Demand Planning- Your Guide to Optimizing Labor & Inventory

Optimization should be at the heart of every business's goals. By planning for finances with demand in mind, management can match their outputs to the expected sales levels and avoid unwanted expenses.

Many managers struggle with finding this delicate balance and planning accurately, especially during more unpredictable times, such as the holiday seasons. For instance, more than 80% of supply chain managers say that adequately planning for supply chain capacity to address peak requirements, or timely fluctuations in customer demand is one of the biggest challenges they face in their roles.

The process of demand planning, therefore, is crucial for improving product forecasting accuracy and optimizing staff and supply chain scheduling. As a result, businesses would also be able to enjoy accurate budgets for better cash flow management.

Finding these optimal points for staff scheduling, inventory ordering, as well as other aspects of operations, can involve various complex calculations. However, with accurate financial reports and forecasting systems, management can rest assured they are on the path to optimization.

Demand Planning vs. Demand Forecasting

The two processes may have a lot in common, as they are both components of demand management. However, they must be viewed as separate factors, as understanding their distinctions can only elevate the ability to successfully manage business operations.

Forecasting for future demand is the process of using statistical analysis to predict the number of expected sales in the coming weeks or months. By using historical data and analysis of current trends in customer behavior, managers can predict expected demands. The mathematical method of forecasting is efficient, but not in and of itself sufficient. This entire process is dependent on using accurate, quality information, as the results will only be as precise as the quality and volume of the data it is based on.

Demand planning is a process that includes forecasting and the operational procedures that follow. In other words, demand planning takes the forecast and translates it into actionable steps. In addition to demand forecasting, planning includes processes such as trade promotion and product portfolio management.

Planning has an impact on all areas of the business, from sales and marketing to operational logistics and production. With reliable forecasts, an organization can cut operating costs, mitigate risks, and improve the overall quality of their service by appropriately meeting the expectations of their consumers.

Demand Planning for Inventory

Optimizing inventory planning and ordering strategy by finding the perfect balance between under and over-stocking is ideal, especially considering how quickly inventory-related costs can add up.

Zebras 2017 study estimated that the worldwide cost of inventory distortion is over $1.1 trillion. To avoid contributing to this statistic, it is crucial to match the inventory with demand as accurately as possible by meticulously tracking stock and utilizing mathematical demand forecasting.

Inventory turnover rate

This is an efficiency ratio that demonstrates how well the inventory is being managed and how quickly it is being sold. This is calculated by determining the ratio of the cost of goods sold (COGS) and the average price of inventory for a given period.

For example-

A retailer has a COGS of $70,000 in a selected period, while they stocked $10,000 of inventory on average. By dividing COGS with the average cost of inventory, they can calculate that their turnover rate was 7 for that period.

Inventory Turnover Rate = COGS / Average Cost of Inventory

7 = $70,000 (COGS) / $10,000 (Average Cost of Inventory)

A low turnover ratio indicates that a business is overstocking, whereas a higher ratio signals under-ordering. To calculate the number of days a business needs to sell their inventory, we simply divide the number of days with the inventory turnover rate.

These numbers vary significantly across industries, as certain items can have shorter or longer shelf lives. By looking at the industry benchmarks, businesses can compare their numbers to other companies and get an idea of how to better manage their stock.

Safety stock

Safety stock refers to having additional inventory on hand for unexpected demand. For instance, a fashion company that sells jackets calculates the average demand for coats based on historical data.

However, if the upcoming winter season is expected to be colder than average, more consumers than expected may wish to purchase a jacket. Demand planners can take this into account when ordering inventory by calculating the necessary level of safety stock.

For example-

It takes 50 days (average lead time) to get the coats made and shipped, and it can take up to 60 days (maximum lead time). On average, the company sells 10 per day (average sales volume), but at peak times this can rise as high as 20 per day (maximum sales volume).

Safety stock = (Maximum sales volume x Maximum lead time) (Average sales volume x Average lead time)

700 = (20 x 60) (10 x 50)

According to this formula, the clothing company would need to carry 700 additional coats to meet the unusual seasonal peak in demand.

Reorder point

By establishing the point at which items must be reordered, businesses can prevent inventory shortages.

For example-

The retail company has established that the average time to make and ship their products is 50 days (average lead time), their average sales volume is 10, and the safety stock is 700.

Reorder Point = (Average Sales Volume x Average Lead Time) + Safety Stock

1,200 = (10 x 50) + 700

Therefore, the company would have to place a new order when the stock for this item falls below 1,200 units. This would prevent any possibilities of stockouts, which can cause poor customer satisfaction and lost sales due to missed opportunities.

Demand Planning for Labor

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Planning for labor demand can be achieved with multiple quantitative and qualitative approaches.

It is an integral part of a companys success since the number of workers available can heavily influence costs and the quality of service provided to customers.

Managerial judgment

Managers often rely on their years of experience to estimate the number of employees theyll require for upcoming periods.

As a subjective, qualitative method, this is usually unreliable and shouldnt be used by larger businesses. Managerial judgment should be complementary to other methods, not a standalone approach.

Workload analysis

Also known as the work-study technique, which helps managers determine the standard time it takes to produce a unit.

For example-

A company usually produces 500,000 items a year. Making a single item takes, on average, 2 hours. It would take 1 million hours to meet the estimated production. By looking at past data, a manager can calculate that a worker puts in 1,500 hours, on average, each year. Absenteeism, resignations, sick leave, and any other factors that could affect this number should also be taken into account.

Estimated Number of Workers = Estimated Number of Hours Needed to Meet Annual Production / Work Ability of Workers

666. 67 = 1,000,000 / 1,500

By dividing the estimated number of hours required to meet production by the average work-hours achieved by each employee, companies can calculate the number of employees required. In this example, the company would need 600 employees that year.

Econometrics model

This is a mathematical model based on a statistical analysis that shows the relationship between dependent and independent variables. Some of the factors that should be considered are sales, total production, workload, employment trends, and replacement needs.

The models used are complex but very accurate in predicting labor demand. Due to the intricacy of econometric models, most businesses opt for using forecasting software which automates the process.

Growth of Demand Planning Technology

Demand planning used to be purely reactive, with the primary purpose being to sell as much as possible with little regard to customer satisfaction. Today, this process is carried out with consumers in mind, with the goal of meeting their needs rather than the needs of businesses. After all, optimization is far more than just about maximizing profits.

By adjusting inventory levels and staff schedules to expected customer demand and foot traffic volumes, businesses can reduce the risk of long lines and poor customer service. With this goal of increasing customer loyalty and retention through positive shopping experiences, businesses can enjoy a steady flow of revenue from returning buyers.

The many mathematical and statistical forecasting methods used to be carried out manually by data analysts, however, new disruptive technology and models are allowing supply chains to become more efficient in regards to costs, logistics, and customer service. This transition from spreadsheets to software has taken demand planning to a level that no manual human labor can match.

The quantifiable information and analytics used to explain the past can also be utilized to predict the future by examining patterns in available historical data. Modern-day forecasting software accesses this information by integrating directly with a companys point of sale system to pull years worth of data in an instant.

It should come as no surprise that more and more companies are turning to software to deliver these insights for them. AI has become the most crucial aspect of data strategy for 61% of marketers, with almost 90% of them using it for sales forecasting.

A McKinsey study has estimated that mistakes in forecasting can be reduced by 30 to 50% with the help of AI. Machine learning enables real-time forecasting while still taking internal and external factors into account, such as weather changes, economic conditions, and holidays. This kind of technology can improve customer profitability and satisfaction while keeping costs at a minimum, as this eliminates the need to enlist the help of expert statisticians and forecasters to produce complicated reports.

Key Tips For Successful Demand Planning

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The following are some general tips for achieving a successful demand planning strategy. These strategies can be applied to a wide variety of industries and business needs.

1. Implement granular models - Implementing models that inspect every channel and product individually enables more accurate forecasts than just looking at historical averages.

2. Utilize software - Software uses real-time data from POS systems and combines various forecasting techniques and sophisticated algorithms.

3. Have a contingency plan - Unexpected situations can always arise. Therefore, having a plan B to avoid risks is essential.

4. Be flexible - Software can forecast more accurately and quickly than any statistician, but planning for demand cannot be achieved solely by machines as the process includes more than just statistical data. When major factors such as economic changes, political situations, and market conditions could unexpectedly affect demand, managers and owners should step in to make changes accordingly.

Demand planning is a pivotal factor that underpins the success of a businesss supply chain. Due to advancements in technology, managers can rely on software to precisely forecast and budget for the future.

In this way, demand planning becomes a straightforward way to optimize sales and operations planning, retain customers, and increase consumer satisfaction.