory: Top priority for the supply chain and procurement.
  5.  Supplier prepayments: These are payments in advance to suppliers before receiving the materials or products.
  6.  Prepaid expenses: The company has made the payment in advance; for example, marketing campaigns and insurance.


Non-current means that the assets can turn into cash after 12 months.

Non-current assets include:

  1. Fixed assets like property, plant, and equipment (PP&E).
  2. Intangible assets such as intellectual property and goodwill.



A liability is money that the company owes to third parties, including suppliers, creditors, the government, and employees. Liabilities also show on the balance sheet based on their liquidity. As with assets, there are current and non-current liabilities. Current liabilities are the amount due in 12 months, while non-current liabilities are the amount due after 12 months.

Current liabilities include:

  1. Accounts payable: The amount that the company owes its suppliers is in AP. When the business makes a payment, it reduces AP and reduces cash and cash equivalents for the same amount.
  2. Wages payable: these are liabilities for wages earned but not yet paid. As such, the amount in wages payable is a short-term obligation, due within 12 months.
  3. Interest payable: this is the amount of interest owed.
  4. Dividends payable: this is the amount of dividends owed. This can happen when the company approves the dividend but needs to make the payment.
  5. Customer prepayments: These are payments in advance that the business receives from customers.
  6. Current portion of long-term debt: the amount due in 12 months or less.


Non-current liabilities include:

  1. Bonds payable: companies may issue bonds to get funds to finance their operations. This is the amortized (remaining) amount of the bonds.
  2. Long-term debt: the amount due after 12 months, based on the debt schedule. The debt schedule shows the outstanding debt, the interest expense, and the payments against the borrowed capital that the company needs to make in every period.



Equity—also known as shareholder’s equity or net worth—consists of what the owners or shareholders own. The equity calculation is as follows:

Equity=Total assets-Total liabilities

Equity has two main items:

  • Paid-in capital—the dollar amount that the owners paid when the company started or, in the case of the shareholders, the dollar amount paid with the first stock issue.
  • Retained earnings—the profits kept at the company for reinvestments.



Paid-in capital

The paid-in capital or contributed capital is the amount of cash or other assets that shareholders have given a company in exchange for stock at the initial issuance.

It can be common stock and preferred stock. There can also be additional paid-in capital or capital surplus. This is the amount that the shareholders have invested above that of common and preferred stock.

Retained Earnings

This is the generated profits or net income that the company does not distribute. It is like the umbilical cord with the P&L. The P&L shows the net profit on the bottom line. From there, the company may decide to distribute. That part goes to the pockets of the shareholders. What is left goes to retained earnings in the balance sheet.


We now have the three core financial statements covered. As a supply chain and procurement professional, you impact them in a massive way.


Excel Functions for Demand Planning

Excel Functions for Demand Planning

In today’s dynamic business environment, supply chain and demand planning have become critical to successful businesses. As a result, demand planners and supply chain managers are constantly looking for ways to improve their planning processes to optimize inventory levels, minimize lead times, and improve customer service levels. Excel is a powerful tool widely adopted for demand planning due to its ease of use and flexibility. This blog post aims to provide insights on effectively utilizing Excel functions for demand planning.


Importance of demand planning for supply chain management


Demand planning is an essential process that helps businesses anticipate customer demand and plan their inventory levels accordingly. Effective demand planning ensures that the right products are available at the right time, in the right quantity, and at the right location. This helps businesses reduce inventory carrying costs and ensures customer satisfaction by minimizing stockouts and backorders.


Supply chain management involves coordinating all activities producing and delivering goods and services to customers. Demand planning is a critical component of supply chain management as it helps businesses to optimize their supply chain processes, reducing lead times and improving customer service levels.

How useful is Excel for Demand Forecasting?


Excel is a powerful tool that provides a range of functions that can be used for demand planning. These functions include forecasting, data analysis, what-if analysis, and optimization. These functions enable demand planners to make data-driven decisions, improve forecast accuracy, and optimize inventory levels to meet customer demand. In the following sections, we will explore these Excel functions in more detail and provide insights on effectively using them for demand planning.

Excel’s forecasting functions for Demand Forecasting


Excel provides a range of powerful forecasting functions that can be used for demand planning. These functions include moving averages, exponential smoothing, and regression analysis. Let’s understand how these functions are built, and later we’ll see how to use them with real data:

1. Moving Averages: Moving averages are a simple and commonly used forecasting technique in demand planning. They involve calculating the average of a specific number of periods of historical data, which is then used as the forecast for the next period. To use moving averages in Excel, you can use the AVERAGE and OFFSET functions to define the period range.

The formula for a 3-period moving average in cell B3 would be =AVERAGE(OFFSET(A3,-2,0,3,1)).

2. Exponential Smoothing: Exponential smoothing is a more sophisticated forecasting technique that assigns more weight to recent data points while assigning decreasing weights to older data points. This technique assumes that recent data is more relevant to future demand than older data. To use exponential smoothing in Excel, you can use the EXPONENTIAL SMOOTHING function, which allows you to define the smoothing factor.

The formula for exponential smoothing with a smoothing factor of 0.2 in cell B3 would be =EXPONENTIAL_SMOOTHING(A3,B2,0.2).

3. Regression Analysis: Regression analysis is a statistical technique identifying the relationship between two or more variables. In demand planning, regression analysis can be used to determine the factors that influence demand and forecast future demand based on these factors. To use regression analysis in Excel, you can use the LINEST function, which provides the regression equation coefficients.

The formula for regression analysis with two independent variables (X1 and X2) in cells B3 and C3 would be =LINEST(A3:A8, {X1range, X2range}, TRUE, TRUE).


Choose the appropriate forecasting technique based on the data and the demand pattern. One important point is to use sufficient historical data to ensure accurate forecasts.

Demand planners must regularly review and update forecasts based on new data and changes in demand patterns. They should also use visualizations to better understand the data and forecast results.


Case Study: Demand Planning for an FMCG Company using Excel


Let’s understand the application of these formulae with the help of a quick case study. This case study is about an FMCG company named ABC Foods, for which we have the historical demand, and we will see how future demand can be forecast with the help of Excel functions.


Step 1: Collecting and Analyzing Historical Data


To begin the demand planning process, the supply chain team at ABC Foods collects historical sales data for the past 12 months for its snack products. The data includes monthly sales figures for each product SKU.



Month Sales
Jan 150
Feb 180
Mar 200
Apr 170
May 190
Jun 220
Jul 240
Aug 250
Sep 210
Oct 230
Nov 240
Dec 260



Next, they use Excel’s data analysis functions to analyze the data and identify patterns or trends. Here’s how they do it:

Descriptive Analytics:


To calculate descriptive analysis for the sales data, the team uses the following formula in Excel:


=AVERAGE(B2:B13) – Calculates the average weekly sales

The result will be 204.17. The average sales can help demand planners understand the baseline level of demand for a product and to detect any seasonal or trend changes that may be occurring. It can also be used as a starting point for forecasting future sales, particularly if the sales history is relatively stable.


=MEDIAN(B2:B13) – Calculates the median weekly sales

The result will be 205. The median sales can provide a more robust measure of central tendency if the sales data has extreme values or outliers. This can be particularly useful if the demand for a product is subject to occasional spikes or dips that can skew the average.


=STDEV.S(B2:B13) – Calculates the standard deviation of weekly sales

The result will be 34.96. The standard deviation of sales can provide an indication of the variability or volatility of demand. A high standard deviation indicates that demand is more variable, which can make forecasting more challenging. It can also be useful for determining safety stock levels and identifying potential supply chain risks.


In demand forecasting terms, the average, median, and standard deviation of historical sales data can provide important insights and help demand planners make more accurate and informed decisions while forecasting.


Step 2: Forecasting Future Demand


Once the team has analyzed the historical data, they use Excel’s forecasting functions to predict future demand. Here’s how they do it:

Moving Averages:

To calculate a rolling average of weekly sales for each product SKU, the team uses the following formula in Excel:


=AVERAGE(SalesData[Week1]:SalesData[WeekN]) – Calculates the average weekly sales for the past N weeks


To calculate a 3-month moving average, we’ll start with the third month (March) and calculate the average of the previous three months’ sales. So, in cell B4, we’ll enter the following formula:




This will give us a moving average of 177.67 for March. We’ll then copy this formula down to the rest of the cells in column B to get the moving averages for the remaining months:



Month Sales 3-Month Moving Average
Jan 150
Feb 180
Mar 200 177.67
Apr 170 183.33
May 190 186.67
Jun 220 193.33
Jul 240 216.67
Aug 250 236.67
Sep 210 236.67
Oct 230 233.33
Nov 240 226.67
Dec 260 243.33



Exponential Smoothing:


To smooth out any fluctuations in the data and identify underlying trends, the team uses the following formula in Excel:


=EXPONENTIALSMOOTHING(SalesData, Alpha) – Calculates the exponentially smoothed values of the sales data with a specified smoothing factor (Alpha)


To perform exponential smoothing, we’ll first need to determine the smoothing factor, alpha. In exponential smoothing, alpha is the smoothing factor or smoothing constant. It is a value between 0 and 1 that determines the weight given to the most recent observation in the time series. A larger alpha value assigns more weight to the most recent observation, resulting in a faster response to changes in the data.


Let’s assume alpha is 0.3. To calculate the first forecast, we’ll simply use the first month’s sales figure. So, in cell C4, we’ll enter:




This gives us a forecast for January of 150. To calculate the forecast for February, we’ll use the following formula in cell C5:


=C4 + 0.3 * (B3 – C4)



Month Sales 3-Month Moving Average Exponential Smoothing Forecast
Jan 150 150.00
Feb 180 156.50
Mar 200 177.67 179.05
Apr 170 183.33 174.94
May 190 186.67 183.70
Jun 220 193.33 207.62
Jul 240 216.67 233.73
Aug 250 236.67 246.41
Sep 210 236.67 230.49
Oct 230 233.33 231.60
Nov 240 226.67 237.08
Dec 260 243.33 251.56





In conclusion, demand planning is a critical process that helps businesses optimize inventory levels, reduce carrying costs, and improve customer satisfaction by ensuring the right products are available at the right time and location. Excel is a powerful tool for demand planning due to its ease of use and flexibility. Its forecasting functions, including moving averages, exponential smoothing, and regression analysis, enable demand planners to make data-driven decisions, improve forecast accuracy, and optimize inventory levels. By analyzing historical data and identifying patterns and trends using Excel’s data analysis functions, demand planners can make informed decisions and update forecasts regularly.





From Data to Decisions—The Role of Analytics in Supply Chain Planning

From Data to Decisions—The Role of Analytics in Supply Chain Planning

Today, manufacturers, distributors, retailers, and logistics providers form a complex global supply chain. In this environment, informed decisions that optimize performance throughout the supply chain are crucial. These decisions for supply chain performance require accurate and timely data for supply chain planners.


Data helps supply chain planners see patterns, trends, and relationships that improve performance. And today, data is available in abundance. With sensors, social media, and customer reviews providing extensive data, the challenge is now extracting insights from it.


Data analytics can help companies make informed decisions. Analytics can improve demand forecasting, optimize inventory, optimize transportation costs, and improve supply chain performance. Analytics report past performance and suggest ways to improve.


Data is crucial to supply chain planning, and analytics helps turn data into actionable insights and decisions. This blog will discuss how data and analytics can improve supply chain performance and business success.


The Importance of Data in Supply Chain Planning


The success of any supply chain planning process depends on the quality and accuracy of the data used for decision-making. Data provides a comprehensive view of the supply chain, enabling planners to identify patterns, trends, and relationships that impact performance. Supply chain planners cannot make informed decisions without accurate data.


Supply chain planners must track various data types to achieve optimal performance, including demand, inventory, and logistics. Demand data helps planners understand customer needs, preferences, and behavior. It includes historical and current sales data, customer orders, and market trends. Inventory data provides insight into the stock levels of finished goods, raw materials, and work-in-progress, enabling planners to optimize inventory levels. Logistics data helps planners understand the performance of logistics providers, including carriers, warehouses, and freight forwarders.


But just getting any data is not enough. Data accuracy is as important as data availability. Inaccurate data can lead to incorrect decisions, significantly impacting the supply chain’s performance. For example, if demand data is erroneous, planners may not be able to anticipate fluctuations in demand, resulting in stock shortages or excess inventory. Inaccurate inventory data can lead to overstocking or stockouts, increasing costs, and decreased customer satisfaction. Logistics data inaccuracies can result in delayed shipments, increased transportation costs, and lower service levels. Hence understanding the importance of data availability and data accuracy together is crucial for businesses starting to venture into the world of Supply Chain Analytics.


The Power of Analytics in Supply Chain Planning


Data is only valuable when transformed into insights that enable effective decision-making. This is where analytics comes in. Analytics can turn data into actionable insights that drive supply chain performance improvements. The different types of analytics relevant to supply chain planning include descriptive, predictive, and prescriptive analytics. Let’s understand them one by one.


  1. Descriptive analytics involves analyzing past data to understand what happened in the supply chain. It helps supply chain planners understand performance trends, patterns, and relationships that impact supply chain performance.
  2. Predictive analytics involves analyzing past data to predict future supply chain performance. It helps supply chain planners anticipate demand fluctuations, inventory shortages, and production delays.
  3. Prescriptive analytics involves analyzing past data to identify the best action to optimize supply chain performance. It helps supply chain planners make data-driven decisions to improve performance.


Analytics can help improve supply chain performance in various ways, including demand forecasting, inventory optimization, and transportation planning. For example, demand forecasting can help supply chain planners anticipate future demand and adjust production and inventory levels accordingly. Analytics can help optimize inventory levels by predicting the optimal stock levels needed to meet customer demand while minimizing inventory holding costs. Transportation planning can benefit from analytics by optimizing transportation routes, modes, and carriers to reduce transportation costs while improving service levels.


The power of analytics in supply chain planning lies in its ability to provide real-time, data-driven insights that enable continuous improvement. Supply chain planners can use analytics to make informed decisions that reduce costs, improve customer satisfaction, and drive overall business success.


Implementing Analytics in Supply Chain Planning: Overcoming challenges.


Implementing analytics in supply chain planning can be a challenging process. Several challenges are associated with implementing analytics in supply chain planning, including data quality and availability, technology and software considerations, and organizational culture.


Data quality and availability can significantly challenge implementing analytics in supply chain planning. The accuracy and completeness of data can impact the effectiveness of analytics. Ensuring that the data collected is accurate, timely, and relevant to the business needs is crucial. Incomplete or inaccurate data can lead to flawed insights and suboptimal decision-making.


Technology and software considerations are other challenges for implementing analytics in supply chain planning. Companies need to invest in appropriate technology and software to support analytics initiatives. This includes a robust data infrastructure, including data storage, integration, and visualization tools. Companies must also consider the skills and expertise required to implement and use the technology and software effectively.


Organizational culture is another challenge for implementing analytics in supply chain planning. Companies need to establish a data-driven culture where decision-making is based on data-driven insights. This requires leadership buy-in and support and a willingness to change the organizational culture to embrace data-driven decision-making.


To overcome these challenges, companies must adopt best practices for implementing analytics in supply chain planning. These best practices include:


Establishing a data-driven culture: This involves promoting a culture of data-driven decision-making throughout the organization from the top down.


Investing in technology and software: This involves investing in appropriate technology and software to support analytics initiatives. This includes a robust data infrastructure, including data storage, integration, and visualization tools.


Collaborating with cross-functional teams: This involves working with cross-functional teams to ensure that analytics initiatives are aligned with the company’s overall strategy and goals. It also involves building partnerships with external stakeholders, including suppliers and customers, to improve the accuracy and completeness of data.

The Future of Analytics in Supply Chain Planning


As technology advances, the future of analytics in supply chain planning looks promising. Several current trends and developments in supply chain analytics include big data and AI/machine learning.


Big data is a term used to describe the vast amount of data generated daily by businesses, individuals, and devices. With the growth of big data, there is an opportunity to leverage this data to gain insights into supply chain operations. Big data analytics can help companies identify patterns and trends, detect anomalies, trigger timely decisions, and optimize supply chain performance.


AI/machine learning is another trend that is transforming supply chain analytics. Machine learning algorithms can analyze vast amounts of data to identify patterns and predict future outcomes. This can help companies to make more accurate demand forecasts, optimize inventory levels, and improve transportation planning. AI can also automate repetitive tasks, freeing employees to focus on more strategic activities.


The potential benefits of these trends in supply chain analytics are significant. Big data and AI/machine learning can help companies make more informed decisions, optimize supply chain performance, reduce costs, and improve customer satisfaction. For example, using predictive analytics to forecast demand can help companies improve inventory management, reducing the risk of stockouts and overstocks. This, in turn, can improve customer satisfaction by ensuring that products are available when customers need them.


However, there are also potential challenges associated with these trends. One challenge is the need for skilled analysts and data scientists who can effectively use these tools to derive insights. Another challenge is data privacy and security, as companies need to ensure that customer data is protected.




In conclusion, analytics helps supply chain planners make decisions. Analytics can improve supply chain operations, performance, and customer satisfaction. Thus, analytics will remain crucial to supply chain operations and should be a priority for companies seeking to compete in a complex and dynamic business environment.


How to Avoid These 7 Costly Supply Chain Planning Mistakes

How to Avoid These 7 Costly Supply Chain Planning Mistakes

Supply chain planning is essential to running any business because it determines how effective and efficient your supply chain operations are. It entails forecasting demand, managing inventory, and coordinating logistics to guarantee that products are delivered to customers on time and at the right price. Despite this, many businesses make mistakes in planning their supply chains, which can be expensive. This article will cover seven most typical mistakes and how to avoid them. Companies can ensure that the planning of their supply chains is effective and efficient by being aware of the most common errors that occur and avoiding them. This results in increased customer satisfaction and revenue.


Mistake #1: Lack of Communication

Successful communication between internal teams and suppliers is vital. Lack of communication is the first and most common mistake in supply chain planning. Ineffective communication can lead to wasted time, reworks, and unnecessary additional costs. Regular meetings and processes like S&OPs should be set up to enhance communication and expectation setting. Within an organization, clear objectives and expectations should be set, and roles and responsibilities should be defined for all the teams in the supply chain. In addition to this, the supply chain teams and leaders should be in touch with the other business teams, like sales, marketing, R&D, or regulatory, so they are on top of the business plans. Such communication setup will help prevent supply chain surprises and last-minute rush.


Mistake #2: Inaccurate Forecasting

Accurate forecasting is essential for the supply chain. It enables businesses to anticipate demand and plan the subsequent steps accordingly. These can range from adding capacity including hiring new resources, or negotiating with suppliers to ensure that the right products are available at the right time and place. However, inaccurate forecasting can throw the rest of the planning in trouble. It can result in overstocking or stockouts, leading to increased expenses and decreased customer satisfaction. To improve forecasting, businesses should utilize historical data as a starting point, involve key stakeholders such as sales and marketing teams, and religiously drive S&OP or IBP – the most advanced phase of S&OP – processes. Advanced analytics tools, such as statistical forecasting, can also improve forecasting accuracy.


Mistake #3: Inflexible Planning

The third mistake is failing to plan in a flexible manner. Flexibility is an essential quality for businesses to possess. The inability to be flexible in one’s planning can lead to missed opportunities and additional costs. Just-in-time inventory strategies, which enable businesses to order only what they require at the precise time needed, require flexible supply chains. In addition, companies should work to develop relationships with several different suppliers so that they have a variety of fallback options available to them if there are disruptions in the supply chain.


Other areas of making the supply chain leaner should also be explored, like running six sigma projects, finding optimum batch sizing, or Single Minute Exchange of Die (SMED) approaches.


Mistake #4: Lack of Visibility

Lack of visibility is the fourth most common mistake that companies make. When businesses have adequate visibility, monitoring shipments, orders, and inventory is much more straightforward. When there is insufficient visibility throughout the supply chain, it can lead to delays as well as additional costs. To improve their ability to see what’s happening, businesses should implement technological solutions such as automated inventory management systems and real-time tracking tools. These tools can provide businesses with information on orders, inventory levels, and the status of shipments that is updated in real-time. Companies should frequently review key performance indicators (KPIs), including on-time delivery, inventory turnover, and supplier performance.


Mistake #5: Not Considering Risk

Businesses should regularly evaluate their supply chains to identify potential risks and develop mitigation strategies and business continuity plans (BCPs) to address them. One of the most typical oversights companies make when planning their supply chains is to fail to consider risk and create a contingency plan for it. For example, diversifying a company’s pool of suppliers is one way for businesses to keep their operations running smoothly and reduce the likelihood of experiencing disruptions in their supply chains.


Mistake #6: Not defining processes.

In supply chain planning, failure to define processes can lead to confusion and inefficiency, resulting in increased expenses. To avoid this mistake, businesses should implement supply planning procedures like regular inventory counts, detailed demand forecasting, and running S&OP and IBPs. In addition, having a clear plan for dealing with disruptions and unanticipated events can reduce their negative impact on the supply chain. By incorporating processes, businesses can ensure a streamlined and effective supply chain, saving money and enhancing customer satisfaction.


Mistake #7: Not Building Strong Relationships

The seventh common mistake is failing to cultivate healthy relationships. Establishing and maintaining solid relationships with suppliers is critical for supply chain management. Companies must keep an open line of communication with their suppliers to keep them apprised of any demand shifts or potential problems that may arise. Businesses can reduce the risk of disruptions to their supply chains and maintain the continuity of their operations by cultivating strong relationships with their suppliers.


In summary, supply chain planning is an indispensable activity for a profitable business operation. Businesses can improve their supply chain performance while simultaneously lowering costs if they avoid making these seven costly mistakes. It is essential to keep in mind that planning for supply chains is a process that is ongoing and calls for continuous monitoring and adaptation.


For companies to ensure that they are satisfying the requirements of their customers and remaining competitive in the market, supply chain strategies should be evaluated and revised regularly. The bottom and top lines of businesses can be improved, and they can gain a competitive advantage in the market if they invest in supply chain planning.


Outsmarting Supply Chain Vulnerabilities: A Guide to Planning and Forecasting—Guest Post by CEO DB Schenker, David L. Buss

Outsmarting Supply Chain Vulnerabilities: A Guide to Planning and Forecasting—Guest Post by CEO DB Schenker, David L. Buss

In today’s digital economy, businesses have come to rely heavily on their supply chain networks. While the efficiency and cost savings associated with these networks are undeniable, they also present a unique set of vulnerabilities that must be managed and monitored to remain successful.


Since the pandemic hit in 2020, supply chain risks have become even more significant. Any disruption has the potential to send a ripple effect through the entire chain. With increased demand and disruptions to global trade, companies must be prepared to take proactive steps to mitigate the potential impacts of supply chain vulnerabilities.


However, a clear and comprehensive approach to supply chain management is needed to do this, including robust planning and forecasting capabilities that can help businesses anticipate potential disruptions before they occur and take appropriate action.


Identifying Weaknesses in Today’s Supply Chain


The container shipping industry, in particular, has experienced a range of disruptions that continue to impact. From rising container costs to volatile freight rates and decreased global trade, these challenges present significant obstacles for companies reliant on international commerce.


Shippers face extended lead times and unpredictable delays due to dwindling warehouse storage capacity and limited labor pools. This makes it increasingly difficult for businesses to achieve their goals in an affordable manner, leading to higher costs and lower profits. While some companies have been able to weather the storm thus far, there’s no clear end in sight, with conditions expected to stick around for the foreseeable future.


The best way to handle supply chain weaknesses is by taking proactive steps towards adaptation and finding creative solutions.


Strategies for Mitigating Supply Chain Risk

Successful supply chain management requires long-term and short-term strategies to help businesses remain competitive in today’s market. This includes the development of comprehensive planning and forecasting processes that can help organizations anticipate potential risks before they occur.


Proactive planning and forecasting should consider a range of variables, including economic conditions, fluctuating demand levels, supplier capabilities, and inventory levels. By creating detailed plans for each area, companies can better prepare for changes in the market or disruptions within their supply chain network.


Additionally, risk assessment tools such as “what if” analysis can be utilized to identify potential problem areas before they become a reality. This allows shippers to anticipate issues and adjust accordingly, helping them minimize delays and financial losses.


How Demand Planning and Forecasting Solutions Can Help

Demand planning and forecasting solutions have become essential tools for organizations that want to maintain a successful supply chain in such turbulent times.


Demand planning allows companies to look into future demand with data-driven forecasts based on sales history and market patterns. This helps them anticipate customer needs, enabling them to adjust production levels or inventory accordingly. Forecasting solutions leverage predictive analytics and machine learning algorithms to analyze customers’ past and current behavior from different sources, providing accurate predictions about their future demands.


These two solutions can be very effective in helping disrupted supply chains by giving organizations visibility into future customer needs. This enables them to manage better inventory levels, production capacity, staffing levels, and other resources required to meet these demands. It also helps reduce costs associated with overproduction or underproduction due to inaccurate forecasts. Advanced analytics can identify when changes in demand occur quickly so that organizations can respond faster than ever.


Furthermore, integrating demand planning and forecasting solutions into existing systems enables real-time collaboration between departments within an organization – which is extremely important when dealing with disrupted supply chains. By connecting the dots between marketing, sales, operations, and finance departments, companies can take a more holistic approach to manage disruptions while maintaining high levels of customer service throughout the entire process.


Common Forecasting Mistakes Businesses Should Avoid


Many businesses rely on forecasting to make decisions and plan for the future. It is crucial for companies to forecast properly, as mistakes can be costly and significantly impact the business’s performance. To ensure accuracy in their forecasts, it is essential for companies to consider some common mistakes that could lead to costly errors in your planning process.


Accounting for Changes in Consumer Behavior


Businesses need to be prepared to identify changes in consumer behavior and adjust their forecasts accordingly. Failing to do this could lead to an inaccurate forecast that does not reflect reality. One way to do this is through customer surveys and market research. Companies should also pay attention to trends in their industry, as they could be indicative of changing consumer preferences.


Ignoring Seasonal Variations


Businesses need to factor in seasonal variations when preparing forecasts, as these can greatly impact demand. For example, certain products may experience higher demand during holidays or special occasions, whereas others may not have any significant seasonality. Failing to consider seasonal changes can lead to poor forecasting accuracy, resulting in incremental losses for the company over time.


Failure to Account for Risk Factors


Risk factors are an important part of any forecast – but businesses often overlook them. These include economic conditions, supplier reliability, political changes, and natural disasters, all of which can significantly impact demand. Failing to account for these factors could lead to an inaccurate forecast that fails to reflect reality, leading to bigger problems in the future.


Relying on Manual Calculations


Manual forecasting is often time-consuming, tedious and error-prone. This can lead to inaccurate results that don’t reflect the actual demand for a product or service. To ensure accuracy, businesses should consider using predictive analytics or forecasting software, which can quickly and accurately generate forecasts with minimal effort.


Stay Ahead of the Curve by Leveraging Forecasting and Planning


Forecasting demand is a crucial part of supply chain management. Businesses need to be prepared to account for changes in consumer behavior, seasonal variations, and risk factors and rely on predictive analytics or forecasting software to help minimize errors and ensure accuracy. By adopting these practices, companies can better manage their inventory levels and production capacity while ensuring the business can remain agile in changing industry conditions.


Author Bio:

David L. Buss

David is CEO of DB Schenker USA, a 150 year old leading global freight forwarder and 3PL provider. David Buss is responsible for all P&L aspects in the United States, which is made up of over 7,000 employees located throughout 39 forwarding locations and 55 logistics centers.