There are a few axioms of a tech services business with which everyone is familiar. One of the most critical being: If you make a promise to a customer, you need to meet it. If you don’t make promises, you may not have customers.  A measurement that is commonly used here is OTIF.  OTIF stands for On Time, In Full.  How often do you service your customers on time and in full?  If you must make multiple trips over the course of days or weeks, it drives down this measurement. 
  
Principals like these lend themselves well to the customer driven strategy practiced across industries. A customer driven approach works well, but there’s more to business success than making customers happy.  Tech service organizations need to maximize profits and growth. Improving inventory management can yield impressive results. Unfortunately, traditional Enterprise Resource Planning (ERP) systems and Field Service Management (FSM) fall short. 
  
Adopters of advanced allocation find increased profits, decreased labor costs, and increased customer satisfaction. Using advanced allocation can fill the gaps between your ERP and work order management system to yield improved fulfillment when its most needed. 
 
Like the axiom mentioned above of a customer focused strategy, some principles ring true across the board for advanced allocation. We’re here to walk through the best practices and specifics on how inventory management solutions make it possible. 
 
Flexibility  

​Few things are more important in keeping customer promises than flexibility. Being able to pivot, realign, learn, and adapt are keys to the most successful tech services practices, but the key is being flexible at speed. In a customer focused model, there’s little room for decision made at a daily rate. Industries with enough variables find a lack of instant flexibility in decision making in their strategy becomes a problem.  This instant flexibility is obtained by: 

1. Accurate parts inventory 

1. Allocation of parts to key projects or work orders 

1. Team member decision making that is sandboxed by technology such as machine learning 

 
In periods of low vs. high demand, it is hard to forecast the needs of hundreds of parts, much less the distribution of your parts to techs and ultimately the customers. With advanced allocation, an organization gains the confidence that needed parts will be ready when the work order starts in the future.  If parts are needed that are not allocated, the inventory system can still support using common stock. When a request for use of a part occurs, the system will first prioritize use of allocated parts to a specific group or work order.  If there is not enough inventory, the system will seamlessly move to common stock without the operator needing to know about it.   
 
When common stock is used, this data can be used to predict the future needs of distribution, manufacturing, or field service branches of the company, so come next quarter, there are accurate pictures of steps to be taken. 
 
Advanced allocation also allows you to prioritize specific customers in ways you simply could not before.  
  
Decreasing Lead Times for Key Customers  
 
Across industries, there are dozens of cases of businesses prioritizing faster delivery of parts over price or even product selection, unfortunately even then, parts go out of stock.  When that happens, the business must prioritize who gets limited supply.  There are many strategies to handle lack of parts.  Allocate based on first come, first serve, or allocate based on the importance of the customer.  As unpopular it can seem to shift availability to larger more profitable customers, it sometimes is the most logical approach.   
 
One customer allocates parts into three groups: 

1. Common stock (50% of inventory) 

1. Key customers (40% of inventory) 

1. Active work orders (10% of inventory) 

 
Using advanced allocation when parts are needed for work orders in key or active work order groups, and there is insufficient inventory, common stock is used. To learn more, read the Case Study. 
 
If you’re curious about taking the next step towards advanced allocation and want to see how effective it can be, what ROI you can expect, and what system applications would benefit your field service operations, reach out to us. ​

In the world of inventory, a trend has emerged causing executives and supply chain operations to redesign process and operations. This trend is the use of big data to feed into AI and machine learning. 
 
First things first, what is big data? 

To put it simply, big data is a term that describes a large volume of structured, semi structured, and unstructured data that is collected by companies and organizations and are mined for information. This information proves to be extremely crucial to these companies because they help enhance machine learning, predictive modeling, and other advanced analytics. Big data is usually characterized by its volume, its variety, and the velocity at which it is collected and eventually processed.  
 
Now that we know what big data’s all about, let’s highlight its importance in the industries of inventory and warehouse management. You see, big data has opened a lot of doors in industries such as these, and the opportunity for business growth is colossal in magnitude. Big data is the perfect example that as technology progresses, it propels establishments in even greater strides, and opens new opportunities that lay down the foundations for continual business development and expansion. Big data basically provides companies nowadays with a rich and vibrant pool of real-time data and comprehensive analysis that they can utilize and use to their advantage in making well-informed decisions pertinent to their businesses' strategies and methods in inventory management. 

WithoutWire’s Travis Smith 

In his most recent blog, Travis Smith, the founder, and CSO of WithoutWire, shared his insights on how big data largely influences the way companies nowadays manage to grow their businesses and increase sales through more efficient inventory management. What prompted this realization in him was his short encounter with a magazine article that he chanced upon during his recent trip titled “Meet a Start-up with a Big Data Approach to Hiring”. The write-up basically talks about how big data is being used by various organizations all over the world to help with their respective Human Resource divisions, allowing them to predict the outcome of new hires.  
 
Travis Smith further elaborated that in the many years he spent reviewing and analyzing all sorts of inventories in businesses, what piqued his interest and amazed him was how solution needs for companies differ vastly from one another. He further explained that even for a single company, recommending the best method for a particular process can be very perplexing and challenging.  
 
Eventually though, through the utilization of volumes of data grouped into inventory related categories called “signals”, companies can match up which methods would provide the best results. He relates this experience with how his inventory management company, WithoutWire, wants to redefine the inventory management space by building inventory platforms that engage workers and drive efficiency in the supply chain, the warehouse, and beyond. He highlights how today's inventory control systems now hold the key to powering business insights that can help them make data-driven decisions for increased productivity and profitability. 

An Empirical Perspective 

Another example to take note is how the company White Truffle does the job right by providing a data service using a proprietary model which analyzes 50 categories of “signals” in a job candidate's profile. This company banks on the common belief that the more data they get, the smarter the model becomes. To better understand how this principle works, you should try to see it from a more empirical perspective; try to imagine it like you're gathering information about a person in order for you to come up with the best way of approaching that person. As you can see, the more you know how they think and act, the better you can communicate with them. Now, apply this to a corporate perspective and you’ll begin to understand that the more information you have about people, the more likely you end up hiring the best employees. 
 
And while he says he’s not really an expert in the “Big Data” trend, Travis Smith is able to perfectly summarize its purpose: “more data to analyze means better results”. Indeed, big data analysis can be a valuable tool to selecting the best performers for your company. Furthermore, it can also be used for selecting the best performing method of picking, receiving, and bin strategies. It can even be used to leverage business operations and improve them to be able to make smarter and more profitable decisions. 

Big Factors for Big Data 

Here’s some of the factors that prove that big data is truly improving the world of inventory management: 

• Big data improves operational efficiency 
  
  • Big data can be used to continually optimize the time given in picking, receiving, and bin strategies.  
• Big data helps boost sales 
  • Companies that use big data can better decide on strategies for promotion and price based on demand, which translate to more maximized sales. 
• Big data increases customer satisfaction across the board 
  • The data gathered will open opportunities to provide a better customer experience, such as in the example of identifying a poor-performing shipping provider so that there is a guarantee in customer satisfaction.  

​Seeing how Big Data helps companies thrive in today's very competitive business climate is a wonderful thing. We can say that it truly is a revolutionary tool and could possibly be one of the key factors in shaping the future of the business sector. especially for executives like Travis who are involved with inventory management. One thing’s for sure though, big data helps distributors and manufacturers come up with the best methods in accomplishing their tasks and have a clearer grasp of the situation. At the end of the day, that’s all you really need. ​

Electronics manufacturers such as Entech Electronics have had to beef up their supply chain and purchasing teams to meet the challenges of a global shortage of electronic components. Yaser Darban explains what companies can do to weather the shortage.

As the world recovers from a pandemic, another threat looms for manufacturers – a global shortage of components. So when companies like Apple say that a chip shortage can cause a loss of $3 billion to $4 billion, it makes us wonder how even industry leaders didn’t foresee it.

The global chip shortage didn’t come without its warnings, which many industry leaders failed to heed.

Preparing for global component shortages. Photo: Entech ElectronicsProjections say that the global chip shortage will last at least another 12 to 24 months, but there are ways in which businesses can mitigate the problem to manage the crisis better.

At WithoutWire, we've seen dramatic changes in our history. Paper transitioned to computers, then to smartphones, and beyond. With artificial intelligence, things are evolving quickly; it requires us to start thinking differently about how we connect as a business. Whether you're privy to it or not, decoupled systems are here to stay. IT projects and software implementations, and integration have left many people with headaches, and we're here to help you avoid them. As a company, WithoutWire has had the privilege of being on the frontline of changes in technology. Payroll, HR, marketing, sales, customer service—all good examples of platforms that now live outside of the financial operations engine known as an ERP, and despite the critical nature of the software applications, they are now integrated into an ERP—not buried inside of it. These functional operations are now independent and creating new and innovative competitive advantages for their adopters.