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A Comprehensive Guide to Predictive Inventory Planning and Management

Business Leadership Inventory Optimization & Management Supply Chain Management Supply Chain Planning
08/04/2020

Inventory management remains an important operational feature that determines the successes and failures of enterprises especially in the manufacturing and retail industries. For inventory management, the challenges for most enterprises are similar at a basic level. The mom and pop shop that supplies nearby factory workers with homemade buns and the large corporation providing products across continents must deal with keeping the shelves full of raw materials, ingredients or products but not too full…

Hacking the process of optimizing inventory leads to a happy consumer base, speedy sales, and an optimized budgeting and expenditure structure for enterprises. On the other hand, inadequate inventory management or levels lead to reduced sales, decimated profits, and unhappy customers who will leave their feedback by spending elsewhere. These reasons are why businesses must understand the inventory planning and management needs to optimize available inventory.

The Value of Inventory

To understand the value of inventory and the need for optimized management, one must first understand the different forms inventory takes within enterprises that produce, distribute, or sell products. Inventory includes raw materials in line for processing, materials going through the production cycle, and the final product which have not been sold.

At every section of the production cycle, these inventories represent working capital in stasis. The moment the product leaves the plant floor or shelf as a purchased good, that’s when it stops being inventory. It is also important to note that the total cost of inventory includes the cost of transporting, storing, and managing inventory. Thus with every batch of material scrap comes big losses due to the resources put into bringing inventory to its designated areas of use.

The Challenges Faced with Inventory Management

As stated earlier, the cost of managing inventory is one that must be considered when planning and making-decisions for production. Studies on the material handling cost for inventory have shown that for small and mid-sized businesses, carrying costs account for 25 to 30% of the value of inventory. This is because specific inventory must be properly packaged, secured, and shipped to retain their value and eliminate shrinkage.

In addition to cost, risk one of the more important challenges organizations face with inventory management and it comes in many forms. Risk can be an unforeseen hike in gas prices, internal unrest within regions or natural phenomena that affect the availability of raw materials. In situations where unforeseen events pose risk to supply chains, organizations that rely on an un-diversified supply chain will struggle with inventory management.

Inventory can also be affected by how consumers view particular vendors. Today, green consumption and ethical production have become important factors for customers. Thus, a negative news report about an organization's supplier could affect inventory. On the more positive side, if planned for, a holiday or viral post on social media platforms can spark immediate interest in a product and proper inventory management is needed to meet the overnight increase that comes with going viral.

Other risk consumers within the food and beverage industry must navigate is the finite shelf-life of the ingredients they use and the products they manufacture. With these challenges in mind, it becomes obvious that a predictive inventory management process is the only viable solution to navigating through the challenges businesses face in the modern world.

What is Predictive Inventory Management?

Predictive inventory management refers to the integration of predictive analytics to forecast the future risks that have the potential to disrupt inventory availability. The analytical process takes into consideration fluctuating demand, which is demand forecasting, historical data, and economic trends to proactively manage inventory. Thus, predictive inventory management includes demand forecasting, supply chain planning, and assortment planning.

Implementing predictive analytics in inventory management provides organizations within the manufacturing and retail industries with multiple benefits. These benefits include:

  • An increased cash flow due to reductions in the working capital spent servicing over-estimated demand requirements.
  • Improved customer service by meeting consumer requests or orders in a timely fashion.
  • Optimizing machine utilization and productivity within manufacturing plant floors.
  • Reducing scrapping, shrinkage, and other damages related to inadequate inventory management.
  • Predict the correct amount of materials or ingredients a facility needs which eliminates stockouts and redundant stock.

How Predictive Inventory Management Works

Like all analytical processes, predictive inventory management involves making sense out of a large assortment of data. These data sets include historical customer demand data, industry benchmark data, supply chain data, and internal material demand data from the plant floor to forecast the need for specific materials. Businesses that employ the conventional process to inventory management generally relied on the orders made by local retailers or merchandise managers to determine their inventory needs.

This process leaves huge gaps in determining customer demands and other risk factors. Thus leading to excessive scrapping, over-production or reduced service-levels depending on the shortfalls in demand. The predictive process tries to eliminate the guesswork in the conventional process by filtering and analyzing inventory data plant by plant to account for all the different variables that affect inventory and demand. So, here is an outline of the process:

  • Filter and aggregate available data to create a management model
  • Include constraints into the model
  • Analyze the model to get the inventory and demand forecast results needed to develop accurate inventory management plans.

Facilities with tens of shops or plants will struggle to accomplish these three processes using reams of binders or an excel sheet. As every shop will require its inventory and has its particular constraints which are native to its location or region. For organizations producing millions of data sets or even thousands, inventory planning becomes a very complex proposition to execute. This is where the need to automate the inventory planning and management process.

Automated solutions provide organizations with agile control of the planning and management process. This includes scaling the system to include data from other facilities, and third-party suppliers and the ability to add new constraints in near real-time. These constraints could be new trends or even weather conditions thus resulting in more accurate forecasting and inventory management plans.

Automated Predictive Inventory Management Options

To automate the integration of predictive analytics in demand forecasting and inventory management, diverse applications have been developed to handle the process. These software applications include:

  • Vendor managed inventory solutions - Vendor managed inventory refers to a process of executing inventory management. In this process, the supplier of a product, which is generally its manufacturer, is responsible for optimizing the inventory held by a distributor or retailer.

In many cases, the VMI process means the retailer does not own the goods but houses them for sale to the consumer. In situations where the items are not sold, the manufacturer takes them back and bears the losses. When the items are sold, the retailer takes a commission for sales and returns the balance to the manufacturer.

 

In this practice, the manufacturer is responsible for determining what gets on the shelf that customers can purchase. The manufacturer can either make use of a third-party logistics provider to handle its demand forecasting or use VMI software to do the analysis. VMI applications are limited to handling analytics based on the practice. While they are capable of aggregating historical data they cannot integrate constraints to make an accurate demand forecast. This can be limiting for organizations that want to scale up or reevaluate their approach to inventory management.

 

  • Market Basket Analysis Solutions - The market basket analysis process to inventory management analyses historical customer data to detect purchasing patterns. Analyzing customer behavior patterns help with developing multiple products or combining products into packs/offers to meet the purchasing patterns of consumers.

 

This process and practice help with managing sales and getting consumers to purchase more items. What market basket analysis software does not do, is predict demand which is the most important factor in managing inventory. Thus, they are generally viewed as more of a product design and sales tool than a predictive inventory management solution.

 

  • Supply Chain Planning (SCP) Software - The supply chain planning software adopts an agile approach to inventory management which takes into consideration demand forecasting, supply planning, capacity planning, and distribution requirements planning. The agility a supply chain planning software brings to the table ensures that every form of constraint can be introduced into the analytical process to produce accurate evaluations. This makes SCPs full-scale predictive inventory management tools.

 

Supply chain planning software also provides the tools needed to execute vendor managed inventory and market basket analysis which makes it a more robust tool for inventory management. The SCP collects data from inventory sources, organizes the data, integrates constraints, and conducts the required analysis. Once the analysis is conducted, it interprets evaluations to help organizations identify the important factors driving sales and customer demand. It also provides insight into a supplier's capabilities and preferences which help optimize supply chain logistics.

 

Supply Chain Planning Software and Optimizing Your Inventory Management Process

The extended capability supply chain planning software makes it possible to automate predictive inventory management among other benefits. These benefits include:

  • Developing agile inventory strategies - The tools SCP provides enable you to understand the significant factors that affect the cost and service-levels of the inventory strategy you currently use. With this insight, effective cost-cutting measures can be put in place to optimize the inventory management process.
  • Simulate and Predict Outcomes - SCP software provides you with an intelligent platform capable of answering what-if questions. With the data it captures or inputted in it, enterprises can introduce constraints or evaluate diverse scenarios to determine the business outcomes and its effect on available inventory or the supply chain. This makes supply chain planning software effective decision-making tools due to the insight and predictions they provide.
  • Share Insights to Enhance Collaboration - SCP software doubles as a collaboration tool for organizations interested in ensuring every facet of their operations is in sync. This includes the various departments involved in production processes, third-party suppliers, and customers. Ensuring everyone has access to production-related timelines enables the involved parties to understand their responsibilities and the importance of meeting specified schedules.

 

Conclusion

As with most business processes, the journey to optimizing inventory management is a continuous process which demands constant evaluation. Today, predictive analytics supply chain planning software brings to inventory management remains the best option to optimizing your inventory and supply chain management process. You can learn more about increasing business revenues by downloading our e-book on accelerating business growth with a connected supply chain.

 

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