Many people consider return on investment (ROI) to be a significant factor when deciding to invest in information technology (IT). When asked how they will receive a return on that investment, they generally focus on some kind of cost reduction. They rarely consider the strategic advantages they might gain. Such advantages can drive revenues, improve customer loyalty, expand product lines, or otherwise increase the top-line gross profit.
Everyone likes to reduce costs. However, whereas costs available for reduction are limited, increased revenues have infinitely more potential in both total return and in the number and types of investment available.
The most obvious ways to invest in IT ultimately center on data: data capture, data analytics, data-driven communication, etc. The amount of data available and the number of options you can focus on can be overwhelming. Depending upon your company’s operational maturity, you may or may not have a clear direction for finding value from a data management investment.
A general guideline for deciding how to invest in data-related IT should include the following aspects:
- Identify why you want to use the data.
- What problem are you solving?
- What opportunities are you wanting to explore?
- Start small.
- Define a pilot or proof of concept project.
- Identify challenges.
- Determine your company’s data capture timeliness, accuracy, and relevance requirements/abilities.
- Determine your available data capture/processing/analysis methods, and evaluate them for efficiency and effectiveness.
- Determine how you will evaluate the project’s effectiveness.
- Make a “go further” or “stop” decision.
Identify Why You Want to Use Data
What are you trying to accomplish by investing in IT? Generally, most manufacturing and distribution-focused companies are either operationally (internally) or customer (externally) focused. There are many instances in which you can invest in IT with a dual focus on both operations and customers, but they require a significantly higher level of operational maturity to accomplish, so consider your capacity when deciding how to approach your data strategy.
Good candidates for an internal focus include operational statistics and management. Operational statistics can be very effective when trying to find problems or opportunities within your organization. These statistics should be used to compare within your organization (e.g. trends over time) as well as within your industry (e.g. how do you compare to industry standards).
Generally, operational statistics do not give you answers. Rather, they provide important indicators on which questions you should be asking, which is just as, important to your success and growth as a business.
Example of Operational Statistic Evaluation
An example of starting small with an operational statistic could be tracking the timeliness of order fulfillment. Here’s a step-by-step guide to how you might accomplish this:
- Start by proactively tracking the promised ship date versus the actual ship date. Accumulate this for each order, then calculate the days early or late (on time = 0), then compute an average each week.
- Consider whether or not delivering early is a bad thing for your customers. If it is not a bad thing, then set both early and on time to 0 days late. If it is a bad thing, then set the number of days early to a positive number so that it has the same adverse analytical impact as the days late.
- Chart the weekly average for a month, quarter, year, etc. The closer you are to 0, the better you are performing.
While this effort does directly improve your operation, it also gives you the ability to answer two simple questions. First, is your company consistently meeting customer expectations as to timely delivery? Second, over time, are we improving our ability to deliver in a timely fashion?
Once you’ve answered those questions, you have the ability to analyze bottlenecks and inefficiencies in your operations to try to improve how well you meet customer expectations, as well as a way to measure the effectiveness of the changes you make.
Example of Internal Operations Statistic Evaluation
In order to more fully explore the second category of internal focus (whirlwind management), let’s continue with our example of on-time shipments forward.
Let’s assume that the on-time shipment statistic is at a 3, meaning that, on average, all orders are 3 days later than promised. Now that you have this data, you can take these results and begin to evaluate the fulfillment process to determine how to reduce this operational statistic.
Some sample questions to ask are:
- How do we determine the promised ship date?
- Are we merely telling the customer what they want to hear?
- Do we have the ability to predict order fulfillment (or production) timelines?
- Is that prediction readily available to the sales and customer service reps so that they can communicate the information to the customer?
The answer to these questions will allow you to focus on different aspects of your business. Good data means reliable predictability, which further translates to confidence when training employees and/or refining your business procedures. Such confidence in data enables the customer service/sales teams to use this information to better communicate fulfillment expectations to the customer.
If examining the data reveals a lack the ability to predict order fulfillment or production timing, then you have a much larger number of questions to ask yourself, such as:
- Do we have a system that is capable of holding the required data?
- If yes, then
- Is our data capture process resulting in a lack of integrity?
- If yes, then do we need to invest in training and/or data capture tools (such as barcode scanners, etc.)?
- Are our system’s analysis capabilities lacking?
- If yes, then see 1b directly below.
- Is our data capture process resulting in a lack of integrity?
- If no, we should probably perform a full requirements analysis and determine what it takes to either supplement or replace our current system. (In other words, it’s time for a serious system upgrade.)
- If yes, then
The answer to these questions will probably provide many possible areas for operational investment in order to improve both efficiency and effectiveness.
Identify How You Want to Use Data
Data is something that every company wants to use to help them improve their processes, but before your business can make any decisions with data, you have to understand what that data means. It’s tempting to jump in headfirst and absorb every bit of data you can unearth, but it’s important to first develop some data analytic techniques on a smaller scale. The first rule of any data analysis is “garbage in = garbage out”, and starting small will limit how large the garbage pile grows if you need to refine your data analysis.
When you start out small, you accomplish two things:
- You teach your team to properly capture data (it’s not as simple as it sounds).
- You learn what the data actually means.
Once you have captured some good data and understand the data analysis process, you can begin to incorporate this information into your business decision-making process. By starting small, you gain insight into both the challenges of running a data-driven operation and the capabilities of your team before you find yourself drowning in numbers that mean nothing to you.
After you’ve gained a better understanding of a data-driven operation by starting small, you may be ready to look at the bigger picture. When executing a strategy that has a wide and sweeping impact on your operation, planning is vital to success.
An effective method of creating a planning framework usually begins with a functional organizational chart analysis. While the concept itself is simple, the effort to fully embrace and execute it is not. Creating a functional organization chart clearly identifies your operational management structure in terms of management roles. Most small to medium-sized businesses assign key personnel multiple roles and/or assign multiple people to a single role. Because of this, the focus of the chart is role definition, with role assignment as a secondary consideration. For each role, define the job requirements in terms of what they must do. For each requirement, define how you will objectively measure the success of the person (or people) fulfilling the requirement.
Once you have completed this analysis, you will find where your current operation is lacking in terms of measurement capabilities. Using this information, create a columnar data requirement analysis that lists all desired requirements, a priority or importance rating (e.g. must have, nice to have, wish list), the current system’s capabilities in use, the current system’s additional capabilities not fully in use, and a final column for defining how to close the gap between requirements and capabilities.
This process allows you to fully understand your company’s strengths and weaknesses, as well as assess your ability to truly execute your short and long-term strategic plan. From there, you can determine where best to invest in order to enhance your strengths and eliminate weaknesses. You will gain insight on how to enable objective measurement and promote continuous improvement of all key roles.
If you cannot objectively measure something, then you cannot reliably improve upon it. Furthermore, all functions/roles that are not objectively measured are likely to be poorly managed. But with the correct gathering and application of good, reliable data, these vital measurements can be taken, and improvements are bound to follow.