Supply chain volatility, macroeconomic, and regulatory pressures are creating challenges for most organizations. Who would have imagined a global pandemic followed by rampant inflation and raw material shortages. Or 3M exiting a $2 billion business with a 16% operating margin?
This kind of uncertainty can only be managed and mitigated with an end-to-end view of manufacturing operations and supply chain. That is why many organizations are investing in digital control towers and digitizing their supply chains. Such investments have a strategic value and can help organizations become truly data-driven, all while improving transparency, resiliency, sustainability, and actionability.
That said, creating a digital supply chain comes with three major challenges. In what follows, we’ll run through these one by one.
Tech Can Obscure More Than It Reveals
Even when data is digitized, it often resides in a variety of systems — not all of them connected — and can be difficult to understand. Supply chains are complex, and no two organizations have the same supply chain challenges, just as no two enterprises have the same enterprise resource planning (ERP) implementations.
Data for the four major areas of the supply chain (plan, source, make, deliver) is typically managed and generated in completely different software systems, some more decentralized than others. ERP is the primary author, but that data often lacks the granularity and real-time visibility needed to react, respond, and predict effectively.
Within organizations — not to mention across them — a lot of data is accumulated and stored. More and more data increases the difficulty of turning that data into information, and using that information to make decisions that support the business. Beyond simply knowing what data one has, there is also the problem of assessing the quality of that data, and therefore its relative value, all of which can be hidden in petabytes of blob storage. Supply chain transparency means nothing if it can’t produce meaningful action or translate into value creation.
Humans Can Be Unpredictable
At any organization, the move toward digitization requires people — champions who understand the benefits of data-driven transformation and are prepared to advocate strongly for it. The problem, of course, is that the right people, in that sense, aren’t always to be found, or are otherwise not in the right position to push for change.
And even when a champion exists, the environment she finds herself in might not be supportive of her efforts. Culture is the most oft-cited reason for digital transformation failures. In general, people tend to embrace only those changes they can easily bring about. New systems, processes, and ways of working are not easily adapted on an enterprise scale, which is one reason large companies often have to delegate the tasks of transformation and restructuring to external consulting firms. Think of this as the problem of inertia: companies tend to continue moving in the same direction or manner unless a sufficiently compelling force to do otherwise is introduced.
Resource limitations are another consideration. Data scientists and engineers are a limited resource, and the IT budgets in industrial companies aren’t designed to support hundreds of data scientists. This means that most industrial enterprises have to maximize the capabilities and capacity of their existing workforce. When it comes to moments of transformation, the major risk lies in overstretching that workforce relative to what it can realistically achieve.
Lastly, a lack of skills and capabilities are another issue large manufacturers have to contend with. You may have plenty of helping hands around, but they won’t be very useful if they aren’t taught the right techniques. This means upskilling the people you have and finding new ways to empower existing domain experts to solve their own problems.
Time Is Not Always On Your Side
The pressure to digitize and to become data-driven is so widespread that it hardly needs to be explained. Everyone wants to achieve it, and everyone is worried that their competitors will get there first or more efficiently. A little fire under one’s feet can be a good thing, but one resource it doesn’t afford is time: a firm intent on modernizing its operations doesn’t have the luxury of spending months or years contemplating the best way forward.
On the contrary, the enterprises that will be best off are those that can develop agility and adapt to changing external conditions on the flow. The flow of information is rapid in our modern world. It moves increasingly faster each year. To mitigate supply chain risks requires both short- and long-horizon planning. Every delay in improving a procurement or logistics process imperils resiliency and incurs risk.
But none of this is to say that you should embrace risk and volatility for the sake of speed. Firms that do these often fail to consider not only the downside of the risks they take, but more importantly ignore the unintended consequences that lurk in the shadows. The airline industry experienced this when a procurement model which reduced on-hand inventory for $300 million in cost savings eventually led to production shutdowns, delays and cancellations due to raw material shortages. The impact of a lean supply chain decision wasn’t felt until 5+ years later.
Time is a valuable resource where quarterly earnings demand quick returns. Most organizations require payback within 2 years or less. Digital supply chains are rightly seen as a long term investment, but the resource commitment can make this challenging when enterprises encounter unseen challenges.
Buy, Build, and Bouy to Win
“Alright,” you may be thinking, “the challenges are clear — how can I overcome them?” There’s no cure-all solution, of course, as each organization will have to strategize according to its unique structure, workforce, and company culture. But there are a few key approaches that any firm might consider taking on its way to complete, data-driven digitization.
Firstly, companies might choose to buy. Investments in digitization can be well-placed by outsourcing the infrastructural work to a third party, who can build a central data control tower that feeds into the firm’s various divisions and their operations. Though the “buy” approach is usually expensive, it can pay off in terms of high value in the medium rather than the long run. That said, systems built by external parties are not easily adapted or maintained by the firm’s internal teams — this can lead to a myriad of problems, including mangled troubleshooting processes and the incursion of complex tech debt over time. The firm will always need the third party to scope, deliver, maintain, and fix projects for the best results.
Secondly, firms might decide to build their own digital systems, relying on internal teams to support their development. The pros and cons of this approach are practically the inverse of the first approach: the time to value is much longer, as systems take a while to build from scratch; the expense is typically much lower, as the firm can tap into its own manpower; and the infrastructure is easy to adapt and support internally, and can be made to extend existing supply chain management capabilities organically. All of this, of course, requires a significant resource investment over the long term, which means long term commitment from management and enthusiasm within the company culture more generally.
Lastly, firms may choose to buoy and build their own systems atop a pre-built platform, like Dataiku, enabling them to incrementally create value over time. In many ways this is the steadiest, least erratic, and least risky approach, enabling companies to generate moderate-to-high value on an ongoing basis. The expense is minimal — the external costs consist mainly of buying software licenses, and the internal ones are lower than the “build” approach because the foundations have already been laid before you get building. And because the third-party software is a foundation, buoying the internal team’s projects and infrastructure, the system will remain transparent to those inside the organization who have developed it, meaning it is easily adapted and supported internally.
The “buoy” approach, to be sure, requires a carefully considered, strategic approach when it comes to proving the concept. Firms opting for this route need to choose and develop the right use cases to drive value right out of the gate and demonstrate the soundness and benefits of the approach.